JP2006187510A - State analysis device and software program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a state analysis device and a software program, accurately analyzing the state of an object. <P>SOLUTION: This state analysis device includes: a threshold computing means 28 for computing a threshold for determining the state of an object on the basis of the measurement data showing the state of an object having periodic motion; and a state discrimination means 26 for comparing a first measurement data or a second measurement data about an object 2 with the threshold, wherein the threshold computing means 28 computes a first threshold and a second threshold, and the state discrimination means 26 determines that the object 2 is in a first state in the case where the magnitude of periodic motion at a determination object time of the first measurement data is continuously below the first threshold and the magnitude of periodic motion at a determination object time of the first measurement data is not below a second threshold, or in the case where the periodic motion at a determination object time of the first measurement data is not below the first threshold and the magnitude of periodic motion at a determination object time of the second measurement data is continuously below the second threshold. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a state analysis device and a software program, and more particularly to a state analysis device and a software program that can accurately analyze the state of an object.

2. Description of the Related Art Conventionally, a motion detection sensor has been proposed as a motion detection device that detects a motion of an object in a space, for example, a bathroom or a toilet, for example, a person. As a typical example, there is a monitoring device that monitors a sleeper's breathing by calculating a pattern movement amount from an image obtained by continuously projecting the projected pattern onto a sleeper on the bed. there were. (For example, refer to Patent Document 1).
JP 2002-175582 A (page 5-9, FIG. 1-13)

  As a conventional apparatus as described above, for example, data indicating a person's state measured from an object having a periodic motion, for example, an area corresponding to substantially the whole person, and a predetermined reference value are compared. However, there has been a state analysis device that discriminates one or both of hypopnea and apnea of a person at the time of interest based on the comparison result.

  However, in the conventional apparatus as described above, for example, in order to accurately evaluate complex respiratory motion as a whole, more accurate state analysis such as detailed state analysis and improvement of analysis accuracy is desired. It was rare.

  Accordingly, an object of the present invention is to provide a state analysis apparatus and a software program that can accurately analyze the state of an object.

  In order to achieve the above object, the state analysis apparatus according to the first aspect of the present invention provides, for example, as shown in FIG. 1, before and after the determination target time point in the measurement data indicating the state of the object 2 with periodic movement. Threshold calculation means 28 for calculating a threshold for determining the state of the object 2 based on the measurement data of the first predetermined period; and a first measurement corresponding to the first region 2a of the object 2 A state determination unit 26 that compares the threshold value with the second measurement data corresponding to the second region 2b different from the first region 2a of the data or the object 2 and the state; Provided; the threshold value calculation means 28 calculates a first threshold value corresponding to the first area 2a and a second threshold value corresponding to the second area 2b, and the state determination means 26 determines the first measurement data. The magnitude of the periodic movement at the target time point exceeds the first threshold value for the second predetermined period. If the magnitude of the periodic movement at the determination target time point of the second measurement data does not fall below the second threshold, or the periodic movement at the determination target time point of the first measurement data If the magnitude does not fall below the first threshold and the magnitude of the periodic movement at the determination target time point of the second measurement data falls below the second threshold continuously for the second predetermined period, the target It is configured to determine that the object 2 is in the first state.

  If comprised in this way, a threshold value calculation means will calculate the 1st threshold value according to a 1st area | region, and the 2nd threshold value according to a 2nd area | region. The state discriminating unit is configured such that the magnitude of the periodic movement at the determination target time point of the first measurement data is continuously lower than the first threshold value for the second predetermined period, and the period at the determination target time point of the second measurement data. The second measurement data if the magnitude of the general movement does not fall below the second threshold, or the magnitude of the periodic movement at the determination target time point of the first measurement data does not fall below the first threshold. The state of the object is determined because the object is determined to be in the first state when the magnitude of the periodic movement at the determination target time falls below the second threshold continuously for the second predetermined period. It is possible to provide a state analysis device that can accurately analyze.

  For example, when the object is a breathing person, the first region is the chest region 2a and the second region is the abdominal region 2b. In this case, the first state is, for example, a so-called obstructive apnea state. The same applies hereinafter unless otherwise specified.

  In order to achieve the above object, a state analysis apparatus according to a second aspect of the present invention is based on measurement data indicating a state of a target object 2 having a periodic motion, as shown in FIG. The first measurement data corresponding to the first region 2a or the second measurement data corresponding to the second region 2b different from the first region 2a of the object and the whole corresponding to substantially the entire object 2 A state determination unit that compares the measurement data with the state of the object to determine the state of the object; the state determination unit includes a periodical movement magnitude at the determination target time point of the first measurement data; If the magnitude of the periodic movement at the determination target time point of the measurement data is substantially equal to or exceeds the magnitude of the periodic movement at the determination target time point of the entire measurement data for the third predetermined period, Configured to determine that the object is in the first state. That.

  If comprised in this way, the state discrimination | determination means has the magnitude | size of the periodic motion at the determination object time point of 1st measurement data, or the magnitude of the periodic movement at the determination object time point of 2nd measurement data as a whole. Since it is determined that the object is in the first state when the magnitude of the periodic movement at the determination object time point of the measurement data is substantially equal to or exceeds the continuous value for the third predetermined period, It is possible to provide a state analysis apparatus that can accurately analyze the state.

  In order to achieve the above object, a state analysis apparatus according to a third aspect of the present invention provides, for example, as shown in FIG. 1, before and after a determination target time point in measurement data indicating a state of an object 2 having a periodic movement. Threshold calculation means 28 for calculating a threshold for determining the state of the object 2 based on the measurement data of the first predetermined period; and a first measurement corresponding to the first region 2a of the object 2 The state determination means 26 which compares the threshold value with the 2nd measurement data corresponding to the 2nd area | region 2b different from the 1st area | region 2a of data or a target object is provided. The threshold value calculating means 28 calculates a first threshold value corresponding to the first region 2a, a second threshold value corresponding to the second region 2b, and a third threshold value corresponding to substantially the entire object 2; The state determination unit 26 determines the entire measurement data corresponding to substantially the entire object 2. The magnitude of the periodic movement at the time of the elephant is continuously below the third threshold for the fourth predetermined period, and the magnitude of the periodic movement at the determination target time of the first measurement data is the first threshold. Or when the magnitude of the periodic movement at the determination target time point of the second measurement data exceeds the second threshold value continuously for the fifth predetermined period, the object is in the first state. Is configured to discriminate.

  If comprised in this way, a threshold value calculation means will calculate the 1st threshold value according to a 1st area | region, the 2nd threshold value according to a 2nd area | region, and the 3rd threshold value according to the substantially whole object 2. To do. The state discriminating means detects the first measurement when the magnitude of the periodic movement at the judgment target time point of the whole measurement data corresponding to substantially the whole object is continuously below the third threshold for the fourth predetermined period. The magnitude of the periodic movement at the determination target time point of the data is the first threshold value, or the magnitude of the periodic movement at the determination target time point of the second measurement data is the second threshold value, the fifth predetermined period. Since the object is determined to be in the first state when continuously up, it is possible to provide a state analysis device that can accurately analyze the state of the object.

  Further, as described in claim 4, in the state analyzing apparatus according to claim 1 or 3, as shown in FIG. 1, for example, the state determining unit 26 is configured to determine the period of the determination target time point of the first measurement data. When the magnitude of the general movement falls below the first threshold and the magnitude of the periodic movement at the determination target time point of the second measurement data falls continuously below the second threshold for the sixth predetermined period. Moreover, you may comprise so that the target object 2 may discriminate | determine that it is a 2nd state.

  When configured in this way, the state determination means is configured such that the magnitude of the periodic movement at the determination target time point of the first measurement data is the first threshold and the periodic movement at the determination target time point of the second measurement data. When the size of the object falls below the second threshold continuously for a sixth predetermined period, it is determined that the object is in the second state, so the state of the object is in the second state Can be analyzed accurately. Furthermore, it is possible to accurately distinguish and discriminate between the first state and the second state different from the first state.

  For example, when the object is a breathing person, the second state is, for example, a so-called central apnea state. The same applies hereinafter unless otherwise specified.

  Further, as described in claim 5, in the state analysis apparatus described in claim 2, for example, as shown in FIG. 1, based on the measurement data of the first predetermined period before and after the determination target time, among the measurement data. And a threshold value calculating means 28 for calculating a threshold value for determining the state of the object 2; the threshold value calculating means 28 corresponds to the first threshold value corresponding to the first area 2a and the second area 2b. The second threshold value is calculated, and the state determination unit 26 determines that the magnitude of the periodic movement at the determination target time point of the first measurement data is the first threshold value, and the period of the determination target time point of the second measurement data. It may be configured to determine that the target object 2 is in the second state when the magnitude of the actual movement continuously falls below the second threshold for the sixth predetermined period.

  If comprised in this way, a threshold value calculation means will calculate the 1st threshold value according to a 1st area | region, and the 2nd threshold value according to a 2nd area | region. The state determination means is configured such that the magnitude of the periodic motion at the determination target time point of the first measurement data is the first threshold value, and the periodic movement magnitude at the determination target time point of the second measurement data is the second value. When the threshold value of the object is continuously lowered for the sixth predetermined period, it is determined that the object is in the second state. Therefore, it is accurately analyzed that the state of the object is the second state. Can do. Furthermore, it is possible to accurately distinguish and discriminate between the first state and the second state different from the first state.

  Further, as described in claim 6, in the state analysis device according to claim 4 or claim 5, as shown in FIG. 1, for example, the state determination means 26 has a first state and a second state. When the object 2 is in the third state when the period in which the object 2 is in the first state and the period in which the object 2 is in the second state are substantially continuous for a seventh predetermined period. You may comprise so that it may discriminate | determine.

  If comprised in this way, a state discrimination | determination means will be the 1st state and a 2nd state substantially continuing, and the period when a target object is the said 1st state and the period which is the said 2nd state are the 1st. 7 is substantially continuous for a predetermined period, it is determined that the object 2 is in the third state, so the third state distinguished from the first state and the second state can be accurately determined. .

  For example, when the object is a breathing person, the third state is, for example, a so-called mixed apnea state. The same applies hereinafter unless otherwise specified.

  In order to achieve the above object, a state analysis apparatus according to a seventh aspect of the present invention provides, for example, as shown in FIG. 1, before and after a determination target time point in measurement data indicating a state of an object 2 having a periodic movement. Threshold calculation means 28 for calculating a threshold for determining the state of the object 2 based on the measurement data of the first predetermined period; and a first measurement corresponding to the first region 2a of the object 2 A state determination unit 26 that compares the threshold value with the second measurement data corresponding to the second region 2b different from the first region 2a of the data or the object 2 and the state; Provided; the threshold value calculation means 28 calculates a first threshold value corresponding to the first area 2a and a second threshold value corresponding to the second area 2b, and the state determination means 26 determines the first measurement data. The magnitude of the periodic movement at the target time point is the first threshold value and the second measurement value. It is configured to determine that the target object 2 is in the second state when the magnitude of the periodic movement at the data determination target time point continuously falls below the second threshold value for a sixth predetermined period. Is done.

  If comprised in this way, a threshold value calculation means will calculate the 1st threshold value according to a 1st area | region, and the 2nd threshold value according to a 2nd area | region. The state determination means is configured such that the magnitude of the periodic motion at the determination target time point of the first measurement data is the first threshold value, and the periodic movement magnitude at the determination target time point of the second measurement data is the second value. When the threshold value of the object 2 is continuously lowered for the sixth predetermined period, it is determined that the object 2 is in the second state. Therefore, it is possible to provide a state analysis apparatus that can accurately analyze the state of the object. it can.

  Further, as described in claim 8, in the state analysis apparatus according to claim 1 or any one of claims 3 to 7, for example, as shown in FIG. The fourth threshold value corresponding to substantially the whole of the object calculated by the threshold value calculation means 28 is set as the eighth predetermined value. It may be configured to determine that the object is in the fourth state when it has not fallen continuously for a period of time.

  With this configuration, the state determination unit 26 is a target object whose magnitude of periodic movement at the determination target time point of the entire measurement data corresponding to substantially the entire target object 2 is calculated by the threshold value calculation unit 28. When the fourth threshold corresponding to the whole is not continuously lowered for the eighth predetermined period, it is determined that the object is in the fourth state. For example, it is obviously strange due to some factor. The first state, the second state, and the third state can be accurately determined.

  For example, when the object is a breathing person, the fourth state is, for example, a normal breathing state. Further, the fourth threshold value is typically a value larger than the third threshold value. The same applies hereinafter unless otherwise specified.

  Further, as described in claim 9, in the state analyzing apparatus according to claim 2, for example, as shown in FIG. 1, based on the measurement data of the first predetermined period before and after the determination target time point among the measurement data. And a threshold value calculating means 28 for calculating a threshold value for determining the state of the object 2; the state determining means 26 is a periodic movement at the determination target time point of the entire measurement data corresponding to substantially the entire object 2. The object 2 is in the fourth state when the fourth threshold value corresponding to substantially the whole object calculated by the threshold value calculation means 28 is not continuously reduced for the eighth predetermined period. You may comprise so that it may be determined.

  If comprised in this way, a threshold value calculation means will calculate the 4th threshold value according to the substantially whole object. The state discriminating means has a fourth threshold value corresponding to substantially the whole of the object calculated by the threshold value calculating means, wherein the magnitude of the periodic movement at the determination target time point of the entire measurement data corresponding to substantially the whole object. If the object is not continuously lowered for the eighth predetermined period, it is determined that the object is in the fourth state. For example, it is possible to eliminate a determination that is apparently strange due to some factor. And the first state can be accurately determined.

  Further, as described in claim 10, in the state analysis apparatus according to any one of claims 1 to 9, measurement data is acquired as shown in FIG. 10 (see also FIG. 9 as appropriate). A measuring apparatus 10; the measuring apparatus 10 projects a plurality of bright spots 11b on the target area 3, an imaging apparatus 12 that images the target area 3 on which the plurality of bright spots 11b are projected, and imaging means 12 generates a movement amount calculation unit 141 that calculates a movement amount between two frames of a plurality of bright spots 11b from two frame images acquired at different time points, and movement amount waveform data in which the movement amounts are arranged in time series. The movement amount waveform generation unit 142 may be included.

  If comprised in this way, since a measuring apparatus is provided and measurement data are acquired from the measuring apparatus, the state of a target object can be measured correctly. Further, since the measuring device is configured to include the projection device, the imaging device, the movement amount calculating means, and the movement amount waveform generating means, it is possible to accurately measure the state of the object without contact, for example.

  In order to achieve the above object, a software program according to an eleventh aspect of the present invention is a software program that is installed in a computer and operates as a state analysis device, for example, as shown in FIG. Processing for calculating a threshold value for determining the state of the object based on the measurement data of the first predetermined period before and after the determination target time among the measurement data indicating the state of the object having a periodic movement; (S5); first measurement data corresponding to the first region 2a of the object or second measurement data corresponding to the second region 2b different from the first region 2a of the object, and a threshold value. In comparison, the computer is controlled to execute the process of determining the state of the object (S7); the process of calculating (S5) includes a first threshold value and a second threshold value corresponding to the first region 2a. The process of determining (S7) includes a process of calculating a second threshold value corresponding to the region 2b. In the determining process (S7), the magnitude of the periodic motion at the determination target time point of the first measurement data is set to the second threshold value When continuously falling below a predetermined period and the magnitude of the periodic movement at the determination target time point of the second measurement data does not fall below the second threshold, or the periodic measurement at the determination target time point of the first measurement data When the magnitude of movement does not fall below the first threshold and the magnitude of periodic movement at the determination target time point of the second measurement data falls continuously below the second threshold for a second predetermined period. And a process for determining that the object is in the first state.

  If comprised in this way, the 1st threshold value according to the 1st area | region of the target object with a periodic motion and the 2nd threshold value according to a 2nd area | region will be calculated. Further, the magnitude of the periodic movement at the determination target time point of the first measurement data corresponding to the first area is continuously lower than the first threshold for the second predetermined period, and corresponds to the second area. When the magnitude of the periodic movement at the determination target time point of the second measurement data does not fall below the second threshold, or the magnitude of the periodic movement at the determination target time point of the first measurement data is the first If the magnitude of the periodic movement at the determination target time point of the second measurement data falls below the second threshold value continuously for a second predetermined period without lowering the threshold value, the object is in the first state. Therefore, it is possible to provide a software program that can accurately analyze the state of the object.

  As described above, according to the present invention, the state of the object is determined based on the measurement data of the first predetermined period before and after the determination target time, among the measurement data indicating the state of the object with periodic movement. Threshold calculation means for calculating a threshold for determination; first measurement data corresponding to the first area of the object or second measurement corresponding to a second area different from the first area of the object A state discriminating unit that compares the data with a threshold value to discriminate the state of the object; the threshold value calculating unit includes a first threshold value corresponding to the first region and a second value corresponding to the second region. And the state determination means is configured such that the magnitude of the periodic movement at the determination target time point of the first measurement data is continuously lower than the first threshold for a second predetermined period, and the second measurement data If the magnitude of the periodic movement at the determination target time does not fall below the second threshold, or The magnitude of the periodic movement at the determination target time of the second measurement data does not fall below the first threshold, and the magnitude of the periodic movement at the determination target time of the second measurement data becomes the second threshold. Since the object is determined to be in the first state when continuously falling for a predetermined period of time, it is possible to provide a state analysis apparatus that can accurately analyze the state of the object.

  Embodiments of the present invention will be described below with reference to the drawings. In addition, in each figure, the same code | symbol is attached | subjected to the mutually same or equivalent member, and the overlapping description is abbreviate | omitted.

  FIG. 1 is a block diagram showing a configuration example of a respiration monitor 1 as a state analysis apparatus according to the first embodiment of the present invention. The respiration monitor 1 is configured to include an arithmetic device 20 and an FG sensor 10 as a measuring device. The arithmetic unit 20 is, for example, a computer such as a personal computer or a microcomputer. The computing device 20 also has an input device 35 for inputting information for operating the respiration monitor 1 and a display 40 for displaying the processing state of the respiration monitor 1. The input device 35 is, for example, a touch panel, a keyboard, or a mouse. The display 40 is typically an LCD (Liquid Crystal Display, liquid crystal display device). The processing state of the respiratory monitor 1 displayed on the display 40 is, for example, measurement data acquired by an FG sensor 10 described later, a determination result of the state of the object 2 by a state determination unit 26 described later, and the like. Although the input device 35 and the display 40 are illustrated as being externally attached to the main body of the arithmetic device 20 in this figure, they may be built in. The FG sensor 10 will be described in detail with reference to FIG.

  The arithmetic unit 20 determines the state of the target object 2 based on the measurement data of the first predetermined period before and after the determination target time among the measurement data indicating the state of the target object 2 having a periodic movement. The threshold value calculation unit 28 serving as a threshold value calculation means for calculating the threshold value of the first object is different from the first measurement data corresponding to the first region 2a of the target object 2 or the first region 2a of the target object 2. A state determination unit 26 as a state determination unit that determines the state of the object 2 by comparing the second measurement data corresponding to the second region 2b with a threshold value.

  The arithmetic unit 20 also includes a representative coordinate calculation unit 24 that calculates the representative coordinates of the position coordinate group of the measurement points at which the phases of the plurality of movements are substantially the same for the measurement points where the movement of the object 2 is measured, When there are two or more position coordinate groups having different phases, a dividing line forming unit 25 for forming an area dividing line for dividing the target area between two or more representative coordinates is provided. The parting line forming unit 25 typically includes a chest region 2a as a first region and an abdominal region 2b as a second region. An area dividing line 2c is formed so as to be divided into two. The chest region 2a (first region) and the abdominal region 2b (second region) will be described later.

  Furthermore, the arithmetic unit 20 determines and detects the non-periodic movement that occurs together with the periodic movement based on the measurement data indicating the state of the object 2 having the periodic movement. And a threshold value based on the measurement data of the first predetermined period excluding the period in which the non-periodic motion is detected by the body motion detection unit 27 among the periods before and after the determination target time point. calculate. The main predetermined period of this embodiment includes the first predetermined period to the tenth predetermined period. These predetermined periods will be sequentially described later (however, the order is not necessarily the number order).

  Here, although the target object is described as being the person 2 in the present embodiment, it may be a domestic animal such as a cow or a pig. Livestock includes pets such as cats and dogs. Hereinafter, the object is described as a person 2. Here, the case where the person 2 is sleeping on the bed 3 will be described. Here, the periodic motion is, for example, the respiratory motion of the person 2, and the non-periodic motion is, for example, a body motion other than the respiratory motion of the person 2 (hereinafter referred to as “body motion unless otherwise specified”). "). It should be noted that the respiratory motion of the person 2 is actually unstable in later-described waveform data indicating the respiratory motion, such as its period and amplitude. In extreme cases, the amplitude may be lost, but there is some instability. If so, it can be ignored and analyzed. The body movement of the person 2 is a movement of the person 2 that is less periodic than breathing, such as turning over, and is larger than the breathing. Further, the movement of the person 2 larger than the respiration is characterized by respiration, for example, by the magnitude of the movement or the magnitude of the change in movement.

  The respiratory monitor 1 according to the present embodiment is a respiratory motion detection device that analyzes the respiratory state of a person during sleep and is used for diagnosis of sleep apnea syndrome (SAS).

  In the present embodiment, the first state is a so-called suffocation state in which the passage of air in the back of the throat (upper airway) is blocked during sleep, and so-called respiratory effort is observed even during an apnea attack. Obstructive apnea. Furthermore, the second state described later is a so-called central apnea in which breathing stops when the respiratory command does not reach the respiratory muscles during sleep, and thoracoabdominal movement (that is, respiratory movement itself) stops. It is a state (Central apnea). A third state to be described later is a subtype of obstructive apnea, which is a mixed apnea state in which obstructive apnea and central apnea are combined. Furthermore, the fourth state described below is an apnea state in which the airflow due to the above-described obstructive apnea, central apnea, mixed apnea, etc. is almost stopped, and the airflow due to respiration is clear. In other words, it is a state of normal breathing which is not a hypopnea state (hypopnea), that is, a state that is lowered to a low level, in other words, a state of normal breathing which has no problem in the diagnosis of sleep apnea syndrome (SAS).

  In the present embodiment, the measurement data is data measured by the FG sensor 10. The measurement data indicating the state of the person 2 is the amount related to the movement of the person 2 within a certain time within the measurement range (for example, the range including the chest and abdomen of the person 2), more specifically, the vertical movement of the body surface. It is a quantity related to speed. The measurement data indicating the state of the person 2 is data based on measurement results obtained by measuring the movement of the person 2 at a plurality of points. Respiration data based on the sum of the measurement values at the plurality of points, It includes both and / or one of body motion data which is data of the sum of absolute values of each measured value at a point. The sum of absolute values is a concept including the sum of the squares of the measured values. Here, the measurement data includes both respiration data and body movement data. Further, the respiration data and the body movement data may be data obtained by further dividing the number of measurement points or the number of measurement points in which the measurement value has changed more than a certain value.

  FIG. 2 is a schematic diagram showing an example of a waveform pattern formed by respiration data and body motion data used in the respiration monitor according to the embodiment of the present invention. For example, the measurement data forms a waveform pattern as shown in FIG. In addition, (a) is an example which simplified the respiration data at the time of measuring the normal respiration of the person 2. FIG. Further, (b) is a typical example of respiratory data including data indicating body movement, and (c) is a typical example of body movement data including data indicating body movement. As shown in (b) and (c), when there is a body motion other than breathing (body motion), the body motion data has a large value distribution, and at the same time, the fluctuation width of the breathing data becomes large and the periodicity is increased. Lose. Here, the sampling interval of measurement data (acquisition interval of one data) is, for example, 0.1 to 0.25 seconds. Further, here, each measurement value obtained by measuring the movement of the person 2 at a plurality of points is a value related to the movement of the person 2 within a predetermined time, in other words, the movement speed of the person 2. Therefore, by adding the measurement values at each measurement point, it is possible to obtain data relating to the overall average position change in a certain direction within a certain period (breathing data). Further, if the absolute values of the respective measured values are added together, data relating to the total amount of the entire movement can be obtained (body movement data). Here, the case where both respiration data and body motion data are used will be described. However, for example, all processes described below can be performed using only respiration data.

  Note that the analysis of the measurement data acquired from the FG sensor 10 by the present apparatus may be performed after acquiring the measurement data for a certain period, or the acquisition of the data with the state transition of the person 2 It may be done in real time. In this embodiment, a case where measurement data for a certain period, for example, overnight (about 7 to 10 hours) is collectively processed (analyzed) will be described.

Here, the determination target time point is a time point at which the state of the person 2 is determined. The determination target time points are set at approximately equal intervals with respect to the acquired measurement data. The interval between the determination target points may be appropriately determined according to, for example, the sampling interval of the measurement data. That is, here, the interval of the determination target time points (1 data acquisition interval) is, for example, about 0.1 to 0.25 seconds.
Hereinafter, each of the above-described configurations will be described in detail with reference to FIG. 1 again.

  As described above, the body motion detection unit 27 is based on the measurement data indicating the state of the object having a periodic motion, that is, the state of the person 2 having the respiratory motion. And detecting a body movement period which is a period in which data indicating the body movement of the object exists from the measurement data.

  In the present embodiment, the body motion detection unit 27 is configured to calculate the body motion reference value based on the distribution state of measurement data (for example, body motion data) within a ninth predetermined period immediately before the determination target time point. . The ninth predetermined period is a period for calculating the body movement reference value, and may be, for example, about 1 to 2 minutes immediately before the determination target time point.

  Here, the distribution state of the body motion data is calculated by, for example, the average value of the body motion data (measured values) in the ninth predetermined period + standard deviation × a, and this is used as the body motion reference value. Here, a is a constant. If this a is 3, for example, and the data has a Gaussian distribution, 99.85% of the body movement data (measured value) falls within the range of values calculated by this equation. It can be determined that the body motion is not a normal distribution. That is, when the body motion data (measured value) at the determination target time exceeds the body motion reference value calculated as described above, the body motion detection unit 27 determines that the motion at the determination target time is a body motion. judge.

  When it is determined that the movement of the person 2 at the determination target time point is a body movement, the body movement detection unit 27 further determines a body movement period that is a period in which data indicating the body movement of the person 2 exists from the measurement data. Configured to detect. The body movement period may be a period in which it is determined that there is actually body movement, but it can be predicted in advance that measurement data (for example, respiratory data) becomes somewhat unstable for several seconds before and after the period in which body movement is detected. Therefore, it is preferable that a period obtained by adding several seconds before and after the period to a period in which it is determined that there is actually body movement is a body movement period. In addition, as described above, the body motion detection unit 27 detects whether or not the motion at the determination target time point is a body motion, in other words, determines whether or not the determination target time point is a body motion period. It is also a thing.

  In addition, if this work is performed sequentially from the past (front) data in time, it is known at the next determination target time whether the past determination target time is a body movement, That is, it is preferable to exclude the period indicating body movement from the ninth predetermined period, as in the first predetermined period described below. That is, it is preferable that measurement data indicating body movement is not included in the subsequent calculation of the body movement reference value. By doing so, body movement can be detected more accurately. The body motion detection unit 27 determines that the body motion is detected when the body motion data (measured value) continuously exceeds the body motion reference value calculated as described above, for example, for about 1 to 5 seconds. You may comprise.

  In addition, the body motion detection unit 27 calculates the body motion reference value based on the measurement data in the ninth predetermined period before the determination target time point and the measurement data in the subsequent ninth predetermined period. Calculated for each of the ninth predetermined period and the subsequent ninth predetermined period, the determination of the body movement of the person 2 is evaluated by evaluating the effectiveness of each calculated body movement reference value, and either one is adopted. Furthermore, the determination may be performed by comparing the data at the determination target time with the adopted body movement reference value. The evaluation of effectiveness may be performed, for example, by evaluating an effective amount of information. The effective information amount can be evaluated by, for example, calculating and comparing information entropy of body motion data in the previous ninth predetermined period and the subsequent ninth predetermined period. Information entropy will be described later.

  The effectiveness may be evaluated using a histogram as shown in FIG. 3 described later. When using such a histogram, for example, from each of the body movement data histograms of the previous ninth predetermined period and the subsequent ninth predetermined period, an output distribution block is obtained, and the lower output is obtained. You may evaluate by making the one with much frequency contained in the lump (left lump in the figure) distributed. In this way, the sensitivity changes due to the posture change of the person 2 due to a large body movement or the like, and it is possible to respond immediately when the level to be used as a reference (for example, the body movement reference value) changes. Here, the range of this lump is assumed to be a Gaussian distribution, and the frequency of the frequency up to a position where the frequency becomes a fixed ratio, for example, 60% (ie, e−1 / 2) with respect to the peak (mode) of the histogram. A certain multiple (for example, 3 times) of the distance from the value is added to the mode value. If a Gaussian distribution is assumed, the right side of the histogram peak in the figure (the side where the body motion data is large) is affected by body motion, so the left side (the side where the body motion data is small) corresponds to the peak. It is recommended to evaluate the frequency ratio. Or, it is determined as simple as 4 times the mode value.

  The body motion detection unit 27 evaluates the effectiveness of the measurement data in the ninth predetermined period before the determination target time point and the measurement data in the subsequent ninth predetermined period, and the ninth predetermined period. Any one of the measurement data may be adopted, and the body movement reference value may be calculated based on the adopted measurement data. In this case, the body movement of the person 2 is determined by comparing the data of the ninth predetermined period with the calculated body movement reference value.

  Further, the body motion detection unit 27 calculates a body motion determination value for determining the body motion of the person 2 at the determination target time point based on measurement data (for example, body motion data) within the tenth predetermined period, The calculated body movement determination value may be compared with a body movement reference value that is a reference for body movement determination, and the body movement of the person 2 may be determined based on the comparison result.

  In this case, the tenth predetermined period is within a time zone having a certain width enough to determine whether the movement of the person 2 is periodic or aperiodic. For example, calculation of a body movement determination value In the case where information entropy or CV value is used as will be described later, it may be set to about 5 to 10 seconds. The body movement determination value is a secondary parameter at each time point obtained from measurement data (for example, body movement data).

  Further, in this case, the body movement determination value may be an amount related to variation in measurement data (for example, body movement data) within the tenth predetermined period. The amount related to variation is, for example, information entropy or variation coefficient (CV value). First, the CV value can be calculated by “CV value = standard deviation / average value”, and has the meaning of normalizing the magnitude of the fluctuation with the bias level. Therefore, it does not react greatly to fluctuations in the bias level of the measurement data or slow changes compared to the calculation period, but reacts to sudden fluctuations that are large compared to the bias state.

Information entropy refers to the entropy used in physics as the uncertainty of an event, and if the decrease in uncertainty due to certain information is the amount of information, the relative value of uncertainty before and after receiving the information. It is. For example, when rolling a dice, the uncertainty or information entropy is maximal since it is not known at all what the eye came out before looking at the result. Upon receiving the information that an odd number of eyes have appeared, the information entropy decreases. If it is known that the first eye has come out, the result is uniquely determined and the information entropy is minimized. The amount of information is a monotonically decreasing function with respect to the probability. Knowing that an unusual event has occurred, the amount of information is large, but of course an unusual event rarely occurs. Each time you know that something unusual has happened, but it is not unusual, the total amount of information observed over time can be large. From this point of view, the average amount of information for a long time is information entropy. Here, p (x) indicates the probability that the event x will occur in the set X of a plurality of events.

  Information entropy can be obtained from a histogram of measurement data. The histogram is, for example, as shown in FIG. In FIG. 3, the vertical axis represents frequency and the horizontal axis represents class (class). The class corresponds to, for example, the magnitude of the output of measurement data, and is set at equal intervals based on the distribution of measurement data in the drawing. Specifically, the class width may be divided into N from 0 to the average value + standard deviation × 2 based on the average value and standard deviation of all measurement data. Here, N may be determined based on the total number of data. For example, it is usually 10 to several tens. Alternatively, it may be possible to divide 50 times by 4 times the frequent value obtained by the provisionally set class width. Here, the occurrence probability p (x) of x of a certain class = frequency of class / total number of data. In the rightmost class (N class) in the figure, since all data of N class or more are counted, the frequency is large.

  In this case, as shown in FIG. 3, when obtaining a histogram with the vertical axis representing the frequency and the horizontal axis representing the class (class), the class width (horizontal axis) is preferably a log scale histogram. As a result, the class width changes in proportion to the center value of the class, and information entropy that is invariant to fluctuations in the sensitivity of the signal (for example, measurement data) can be obtained. The width of the class of the histogram can be obtained from the number of data in the tenth predetermined period, the distribution range of body motion data in the overnight (about 7 to 10 hours), variation, and the like. it can.

  The body movement determination value may be calculated from the frequency distribution around the determination target time point. Here, the periphery of the determination target time point is, for example, a tenth predetermined period including or adjacent to the determination target time point. The frequency distribution is reflected in, for example, an output through a high-pass filter, a distribution of frequency components by Fourier transform, and an output distribution with respect to a scale by wavelet transform. The body motion determination value may be calculated based on the distribution of the magnitude (output value or the like) of the measurement data (for example, body motion data) thus extracted.

  In addition, the body motion reference value used as a reference when calculating and comparing the body motion determination value as described above is a single value as a reference value regardless of individual measurement data (for example, body motion data). Or may be determined from a distribution of a plurality of body movement determination values for each determination target time point or a distribution of body movement determination values for one night (about 7 to 10 hours) measured in advance. . In addition, the body motion detection unit 27 calculates a body motion reference value that is a reference for determining the body motion of the person 2 based on measurement data (for example, body motion data) within a ninth predetermined period before the determination target time point. Then, the body motion determination value at the determination target time point may be compared with the body motion reference value, and the body motion of the person 2 may be determined based on the comparison result.

  The body motion determination by the body motion detection unit 27 is performed by setting a body motion threshold value from a distribution of measurement data (for example, body motion data) in a ninth predetermined period, for example, and setting the body motion reference value. And may be determined by comparison with body motion data at the determination target time point. Here, the body motion threshold value Thm (shown in FIG. 3) is, for example, the mode up to a position where the frequency of the mode value in the histogram of FIG. 3 becomes a certain ratio (eg, 60% (ie, e−1 / 2)). It is possible to determine a position obtained by adding a certain multiple (for example, three times) of the distance from the value to the mode value. In addition, since the right side of the peak of the histogram in the figure is affected by body movement, the ratio of the frequency to the peak may be evaluated on the left side.

  Next, the threshold value calculation unit 28 is based on the measurement data of the first predetermined period excluding the body motion period that is the period in which the body motion detection unit 27 detects the body motion among the periods before and after the determination target time point. A threshold value used for determining the state of the person 2 by the state determining unit 26 is calculated.

  The first predetermined period may be, for example, 1 minute or 1 hour, and may be a time sufficient to calculate the threshold, and typically includes a plurality of determination target time points. Preferably, since the first predetermined period represents the most recent state of the person 2, approximately 1 minute to several minutes is appropriate, and here, for example, 2 minutes.

  Here, as shown in FIG. 4A, when the body motion period detected by the body motion detection unit 27 and the first predetermined period overlap, the remaining data excluding the measurement data of the body motion period are included. Measurement data for a period corresponding to the excluded period is added to the measurement data to obtain measurement data for the first predetermined period. Specifically, for example, when there is a body movement period detected by the body movement detection unit 27 on the rear side (future side) of the first predetermined period, the front side of the first predetermined period (2 minutes) ( It is preferable to add measurement data for a period corresponding to the excluded period from the measurement data on the past side (see FIG. 4B). For example, when there is a large body motion slightly before the determination target time point and a body motion period exists on the front side (past side) of the first predetermined period, the first predetermined period (2 minutes) It is preferable to add measurement data in a period corresponding to the excluded period from the measurement data on the rear side (future side) following the determination target time (FIG. 4C). That is, measurement data for two consecutive minutes (first predetermined period) not including the body movement period can be secured. In addition, it is good to take the continuous period without a body movement period as much as possible for the 1st predetermined period including a determination object time point. By not including the body movement period in the first predetermined period, the threshold value calculation unit 28 can calculate a respiration reference value described later with high accuracy, and accurately calculate a threshold value calculated based on the respiration reference value. Therefore, the state determination by the state determination unit 26 can be performed more accurately.

  Returning to FIG. 1 again, the threshold value calculated by the threshold value calculation unit 28 based on the measurement data for the first predetermined period and used for determining the state of the person 2 is determined based on a respiration reference value to be described later. In other words, the threshold value calculation unit 28 calculates a respiration reference value based on measurement data, here, respiration data, and calculates a threshold value based on the respiration reference value.

  The state determination unit 26 corresponds to the first measurement data corresponding to the chest region 2a as the first region of the person 2 or the abdominal region 2b as the second region different from the chest region 2a of the person 2. The second measurement data to be compared is compared with a threshold value to determine the state of the person 2. Further, as will be described later, the state determination unit 26 is also configured to determine the state of the person 2 by comparing the entire measurement data corresponding to substantially the entire person 2 with a threshold value.

  Here, in the present embodiment, the chest region 2a as the first region of the person 2 is the upper part (chest region) of the upper body of the person 2, and the abdominal region 2b as the second region of the person 2 is the person. 2 is the lower part (abdomen) of the upper body. Further, substantially the entire person 2 is typically an area obtained by combining the chest area 2 a and the abdominal area 2 b of the person 2. That is, the term “substantially whole of the person 2” here does not mean from the head to the toe of the person 2 as the object, but the meaning of the whole of a portion that is a region to be measured by the FG sensor described later with reference to FIG. Yes, it is a region where the chest and abdomen of the person 2 are combined.

  The first measurement data corresponding to the first region is measurement data corresponding to the upper part (chest) of the upper body of the person 2, and the second measurement data corresponding to the second region is It is measurement data corresponding to the lower part (abdomen) of the upper body. The total measurement data is measurement data corresponding to substantially the entire person 2, and is typically data obtained by combining the first measurement data and the second measurement data.

  Furthermore, the threshold value calculation unit 28 is based on the first threshold value based on the measurement data corresponding to the upper part (chest part) of the upper body of the person 2 and the measurement data corresponding to the lower part (abdomen) of the upper part of the person 2. The second threshold value, the third threshold value corresponding to substantially the whole person 2, and the fourth threshold value are calculated. The respiration reference value or threshold is set for an amount indicating the magnitude of the periodic movement of the object, that is, the magnitude of the respiration movement of the person 2. The state determination unit 26 typically determines the state by comparing an amount indicating the magnitude of the respiratory movement of the person 2 at the determination target time point with a threshold value.

  Unless otherwise specified, the first region of the person 2 is referred to as a “chest region 2a”, the second region of the person 2 is referred to as an “abdominal region 2b”, and the substantially entire region of the person 2 is referred to as an “overall region”. . Further, the first measurement data is referred to as “chest measurement data”, and the second measurement data is referred to as “abdominal measurement data”. Furthermore, the first threshold is referred to as “chest threshold”, the second threshold is referred to as “abdominal threshold”, the third threshold is referred to as “first overall threshold”, and the fourth threshold is referred to as “second overall threshold”. Note that the second overall threshold is a threshold used when determining the normal breathing state (fourth state) of the person 2, and is typically a threshold having a value larger than the first overall threshold. . In addition, when there is no need to describe the chest threshold value, the abdominal threshold value, and the overall threshold value separately, they are simply referred to as threshold values.

  Here, the magnitude of the breathing motion of the person 2 at the determination target time is, for example, respiratory data within a certain period (for example, 5 to 15 seconds) including a period of at least one cycle (FIG. 2A). Amplitude), peak, bottom, amount corresponding to tidal volume, etc. can be used. Here, the peak is the peak portion of the waveform pattern formed by the measurement data, and the bottom is the valley portion (see FIG. 2A). Here, the amplitude is the difference between the peak and the bottom.

  The threshold calculation unit 28 calculates the respiration reference value based on the magnitude of the respiration movement of the person 2. Here, the threshold value calculation unit 28 is configured to calculate the respiration reference value based on the average value of both or one of the peak and the bottom of the measurement data. The respiration reference value calculated by the threshold value calculation unit 28 may be the average value of either the peak or the bottom of the measurement data within the first predetermined period before the determination target time, or both the peak and the bottom It may be an average value. Furthermore, it is good also as an average value of the amplitude of the respiration data which can be calculated from the difference of the average value of a peak and a bottom.

  Further, it is more preferable that the threshold value calculation unit 28 evaluates the stability of the measurement data within the first predetermined period and calculates a respiration reference value based on the evaluation. The stability evaluation may be performed based on variations in the peak (or bottom) and peak intervals (or bottom intervals and zero-cross intervals) of the respiratory data in the target period, here the first predetermined period. The threshold value calculation unit 28 may calculate the respiration reference value by a different method (for example, a different calculation formula) based on the evaluation result. Here, the zero cross means that the value of the periodic motion is zero, for example, as shown in FIG. 2A, when the horizontal axis is time and the vertical axis is speed, the speed intersects the time axis. Or the crossing point. That is, the point where the speed becomes zero or zero.

  Specifically, when it is evaluated as stable, as described above, the average value of either or both of the peak and the bottom of the respiratory data within the first predetermined period, or the difference between the peak and the bottom The average value of (respiration data amplitude) or the difference between the average value of the peak and the average value of the bottom is used as the respiration reference value. In addition, if it is evaluated as unstable, the respiratory data peak and bottom within the first predetermined period and / or either one of the larger ones from the average value of the three values or the peak from the larger one The difference between the average value of the three values and the average value of the three values from the larger one at the bottom is taken as the respiratory reference value. Or let the average value of three values from the one with the larger amplitude of respiration data be a respiration standard value.

  In addition, the threshold calculation unit 28 evaluates the stability of the measurement data for a certain period after the determination target time when it is evaluated as unstable in the above-described stability evaluation. The respiration reference value calculated in the subsequent fixed period is set as the respiration reference value at the determination target time point. Here, the subsequent fixed period is the same period as the first predetermined period described above, but here, the first predetermined period is shorter, and may be, for example, about one minute. For example, when the front side (past side) of the determination target time point of person 2 is unstable breathing and the rear side (future side) is stable breathing with a small amplitude, If the evaluation is performed in a predetermined period of 1, the respiration reference value is calculated in an unstable respiration state. For this reason, there is a possibility that all future breaths with small amplitudes are determined to be apnea and hypopnea in the determination by the state determination unit 26 described later. Also, for example, when the past (first predetermined period before the determination target time) is stable breathing and the future (first predetermined period after the determination target time) is unstable breathing, the determination target time point is unstable breathing. If it enters the region, it is evaluated as unstable relatively quickly, so that the determination by the state determination unit 26 during the period evaluated as unstable is correctly determined.

  The threshold calculation unit 28 always evaluates the stability of the measurement data in the first predetermined period after the determination target time in the above-described stability evaluation, and the first predetermined period before the determination target time. If it is evaluated as unstable, the respiration reference value calculated in the first predetermined period after the determination target time may be used as the respiration reference value at the determination target time. That is, the stability of the measurement data in the first predetermined period after the determination target time is always evaluated, not limited to the case where the evaluation is unstable.

  Further, the threshold value calculation unit 28 calculates a respiration reference value for both the case where the stability evaluation is evaluated as stable and the case where the evaluation is unstable, and both of the calculated respiration reference values are calculated. If there is a divergence, it should be evaluated as unstable. In addition, it is good to use the respiration reference value calculated by the method different in the case evaluated as stable, and the case evaluated as unstable. In other words, it is preferable to calculate the respiration reference value by both the calculation method for the case where the evaluation is stable and the case where the evaluation is unstable. For example, the respiration reference value l (el) when stable is the average value of the peaks in the period for which stability is evaluated, and the respiration reference value m when unstable is the upper (from the larger) peak in the period 3 Assuming that two average values are used, m is assumed to be unstable when | l−m | / (l + m)> threshold th1, and l is adopted as a respiration reference value when it is not. Alternatively, when the average value excluding the top three is n, m is adopted when | mn − / (m + n)> threshold th2, and l is adopted otherwise.

  Note that a pseudo tidal volume can also be used as an amount indicating the magnitude of the movement of respiration at the determination target time point of the entire measurement data corresponding to the entire region. In this case as well, for example, the average value of the pseudo tidal volume within the first predetermined period may be taken as the respiration reference value.

  Here, the pseudo tidal volume at the determination target time point is a value obtained by integrating measurement data from the zero cross to the zero cross before the determination target time point, in this case, respiration data (see FIG. 2A). Because it is not a strict tidal volume but a value corresponding to tidal volume, it is described as “pseudo”. “From zero cross to zero cross” means a period in which measurement data has a predetermined positive or negative sign between two consecutive zero crosses. In addition, here, “from the zero cross before the determination target time to the zero cross” is typically “from the zero cross immediately before the determination target time to the zero cross”, and more specifically, the determination target time is the reference. As described above, it means from the zero cross just before the zero cross just before the determination target time to the zero cross just before the determination target time. It should be noted that, during three consecutive zero crossings, the measurement data is integrated between the first two zero crosses and the second half zero crosses, which have different signs of the measurement data, and the average of the absolute values is taken. It is good also as pseudo tidal volume. In addition, since a plurality of determination target time points are typically included in the first predetermined period, a plurality of pseudo tidal volumes are determined based on the respiration data in the first predetermined period. Obtainable.

  By using the pseudo tidal volume as the magnitude of the respiratory movement of the person 2 at the determination target time point of the entire measurement data corresponding to the entire area, changes in both the amplitude and the interval of the respiratory data can be reflected. That is, if the amplitude becomes smaller and the interval becomes shorter, the pseudo tidal volume becomes smaller and it can be said that the change is reflected more accurately than the amplitude alone.

  As described above, the threshold value calculation unit 28 performs chest measurement data corresponding to the chest region 2a within the first predetermined period before and after the determination target time point, abdominal measurement data corresponding to the abdominal region 2b, and the entire region corresponding to the entire region. Based on the measurement data, a respiration reference value for each region is calculated. In addition, each respiration reference value forms the baseline within the period (for example, overnight, about 7 to 10 hours) of the measurement data acquired collectively by collecting the values calculated for each determination target time point.

  FIG. 5 shows an example of the waveform pattern of respiratory data and body motion data corresponding to the entire region acquired by actual measurement for a certain period (about 6 minutes). An outline of the baseline will be described with reference to FIG. Further, in this figure, the baseline formed by the calculated respiration reference value is also shown corresponding to the respiration data. As described above, the respiration reference value forming the baseline is calculated based on the respiration data in the present embodiment. As shown in the body motion data shown in the drawing, the portion where the data indicating the body motion (see FIG. 2C) is present is excluded from the calculation of the respiration reference value because the respiration data also changes greatly. That is, the signal level may change at the boundary of the body movement period, and the first predetermined period is set as a series of periods that do not include the body movement period as much as possible. (Respiration standard value) changes. In this figure, the average value of the body movement data is increased on the right side. This is because the measurement point reflecting the movement of the person 2 is changed due to, for example, a change in posture accompanying the body movement. This is because the body motion data is biased with the change in sensitivity.

  Referring back to FIG. 1 again, the threshold value calculation unit 28 calculates each threshold value by multiplying each obtained respiration reference value by a certain coefficient. For example, the threshold value calculation unit 28 calculates a value of about 25% to 15% of each obtained respiration reference value, here a value of 20%, as the chest threshold value, the abdominal threshold value, and the first overall threshold value at the determination target time point. . That is, the threshold calculation unit 28 calculates the chest threshold, the abdominal threshold, and the first overall threshold by multiplying the respiration reference value for each of the chest region 2a, the abdominal region 2b, and the entire region by a coefficient of 0.2. Furthermore, the threshold value calculation unit 28 calculates a value of about 55% to 45% of the respiration reference value obtained according to the entire region, in this case, a value of 50% as the second overall threshold value. That is, the threshold calculation unit 28 calculates the second overall threshold by multiplying the respiration reference value for the entire region by the coefficient 0.5.

  The state determination unit 26 compares the measurement data of each region at the determination target time point with each threshold value calculated by the threshold value calculation unit 28, and based on the comparison result, the state of breathing of the person 2 at the determination target time point is determined. Determine. The state determination unit 26 typically compares the respiration data at the determination target time point with a threshold value.

  For example, when the respiration reference value calculated by the threshold calculation unit 28 is an average value of either the peak or the bottom, the determination by the state determination unit 26 is positive (peak side, inspiration side) or negative. Only the side (bottom side, exhalation side) may be determined, and when the average value of both the peak and the bottom is satisfied, both the positive side and negative side judgments match or The determination may be made based on whether only the determination is made (AND or OR).

  Specifically, the state determination unit 26 calculates the magnitude of the respiratory movement at the determination target time in each region, and determines the magnitude of the respiratory movement at the determination target time in the chest measurement data corresponding to the chest area 2a. When the chest threshold value continuously falls below the chest threshold value for the second predetermined period and the magnitude of respiratory movement at the determination target time point of the abdomen measurement data corresponding to the abdominal region 2b does not fall below the abdominal threshold value, or conversely, the chest measurement data The magnitude of the respiratory movement at the determination target time does not fall below the chest threshold, and the magnitude of the respiratory movement at the judgment target time in the abdominal measurement data falls below the abdominal threshold continuously for the second predetermined period. The person 2 is configured to be determined to be in an obstructive apnea state (first state).

  Here, the second predetermined period is typically a period considered to be longer than the normal breathing interval of the person 2, and may be, for example, about 6 seconds to 15 seconds. Note that, in the sleep apnea determination criterion that is most commonly employed at present, typically, respiratory stop or respiratory depression continues for 10 seconds or longer, so here it is also set to about 10 seconds. However, as will be described later, the interval to the peak before and after falling below the threshold may be set to about 10 seconds. In the present embodiment, the second predetermined period is continuous, for example, the magnitude of the respiratory movement of the chest measurement data is below the chest threshold, and the determination target time point of the abdominal measurement data corresponding to the abdominal region 2b The determination target time points at which the magnitude of the respiratory motion does not fall below the abdominal threshold value are continuous at a plurality of determination target time points corresponding to the second predetermined period. That is, for example, if the interval between determination target points is set to about 0.25 seconds and the second predetermined period is set to about 10 seconds, the above condition continues for 40 determination target points. It can be determined that two predetermined periods have been continued. Note that continuously falling for the second predetermined period does not necessarily mean that the condition must not be deviated from the condition, but it may be substantially continuous. If there is no. The same applies to the following description unless otherwise specified.

  As described above, the state determination unit 26 of the respiratory monitor 1 according to the present embodiment performs the abdominal measurement when the magnitude of the respiratory motion at the determination target time point of the chest measurement data continuously falls below the chest threshold value. The person 2 is in an obstructive apnea state (first state) when only one of the conditions in which the magnitude of the breathing motion at the time of the data determination target continuously falls below the abdominal threshold is satisfied. By determining that it is present, the obstructive apnea state (first state) can be accurately detected.

  That is, in the absence of obstruction, the chest region and the abdominal region move in substantially the same manner, so only one of the magnitude of respiratory movement in the chest measurement data or the magnitude of respiratory movement in the abdominal measurement data is If the magnitude of respiration is significantly lower than the threshold value, and therefore the magnitude of either breathing is significantly reduced, it can be determined that some kind of obstruction has occurred, and the obstructive apnea state (first state) is detected with high accuracy. be able to.

  FIG. 6 is a diagram showing a first specific example of a respiratory waveform (respiratory data) of obstructive apnea. In the figure, the upper part shows the whole measurement data, the middle part shows the chest measurement data, and the lower part shows the abdominal measurement data. In FIG. 6, the magnitude of the breathing movement in the chest measurement data during the apnea period is almost the same as the movement during the normal breathing period, but the abdominal measurement data clearly breathes during the apnea period. The magnitude of the movement has decreased. In other words, the magnitude of the respiratory movement of only one of the chest measurement data and the abdominal measurement data falls below the threshold value, but the other may not be significantly reduced. That is, in an extreme case, it can be determined that an apnea cannot be determined only by evaluation of the entire measurement data, and an apnea can be detected by discriminating the measurement data separately from the chest measurement data and the abdomen measurement data. If the determination of apnea is made only by the magnitude of the respiratory movement of the whole measurement data, as shown in this figure, when the movement magnitude of only one of the chest measurement data or the abdominal measurement data is significantly reduced, However, as described above, it can be determined that only one of them is significantly reduced by detecting that there is a significant decrease. Respiration can be detected.

  The state determination unit 26 determines that the chest measurement data falls below the chest threshold and the abdomen measurement data does not fall below the abdominal threshold, or the chest measurement data does not fall below the chest threshold and the abdomen measurement data falls below the abdominal threshold. If it falls, the peak interval of the respiratory data that is equal to or greater than the threshold immediately before and after that is defined as an obstructive apnea event period, and if this obstructive apnea event period is a certain period, for example, about 10 seconds or more, You may comprise so that it may determine with an event period being an obstructive apnea state (1st state). In this case, strictly speaking, there is a period in which the respiratory data exceeds the threshold value at the beginning and end of the obstructive apnea event period, but the period exceeding the threshold value is constant for the event period. If it is within the ratio, within a certain ratio of the second predetermined period, or within a predetermined period, it is sufficient to satisfy the determination conditions such as apnea and hypopnea. In other words, when the start end of the event period to be compared with the second predetermined period is the peak immediately before and the rear end is the peak immediately after that, the event period is longer than the second predetermined period, and the threshold value If the period exceeding the threshold is within a certain percentage of the event period, or within a certain percentage of the second predetermined period, or within a predetermined period, the period actually falling below the threshold is greater than the second predetermined period. Although it may be shortened, it can be considered that it is substantially falling below the threshold value, and can be included in the concept of continuously falling below the second predetermined period. By doing in this way, it can be seen that the period during which the respiratory data falls below the threshold is substantially continuous for the second predetermined period. The same applies to the state determination by the state determination unit 26 described below unless otherwise specified.

  In addition, the state determination unit 26 picks up the peak and bottom of the respiratory data as the determination target time point, and when the difference, that is, the amplitude of the respiratory data is less than the threshold value and continues for the second predetermined period or longer, You may comprise so that it may discriminate | determine that the continuation period is an obstructive apnea state (1st state). As another determination method, the peak (or bottom) of the respiratory data is picked up as the determination target time point, and when the interval of the peak of the respiratory data that is equal to or higher than the threshold value becomes a certain period, for example, about 10 seconds or more, further You may comprise so that it may discriminate | determine that it is an obstructive apnea state (1st state) when the period when breathing data is less than a threshold value between both peaks is more than a 2nd predetermined period. The same applies to the state determination by the state determination unit 26 described below unless otherwise specified.

  The state determination unit 26 further determines that the magnitude of the respiratory motion at the determination target time point of the chest measurement data corresponding to the chest region 2a is the chest threshold value and the respiration at the determination target time point of the abdominal measurement data corresponding to the abdominal region 2b. When the magnitude of the movement continuously falls below the abdominal threshold for a sixth predetermined period, the person 2 is determined to be in the central apnea state (second state).

  Here, the sixth predetermined period is typically the same period as the second predetermined period, and is considered to be longer than the normal breathing interval of the person 2, for example, 6 seconds to 15 seconds. It should be about. In this case, too, the sleep apnea criterion that is most commonly adopted at present is typically that respiratory stop or hypopnea lasts for 10 seconds or more, so here again, it is about 10 seconds. To do. The third predetermined period, the fourth predetermined period, the fifth predetermined period, and the eighth predetermined period described below are similar periods.

  As described above, the state determination unit 26 continuously determines the respiratory movement magnitude at the determination target time point of the chest measurement data and the respiratory movement magnitude at the determination target time point of the abdominal measurement data as the abdominal threshold value. Whether or not the overall respiratory movement of the person 2 is significantly reduced when the respiratory movement of both the chest region 2a and the abdominal region 2b is significantly reduced. Even if the determination is not made, it is possible to accurately detect the central apnea state (second state) by determining that the person 2 is in the central apnea state (second state).

  FIG. 7 is a diagram showing a first specific example of a respiratory waveform (respiration data) of central apnea. Like FIG. 6, in the figure, the upper part is the whole measurement data, the middle part is the chest measurement data, and the lower part is Abdominal measurement data is shown. In central apnea, both the magnitude of respiratory movement in the chest measurement data and the magnitude of respiratory movement in the abdominal measurement data are significantly reduced. In the respiratory monitor 1 using the FG sensor 10 to be described later, the total measurement data is the sum of the chest measurement data and the abdominal measurement data, so the magnitude of the respiratory movement of the chest measurement data and the magnitude of the respiratory movement of the abdominal measurement data. If both are significantly reduced, the overall measurement data is also greatly reduced. Therefore, the chest measurement data and the abdomen can be measured without determining whether the respiratory movement of the overall measurement data is below the threshold. The determination of the central apnea state (second state) can be made accurately based only on the magnitude of the respiratory movement of the measurement data. At the same time, the central apnea state (second state) can be accurately distinguished from the obstructive apnea state (first state).

  Further, in the present embodiment, the state determination unit 26 determines that the magnitude of the respiration movement at the determination target time point of the entire measurement data corresponding to the entire region of the person 2 exceeds the first entire threshold for a fourth predetermined period. The respiratory movement magnitude at the determination target time point of the chest measurement data is the chest threshold value, or the respiratory movement magnitude at the determination target time point of the abdominal measurement data is the abdominal threshold value. It is configured to determine that the person 2 is in the obstructive apnea state (first state) even when the period is continuously increased.

  Here, the fourth predetermined period and the fifth predetermined period are typically overlapping periods, that is, the magnitude of the respiration movement of the total measurement data falls below the first total threshold, and at the same time. When the period during which either the magnitude of the respiratory movement of the chest measurement data or the magnitude of the respiratory movement of the abdominal measurement data exceeds the chest threshold or the abdominal threshold continues for a predetermined period at the same time, the state determination unit 26, it is determined that the person 2 is in an obstructive apnea state (first state).

  The magnitude of the respiratory movement of the overall measurement data is below the first global threshold, i.e., the magnitude of the respiratory movement in the entire area of the person 2 is significantly reduced, so that at least an obstructive apnea condition (First state) or central apnea (second state), and at the same time, the magnitude of the respiratory movement of the chest measurement data and the magnitude of the respiratory movement of the abdominal measurement data are If both are not significantly lowered, it can be determined as an obstructive apnea state (first state). Conversely, if the magnitude of respiratory movement in the chest measurement data and the magnitude of respiratory movement in the abdominal measurement data are both significantly reduced, it is determined as a central apnea state (second state). can do. That is, it is possible to determine the detailed state, and it is possible to analyze the state more accurately. Further, when used in combination with the above-described determination of the obstructive apnea state (first state), it is possible to make a more reliable and leak-free determination.

  In addition, when comparing the magnitude of the respiratory motion at the determination target time point of the whole measurement data corresponding to the whole area of the person 2 with the first overall threshold, the above-described pseudo one time is described as the magnitude of the respiratory motion. Ventilation may be used. By using the pseudo tidal volume, changes in both the amplitude and interval of the respiration data can be reflected, and the discrimination can be made more accurately than when only the peak, bottom, and amplitude are used.

  In the present embodiment, the state determination unit 26 further corresponds to the chest measurement data corresponding to the chest region 2a of the person 2 or the abdominal measurement data corresponding to the abdomen region 2b based on the measurement data, and the entire region of the person 2. It is also configured to determine the state of the person 2 by comparing with the whole measurement data.

  That is, the state determination unit 26 determines whether the magnitude of the respiratory motion at the determination target time point of the chest measurement data or the magnitude of the respiratory movement at the determination target time point of the abdominal measurement data is Even when the magnitude of the movement is substantially equal to or surpassing continuously for the third predetermined period, the person 2 is determined to be in the obstructive apnea state (first state).

  FIG. 8 is a diagram showing a second specific example of the respiratory waveform (breathing data) of obstructive apnea. Like FIG. 6, the upper part shows whole measurement data, the middle part shows chest measurement data, and the lower part shows Abdominal measurement data is shown. The motion amplitude remains in both the chest measurement data and the abdominal measurement data, but each cancels out, and the motion amplitude is almost lost in the whole measurement data. This is a typical example of the bizarre movement generally called. As shown in FIG. 8, the movements in the whole region are almost equal because the chest region and the abdominal region move in opposite directions. Looks like it disappeared. In other words, the magnitude of the respiratory movement of the chest measurement data or the abdominal measurement data is approximately equal to or greater than the magnitude of the respiratory movement of the entire measurement data.

  In this embodiment, measurement data is acquired using the FG sensor 10 as will be described later, and therefore the chest measurement data, abdominal measurement data, and the whole measurement data have the same measurement principle. For example, in the PSG apparatus using the mouth-nose flow sensor for measuring the whole as described above and the coil and strain gauge (strain gauge) for measuring the chest and abdomen, the measurement principle is different. Although the measurement values corresponding to the chest and abdomen could not be directly compared, in the present embodiment, it is possible to directly compare the chest measurement data, the abdomen measurement data, and the entire measurement data.

  The state determination unit 26 is based on a relative comparison result between the magnitude of the respiratory movement at the determination target time point of the entire measurement data and the magnitude of the respiratory movement of the chest measurement data or the magnitude of the respiratory movement of the abdominal measurement data. Therefore, the person 2 is in the obstructive apnea state (the first state) without determining whether or not the magnitude of the movement of the breathing in the whole person 2 is significantly reduced. Therefore, the detection accuracy of the obstructive apnea state (first state) can be improved. In addition, this makes it possible to detect the detection sensitivity of measurement data acquired by the FG sensor 10 to be described later depending on the environmental conditions such as the posture of the person 2 and the situation of the top, the position of the person 2 on the bed 3, the situation of ambient light, etc. Even when the noise level has changed, for example, it is possible to make a more accurate determination appropriately corresponding to the change without using one fixed threshold value. In this case, since it is not necessary to calculate a threshold value, the data processing amount in the respiratory monitor 1 can be reduced.

  In the state determination unit 26, the obstructive apnea state (first state) and the central apnea state (second state) are substantially continuous, and the person 2 is in the obstructive apnea state (first state). ) And the central apnea state (second state) are substantially continuous for the seventh predetermined period, the person 2 is in a mixed apnea state (third state). Configured to discriminate.

  Here, the seventh predetermined period is typically the same period as the second predetermined period and is considered to be longer than the normal breathing interval of the person 2, for example, 6 seconds to 15 seconds. It should be about. In this case, too, the sleep apnea criterion that is most commonly adopted at present is typically that respiratory stop or hypopnea lasts for 10 seconds or more, so here again, it is about 10 seconds. To do. Furthermore, here, “substantially continuous for the seventh predetermined period” does not necessarily mean that the condition must not be deviated, but it is only necessary that the condition is substantially continuous. That's fine.

  A mixed apnea state (third state) is generally preceded by a central apnea state (second state), which transitions to an obstructive apnea state (first state). Often ends the apnea period. However, since the reverse case is also conceivable, any order may be used.

  Further, the state determination unit 26 does not continuously reduce the second overall threshold value calculated by the threshold value calculation unit 28 for the eighth predetermined period in the magnitude of the respiratory movement at the determination target time point of the total measurement data. In this case, the person 2 is configured to determine that the person 2 is in a normal breathing state (fourth state) having no problem in the diagnosis of sleep apnea syndrome (SAS). In other words, the state determination unit 26 is normal with no problem when the magnitude of the respiration movement at the determination target time of the total measurement data exceeds the second total threshold (regardless of continuous or discontinuous). You may comprise so that it may discriminate | determine that it is in the state (4th state) of the perfect breath.

  The normal breathing state (fourth state) is an apnea state in which the airflow caused by respiration such as obstructive apnea, central apnea, and mixed apnea is almost stopped. It is a state which is not any of the hypopnea state (hypopnea) which is the state which fell clearly. Thereby, for example, without determining whether or not the magnitude of the movement of breathing in the entire person 2 is significantly reduced, an obstructive apnea state (first state) or a central apnea state ( When the second state) is determined, it is possible to eliminate a determination that is considered to be obviously wrong due to some factor, so that a more accurate analysis of the state becomes possible.

  In other words, when the magnitude of the respiration movement of the whole measurement data continuously falls below the second whole threshold, the state of the person 2 is any apnea state or low. Considered to be in a respiratory state. Therefore, the state determination unit 26 continuously reduces the magnitude of the respiration movement of the entire measurement data below the second overall threshold, and the above-described obstructive apnea state (first state), If it is determined that neither the breathing state (second state) nor the mixed apnea state (third state) is present, the state of the person 2 is a hypopnea state in which the airflow due to breathing is clearly reduced. It can also be configured to discriminate. This makes it possible to accurately determine the hypopnea state.

  According to the respiratory monitor 1 according to the present embodiment, the state of the person 2 is determined according to the chest region 2a, the abdominal region 2b, and the entire region as described above, for example, for the entire complex respiratory motion. It is possible to perform more accurate state analysis such as correct evaluation, detailed state analysis, and improvement of analysis accuracy. For example, in the determination based only on the entire signal corresponding to the entire region of the person 2, a more detailed type determination of the apnea state that is difficult to determine, that is, the obstructive apnea state as the first state, The central apnea state as the state and the mixed apnea state as the third state can be distinguished and discriminated.

  Note that more detailed types of apnea states determined by the respiration monitor 1 are, for example, an overnight measurement test, an obstructive apnea state (first state), a central apnea state (second state), By recording how many times the obstructive apnea mixed apnea state (third state) has occurred, it can be used as an item necessary for diagnosis of sleep apnea syndrome (SAS) by a doctor or the like.

  As described above, the state determination unit 26 determines that the state of the person 2 is an obstructive apnea state (first state) when any one of a plurality of different conditions is satisfied. By determining that it is, it is possible to determine a comprehensive and leak-free state. However, it goes without saying that it is not necessary to use all of the plurality of different conditions described above.

  In the above description, the state determination means for determining the obstructive apnea state (first state) based on the comparison between the chest measurement data and the chest threshold and the abdominal measurement data and the abdominal threshold, the chest measurement data and the whole measurement data , State discriminating means for discriminating an obstructive apnea state (first state) based on comparison between abdominal measurement data and overall measurement data, overall measurement data and overall threshold, chest measurement data and chest threshold, abdominal measurement data and abdomen The state determination means for determining the obstructive apnea state (first state) based on the comparison of the threshold values has been described as being the integrated state determination unit 26. However, the first state determining unit (not shown), the second state determining unit (not shown), and the third state determining unit (not shown) may be configured separately. State determining means for determining a central apnea state (second state), state determining means for determining an obstructive apnea mixed apnea state (third state), and a normal breathing state (fourth state) The same applies to the state discriminating means for discriminating.

  Further, for example, in the case of the respiratory monitor 1 including only the state determination means for determining the obstructive apnea state (first state) based on the comparison between the chest measurement data and the whole measurement data and the abdominal measurement data and the whole measurement data. Since it is not necessary to calculate the threshold value, the threshold value calculating unit 28 may be omitted. That is, the configuration may be changed as appropriate according to which state determination means is provided. Further, in the above embodiment, the case has been described where measurement data for a certain period of each region is acquired and analyzed together. However, even if measurement data is acquired and analyzed in real time along with the state transition of the person 2 good. Also in this case, it is possible to perform more accurate analysis of the state, such as a detailed analysis of the state and an improvement in the accuracy of the analysis as compared with the conventional case. When acquiring and analyzing measurement data in real time, for example, the first predetermined period for calculating the threshold is typically a predetermined period before the determination target time, but after the determination target time. There is no problem even if the predetermined period is used. For example, the state analysis may be performed after a lapse of several seconds from the determination target time, that is, there is no problem even if there is a slight time lag between the acquisition of measurement data and the state analysis as long as there is no problem in the state analysis.

  FIG. 9 is a schematic perspective view of the FG sensor 10 of the respiratory monitor 1 according to the embodiment of the present invention. Here, with reference to this figure, the FG sensor 10 which is a measuring apparatus suitable for the respiration monitor 1 is demonstrated. The FG sensor 10 includes a projection device 11 that projects a predetermined illumination pattern 11a, in other words, a plurality of bright spots 11b, on the bed 3 as a target area, and light projected by the projection apparatus 11, that is, a plurality of bright spots 11b. Is configured to include an imaging device 12 that images the bed 3 on which the image is projected, and a measurement unit 14 that generates measurement data indicating the state of the person 2 based on an image captured by the imaging device 12. In the present embodiment, the measurement unit 14 is described as being configured integrally with the arithmetic device 20.

  FIG. 10 is a block diagram showing a schematic configuration of the FG sensor 10 of the respiratory monitor 1 according to the embodiment of the present invention. The measurement unit 14 includes a movement amount calculation unit 141 serving as a movement amount calculation unit that calculates a movement amount between the two frames of a plurality of bright spots from two frames of images acquired at two different times by the imaging device 12; A movement amount waveform generation unit 142 is provided as movement amount waveform generation means for generating movement amount waveform data in which movement amounts calculated by the movement amount calculation unit 141 are arranged in time series.

  Here, the measurement of the movement of the bright spot based on the images at two different time points will be described. The images at two different time points may be an arbitrary time point and a slightly previous time point. “Slightly before” only needs to be a time interval sufficient to detect the movement of the person 2. In this case, when a slight movement of the person 2 is desired to be detected, the time is short, for example, the movement of the person 2 does not become too large, and the time can be regarded as substantially no movement, for example, about 0.1 seconds. . Or it is good to set it as the 1-10 period (1 / 30-1 / 3) of a television period. Further, when it is desired to detect a rough movement of the person 2, it may be long, for example, about 10 seconds. However, in the case of detecting the respiration of the person 2 as in the present embodiment, if it is too long, accurate detection of respiration cannot be performed. Hereinafter, an image acquired at an arbitrary time (current) is described as an acquired image, and an image acquired slightly before (past) the acquired image is described as a reference image. The reference image is stored in a storage unit (not shown).

  Further, in the present embodiment, the images at two different time points are an acquired image (N frame) and an image acquired immediately before the acquired image (N-1 frame). That is, the reference image is an image acquired immediately before the acquired image. The image acquisition interval may be appropriately determined depending on, for example, the processing speed of the apparatus and the content of the motion to be detected as described above. For example, the interval is 0.1 to 3 seconds, preferably about 0.1 to 0.5 seconds. It is good to do. Here, it is set to 0.1 to 0.25 seconds. In addition, it is effective to acquire images at shorter time intervals and perform averaging or filtering to reduce the influence of random noise, for example. The measuring unit 14 will be described in detail later.

  Returning to FIG. 9, an installation example of the FG sensor 10 will be described. On the bed 3 in the figure, the person 2 lies in a sleeping state. Here, the bedding 4 is further hung on the person 2 and covers a part of the person 2 and a part of the bed 3. In this case, the FG sensor 10 measures the amount related to the movement of the upper surface of the bedding 4 in the height direction within a certain time. When the bedding 4 is not used, the FG sensor 10 measures an amount related to the movement of the person 2 in the height direction within a certain time. Note that the movement of the person 2 in the height direction is, for example, movement accompanying the breathing or body movement of the person 2.

  The projection device 11 and the imaging device 12 constituting the FG sensor 10 are arranged above the bed 3 that is the target region in the vertical direction. The measurement range is set to a range including the chest and abdomen of the person 2. In the drawing, the projection device 11 is disposed approximately above the head of the person 2, and the imaging device 12 is disposed approximately at the center of the bed 3 or slightly above the head from the center. The projection device 11 projects a pattern 11 a as an illumination pattern on the bed 3. The pattern 11a is a plurality of bright spot lights. In addition, the angle of view of the imaging device 12 is set so that an approximately central portion of the bed 3 including a portion corresponding to the upper half of the person 2 can be captured. The imaging device 12 is installed so as to look down on the bed 3 substantially vertically, but may be installed with a certain degree of inclination. Here, each position of the plurality of bright spots 11b projected on the person 2 that can be imaged by the imaging device 12 corresponds to each measurement point.

  The projection device 11 and the imaging device 12 may be installed with a certain distance. By doing so, the distance d (base line length d, see FIG. 12) becomes longer, so that the change can be detected sensitively (detection sensitivity is improved). The base line length is preferably long, but may be short. However, in this case, it is difficult to detect small movements such as respiration, but small movements (breathing) can be detected by detecting the barycentric position of the bright spot as described later.

  Here, the baseline length d (see FIG. 12) will be described. Here, as will be described later with reference to FIG. 12, the FG sensor 10 measures the movement of a bright spot forming a pattern. At this time, for example, the greater the movement of the object (here, the person 2) in the height or height direction, the greater the movement amount of the bright spot. For this reason, according to the concept described later with reference to FIG. 12, if the amount of movement of the bright spot is large, a phenomenon may occur in which the bright spot adjacent to the bright spot to be compared is skipped. In this case, it is determined that the light has moved from the adjacent bright spot, and the amount of movement of the bright spot to be measured may be small. That is, the movement amount of the bright spot cannot be measured accurately. When the base line length is short, the amount of movement of the bright spot is small and the above jump is unlikely to occur, but it is difficult to distinguish it from noise for minute movements. In addition, when the base line length d (see FIG. 12) is long, even a slight movement of the object is greatly reflected in the amount of movement of the bright spot, for example, a minute movement in the height or height direction. Can be measured, but jumping may occur, for example, when there is a large movement.

  FIG. 11 is a schematic perspective view for explaining the projection device 11 of the respiratory monitor 1 according to the embodiment of the present invention. With reference to this figure, the projection apparatus 11 suitable for the respiration monitor 1 is demonstrated. Here, for the sake of explanation, a case will be described where the target region is the plane 102 and a laser beam L1 described later is projected perpendicularly to the plane 102. The projection apparatus 11 includes a light beam generation unit 105 serving as a light beam generation unit that generates a coherent light beam, and a fiber grating 120 (hereinafter simply referred to as a grating 120). The coherent light beam projected by the light beam generation unit 105 is typically an infrared laser. The light beam generation unit 105 is configured to generate a parallel light beam. The light flux generation unit 105 is typically a semiconductor laser device including a collimator lens (not shown), and the generated parallel light flux is a laser light flux L1. The laser light beam L1 is a light beam having a substantially circular cross section. Here, the parallel light flux only needs to be substantially parallel, and includes a nearly parallel light flux. The substantially circular shape includes a substantially elliptical shape.

  Further, here, the grating 120 is disposed in parallel to the plane 102 (perpendicular to the Z axis). Laser light L1 is incident on the grating 120 in the Z-axis direction. Then, the laser light L1 is collected in a plane having the lens effect by each optical fiber 121, then spreads as a diverging wave, interferes, and a plurality of bright spots on the plane 102 which is a light projecting surface. The pattern 11a which is an array is projected. Note that the arrangement of the grating 120 in parallel with the plane 102 means, for example, that the plane including the axis of each optical fiber 121 of the FG element 122 constituting the grating 120 and the plane 102 are parallel.

  The grating 120 includes two FG elements 122. In the present embodiment, the planes of the FG elements 122 are parallel to each other. Hereinafter, the plane of each FG element 122 is referred to as an element plane. In the present embodiment, the axes of the optical fibers 121 of the two FG elements 122 are substantially orthogonal to each other.

  The FG element 122 is configured by arranging, for example, several tens to several hundreds of optical fibers 121 having a diameter of several tens of microns and a length of about 10 mm in parallel in a sheet shape. Further, the two FG elements 122 may be arranged in contact with each other, or may be arranged at a distance from each other in the normal direction of the element plane. In this case, the distance between the two FG elements 122 is set so as not to interfere with the projection of the pattern 11a. The laser beam L1 is typically incident perpendicular to the element plane of the grating 122.

  Thus, since the grating 120 configured to include the two FG elements 122 serves as an optical system, the optical housing can be downsized without requiring a complicated optical system. Furthermore, by using the grating 120, the projection device 11 can project a plurality of bright spots 11b as patterns 11a onto the target area with a simple configuration. The pattern 11a is typically a plurality of bright spots 11b arranged in a square lattice pattern. The bright spot has a substantially circular shape including an ellipse.

  FIG. 12 is a conceptual perspective view for explaining the concept of the bright spot movement of the respiratory monitor 1 according to the embodiment of the present invention. The imaging device 12 will be described with reference to this figure. The imaging device 12 has an imaging optical system 12 a and an imaging element 15. The image sensor 15 is typically a CCD image sensor. In addition to the CCD, an element having a CMOS structure has recently been actively announced as the image pickup element 15, and these can naturally be used. In particular, some of the elements themselves have inter-frame difference calculation and binarization functions, and it is preferable to use these elements.

  The imaging device 12 may include a filter 12b that attenuates light having a wavelength other than the peripheral portion of the wavelength of the laser light beam L1 generated by the light beam generation unit 105 (see FIG. 11). The filter 12b is typically an optical filter such as an interference filter, and may be disposed on the optical axis of the imaging optical system 12a. In this way, the imaging device 12 can reduce the influence of disturbance light because the light intensity of the pattern 11a projected from the projection device 11 out of the light received by the imaging device 15 is relatively increased. Further, the laser beam L1 (see FIG. 11) generated by the beam generation unit 105 (see FIG. 11) is typically an infrared laser beam. Further, the laser beam L1 (see FIG. 11) may be irradiated continuously or intermittently. When irradiating intermittently, imaging by the imaging device 12 is performed in synchronization with the timing of irradiation.

  Here, the concept of bright spot movement will be described. Here, it is easy to understand, and the target area will be described as the plane 102 and the target object as the object 103. Further, here, for description, the reference image is an image of the pattern 11a when the object 103 is not present on the plane 102, and the acquired image is described as the pattern 11a when the object 103 is present on the plane 102. To do.

  In the figure, an object 103 is placed on the plane 102. Further, the orthogonal coordinate system XYZ is taken so that the XY axis is placed in the plane 102, and the object 103 is placed in the first quadrant of the XY coordinate system. On the other hand, a projection device 11 and an imaging device 12 are arranged above the plane 102 on the Z axis in the drawing. The imaging device 12 images the plane 102 on which the pattern 11 a is projected by the projection device 11. In other words, the object 103 placed on the plane 102 is imaged.

  Here, the imaging lens 12a as the imaging optical system of the imaging device 12 is disposed so that its optical axis coincides with the Z-axis. The imaging lens 12 a forms an image of the pattern 11 a on the plane 102 or the object 103 on the imaging surface 15 ′ (image plane) of the imaging device 15 of the imaging device 12. The image plane 15 'is typically a plane orthogonal to the Z axis. Further, an xy orthogonal coordinate system is taken in the image plane 15 'so that the Z axis passes through the origin of the xy coordinate system. The projection device 11 is arranged at a distance equal to the imaging lens 12a from the plane 102 and a distance d (baseline length d) from the imaging lens 12a in the negative direction of the Y axis. A pattern 11 a formed by a plurality of bright spots 11 b is projected onto the object 103 and the plane 102 by the projection device 11.

  The pattern 11 a projected onto the plane 102 by the projection device 11 is blocked by the object 103 and does not reach the plane 102 in a portion where the object 103 exists. Here, if the object 103 exists, the bright spot 11 b to be projected onto the point 102 a on the plane 102 is projected onto the point 103 a on the object 103. Since the bright spot 11b has moved from the point 102a to the point 103a and the imaging lens 12a and the projection device 11 are separated by a distance d (baseline length d), the point 102a is formed on the imaging plane 15 ′. An image to be imaged at '(x, y) is imaged at a point 103a' (x, y + δ). That is, when the object 103 does not exist and when the object 103 exists, the image of the bright spot 11b moves by a distance δ in the y-axis direction.

  For example, as shown in FIG. 13, the bright spot imaged on the imaging surface 15 ′ of the image sensor 15 moves in the y-axis direction by δ by the object 103 having a height.

  Thus, by calculating the moving amount δ of the bright spot, the position of the point 103a on the object 103 can be specified three-dimensionally. That is, for example, the height of the point 103a is known. Thus, by calculating the difference between a point that should be imaged on the imaging plane 15 ′ if the object 103 is not present and the actual imaging position on the imaging plane 15 ′, The height distribution of the object 103, in other words, the three-dimensional shape can be measured. Alternatively, the three-dimensional coordinates of the object 103 can be measured. Further, if the pitch of the pattern 11a, that is, the pitch of the bright spot 11b is made fine enough that the correspondence relationship of the bright spot 11b is not unknown, the height distribution of the object 103 can be measured in detail.

  Based on the above concept, the height of the object can be measured by calculating the movement amount of the bright spot. However, here, since the movement in the height direction is measured based on the acquired image and the image acquired immediately before the acquired image, that is, the reference image, the movement amount of the bright spot is observed. For this reason, for example, the absolute height of the person 2 cannot be measured, but there is no problem because the purpose is to detect the movement of the person 2 in the height direction.

  Returning to FIG. 10 again, the measurement unit 14 will be described in detail. The movement amount calculation unit 141 calculates the movement amount of the bright spot as described with reference to FIG. The movement amount calculation unit 141 is configured to calculate the movement amount of the bright spot as described above for each of the plurality of bright spots forming the pattern 11a. That is, the positions of a plurality of bright spots are measurement points. The movement amount calculation unit 141 outputs the movement amount of the bright spot calculated for each of the plurality of bright spots forming the pattern 11 a to the movement amount waveform generation unit 142. That is, the calculated movement amount of each bright spot becomes a measurement value at each measurement point. In other words, here, each measured value obtained by measuring the movement of the person 2 at a plurality of points corresponds to the amount of movement of each bright spot.

  It should be noted that a waveform (for example, a sum of bright spot movement amounts) obtained by measuring bright spot movement based on an image at an arbitrary time point and two time points slightly different from the previous time point is a differential waveform of distance, The waveform represents the speed change. For example, when it is desired to obtain a waveform representing a change in height, if the waveform is integrated, a waveform of distance, that is, a waveform indicating a change in height is obtained.

  Here, the acquired image and the reference image are images picked up by the image pickup device 12, for example, and are concepts including the position information of the bright spot on each image. In other words, the acquired image and the reference image are images of the pattern 11a formed by the projection of the projection device 11 at each time point. In the present embodiment, the reference image is not stored as a so-called image, for example, but is stored in a storage unit (not shown) in the form of positional information such as coordinates regarding the position of each bright spot. In this way, when calculating the amount of movement of the bright spot, which will be described later, for example, it is only necessary to compare the coordinates and direction of the bright spot, so the processing becomes simple. Further, here, the position of the bright spot is the barycentric position of the bright spot. By doing so, a slight movement of the bright spot can be measured.

  Further, the movement amount of the bright spot can be calculated by comparing the position information of each bright spot on the reference image with the position information of each bright spot on the acquired image. Each amount of movement can be obtained, for example, by counting the number of pixels to which the position of the bright spot has moved (how many pixels have moved). However, if the position of the bright spot is obtained as the position of the center of gravity, the movement amount can be calculated in units smaller than one pixel. The calculated moving amount of the bright spot is a concept including the moving direction of the bright spot. In other words, the measured moving amount of the bright spot includes information on the moving direction. In this way, as will be described later, it is not necessary to generate a difference image, so that the processing can be simplified.

  In the above description, the position information of the bright spots is compared. However, a difference image between the reference image and the acquired image may be created. In this case, the movement amount of the bright spot is calculated from the difference image based on the position of the corresponding bright spot. In this way, since only the moved bright spot remains on the difference image, the processing amount can be reduced.

  The movement amount waveform generation unit 142 generates movement amount waveform data in which the movement amounts of the bright spots calculated by the movement amount calculation unit 141 are arranged in time series. Here, as described above, the movement amount of the bright spot calculated by the movement amount calculation unit 141 includes the acquired image (N frame) and the image acquired immediately before the acquired image (N-1 frame). Are calculated on the basis of images of two frames acquired at different points in time. In other words, the calculation is made based on images of two time points, which are different from an arbitrary time point and a slightly previous time point. For this reason, the generated movement amount waveform data (for example, when the total movement amount of each bright spot is taken) is a volume fluctuation waveform per unit time or a rough fluctuation waveform of average height per unit time. That is, the waveform represents a change in volume fluctuation or a change in average height. For example, when it is desired to obtain a waveform representing the transition of the height, the waveform of the distance, that is, the waveform indicating the transition of the height is obtained by integrating the waveform.

  Here, when the total amount of movement is taken, the volume fluctuation amount per unit time is generally shown. Since the movement of each bright spot indicates individual height fluctuations, taking the summation results in volume fluctuations. The amount of movement of each bright spot is a change in the bright spot position per unit time at each bright spot position (ie, bright spot moving speed), and roughly corresponds to a height change in unit time.

  The movement amount waveform generation unit 142 generates movement amount waveform data for each of the chest region 2a (first region) and the abdominal region 2b (second region), and uses the generated movement amount waveform data as measurement data. 20 state determination unit 26 (see FIG. 1), body motion detection unit 27 (see FIG. 1), threshold value calculation unit 28 (see FIG. 1), and the like. That is, the movement amount waveform generation unit 142 is a body that is movement data waveform data that is at least the total movement amount waveform data of the bright spot and the absolute value of the absolute movement amount of the bright spot, for example. Measurement data for each of the chest region 2a (first region) and abdominal region 2b (second region) including both waveform data and motion data is output.

  Note that the detection sensitivity and noise level of the measurement data acquired by the FG sensor may be affected by environmental conditions such as ambient light conditions and the posture and position of the person 2 on the bed 3. For example, the projection device 11 (see FIG. 9) is placed in the bed 3 (see FIG. 9) in order to increase the baseline length d (see FIG. 13). The projection of the pattern 11a (see FIG. 9) is performed obliquely when installed off the center of the screen, and the density of bright spots on the bed 3, in other words, the bright spot interval is As the distance increases, the detection sensitivity and noise level may differ between a position close to and far from the projection device 11.

  Returning to FIG. 1 again, the description will be continued. The representative coordinate calculation unit 24 is configured to calculate the representative coordinates of the position coordinate group of the measurement points where the phases of the plurality of movements are substantially the same for the measurement points where the movement of the person 2 is measured. Here, as described above, the positions of a plurality of bright spots projected by the projection device 11 (see FIG. 9) are measurement points. The dividing line forming unit 25 is configured to form an area dividing line 2c that divides the target area between two or more representative coordinates when there are two or more position coordinate groups having different motion phases. The dividing line forming unit 25 typically divides the measurement range of the person 2 as an object existing in the target region into a chest region 2a (first region) and an abdominal region 2b (second region). A region dividing line 2c is formed so as to be divided. The target area is an area on the bed 3 and imaged by the above-described imaging device 12 (see FIG. 9).

  The movement amount waveform generation unit 142 (see FIG. 10) described above is an area divided by the area dividing line 2c formed by the dividing line forming part 25, in the present embodiment, the chest area 2a, the first area as the first area. For each abdominal region 2b as the region 2, the measurement point group data (movement amount of each bright spot) calculated by the movement amount calculation unit 141 (see FIG. 10) is integrated to measure measurement data (breathing data) of the person 2 And body movement data). That is, chest measurement data and abdominal measurement data are generated and output. The total measurement data may be generated and output by integrating the measurement point group data (movement amount of each bright spot) in the entire area, but is simply the sum of the chest measurement data and the abdominal measurement data But you can.

  The movement amount waveform generation unit 142 (see FIG. 10) can generate and output measurement data for each chest region 2a (first region) and abdominal region 2b (second region) divided by the region dividing line 2c. Therefore, for example, even when the movement phases are different between the chest and the abdomen, each measurement data is output, so that the state of the person 2 can be accurately grasped. In the present embodiment described above, the measurement data generated and output by the movement amount waveform generation unit 142 (see FIG. 10) is collected for a certain period, for example, overnight (about 7 to 10 hours). Although described in the case of acquisition, it may be acquired in real time with the state transition of the person 2. That is, the movement amount waveform generation unit 142 (see FIG. 10) may be configured to output measurement data in real time. To output in real time means to immediately output, for example, the sum of the movement amount of the bright spot corresponding to the representative coordinates calculated for each image captured by the imaging device 12 and the region dividing line 2c. Furthermore, in this case, by outputting this sum in real time, the movement amount waveform generation unit 142 (see FIG. 10) outputs a respiration waveform pattern of the person 2 formed side by side in the time direction. Become.

  Here, among the plurality of measurement points measured by the FG sensor 10, the measurement points that do not move, that is, the measurement points that do not move the bright spot are ignored (excluded) and the above calculation (for example, representative) Coordinate calculation). That is, here, processing is performed using all measurement points that are in motion (measured).

  Here, the phase is a concept including the direction of motion, and the phase is substantially the same is a concept including simply matching the directions of motion. In other words, it is a concept including, for example, the direction of movement is approximately upward (upward) or downward (downward). Further, here, the identification of whether the phases are substantially the same is performed by identifying whether the movement measured at each measurement point by the measurement unit 14 is an upward movement or a downward movement. . That is, in the present embodiment, the phase of motion is two directions, upward and downward. Thus, since the phase of the movement is in two directions, up and down, in this case, hereinafter, the phase being substantially the same is simply referred to as the same phase. The representative coordinates are set in the captured image.

  Further, dividing the target area means, for example, dividing an existing area of a plurality of measurement points, and here, dividing an area captured by the imaging device 12 with the person 2 existing on the bed 3. Here, since the imaging device 12 mainly captures the bright spots projected on the chest and abdomen of the person 2 as the measurement range, the region dividing line 2c that divides the target region is the chest region 2a of the person 2 (the first region). ) And an abdominal region 2b (second region). The region dividing line 2c is typically a straight bisector of a straight line connecting representative coordinates of different phases.

  Furthermore, integrating the data of the measurement point group means, for example, measurement points existing in the region divided by the region dividing line 2c, that is, the chest region 2a (first region) and the abdominal region 2b (second region). The sum of the bright spot movement amounts and the sum of the absolute values of the bright spot movement amounts are calculated, and the output motion waveform is a waveform pattern formed by arranging the sums in the time direction.

The representative coordinate calculation unit 24 sets the average value of the coordinates of each measurement point where the representative coordinates form a position coordinate group. Hereinafter, this average value is referred to as center coordinates. The center coordinates are expressed by the following equation (2).

Center coordinates Xcenter = {Σ (Xi)} ÷ n (2)

Here, n is the number of measurement points forming the position coordinate group. Xi is the coordinate value of each measurement point. When calculating in two dimensions, for example, the central coordinates are calculated with Xi as the coordinate value in the x-axis direction and Yi as the coordinate value in the y-axis direction.

Alternatively, the representative coordinate calculation unit 24 may use an average value of values obtained by weighting the coordinates of the measurement points that form the position coordinate group by the amount related to the amount of movement. Hereinafter, this average value is referred to as a barycentric coordinate. Note that the amount related to the amount of movement is typically the amount of movement of the bright spot at each measurement point. Furthermore, the weight is unsigned. That is, the barycentric coordinate is an average value of values obtained by weighting the coordinates of each measurement point with the moving amount of the bright spot. The barycentric coordinates are expressed by the following equation (3).

Centroid coordinates Xcenter ′ = {Σ (Xi × | ΔZi |)} ÷ Σ | ΔZi | (3)

Here, ΔZi is a weight at each measurement point, that is, a moving amount of the bright spot. | ΔZi | is an unsigned weight at each measurement point, that is, an absolute value of the amount of movement of the bright spot.

  Here, with reference to FIG. 14, the calculation of the representative coordinates by the representative coordinate calculation unit 24 and the formation of the region dividing line 2c by the dividing line forming unit 25 will be specifically described. In the figure, for the purpose of explanation, the captured image, the angle of view, the position of the measurement point within the angle of view (only the measurement point that has moved), and the person 2 (shown by a broken line in the figure) are shown. However, it is better to actually calculate only with the numerical value of coordinates. As shown in (a), the representative coordinate calculation unit 24 first calculates the representative coordinates of a position coordinate group that is a set of measurement points having the same movement phase. The position coordinate group is, for example, a set of measurement points having the same movement phase, that is, the same moving direction of the bright spots. By doing so, the amount of calculation can be reduced. In the figure, the representative coordinates indicate the case where the average value of the values obtained by weighting the coordinates of each measurement point with the bright spot movement amount, that is, the barycentric coordinates is calculated.

  In addition, although the case where there are two or more position coordinate groups with different motion phases is described here, there is no position coordinate group with different motion phases, that is, when there are only position coordinate groups with the same phase, For example, the representative coordinate calculation unit 24 calculates the representative coordinates of the position coordinate group of all of the plurality of measurement points, and the dividing line forming unit 25 passes the calculated representative coordinates to divide the target area in an appropriate direction. And this may be used as a region dividing line 2c. The appropriate direction is typically a direction perpendicular to the baseline direction of the FG sensor 10 (see FIG. 9). This is based on the assumption that the spine direction of the person 2 on the bed 3 is substantially parallel to the center line of the bed 3 because the base line direction is parallel to the center line of the bed 3. That is, the spine direction of the person 2 can be viewed as being substantially parallel to the baseline direction. In other words, the appropriate direction is a direction perpendicular to the spine direction of the person 2. That is, in this case, since the region dividing line 2c is perpendicular to the spine direction, for example, the chest and abdomen of the person 2 can be accurately divided, which is preferable. Here, the region dividing line 2c is typically a straight line that is perpendicular to the base line direction and passes through the representative coordinates. For example, when the center line of the bed 3 (the longitudinal direction of the bed), that is, the spine direction of the person 2 is arranged substantially orthogonal to the base line direction, the region dividing line 2c is a direction parallel to the base line direction.

  As shown in the example (a), when there are two position coordinate groups with different movement phases, the dividing line forming unit 25 generates a perpendicular bisector of a straight line connecting the two representative coordinates. The vertical bisector is formed as a region dividing line 2c. Since the figure shows a case where representative coordinates having different movement phases are calculated between the chest and abdomen of the person 2, the region dividing line 2 c is formed between the abdomen and the chest of the person 2. It can also be said that the straight line connecting the representative coordinates of different phases is substantially parallel to the spine direction.

  As shown in (b), the formed region dividing line 2c is a straight bisector of a straight line connecting representative coordinates of different phases, and thus is not necessarily parallel to the base line direction (even if it is oblique). Good). That is, there is no problem even if the spine direction of the person 2 sleeping on the bed 3 is oblique to the baseline direction.

  Further, the dividing line forming unit 25 calculates a value obtained by weighting the representative coordinates calculated by the representative coordinate calculating unit 24 with an amount related to the representative coordinates, and there are at least two calculated representative coordinates, and there are two representative coordinates. The area dividing line 2c may be formed so as to intersect the straight line connecting the two representative coordinates at a point where the straight line connecting the coordinates is internally divided by a value weighted by the amount related to the representative coordinates. The amount related to the representative coordinate is typically the total amount of movement at each measurement point that forms the position coordinate group used for calculating the representative coordinate, that is, the total amount of movement of the bright spot, but the measurement that forms the position coordinate group. There may be a number of points or an average value. Further, it may be a center value (median) or a peak value (maximum value of the amount of movement in the position coordinate group) at each measurement point forming the position coordinate group. Hereinafter, the amount relating to the representative coordinates will be described in the case where the amount of movement of the bright spot at each measurement point forming the position coordinate group is the sum. The center value and the peak value can also be employed as quantities related to the representative coordinates and the amount of movement other than the above.

  For example, as shown in FIG. 15, a point that is internally divided by a value weighted by the total amount of movement of bright spots is an internal ratio of a straight line connecting two representative coordinates by a ratio of a signed weight corresponding to each representative coordinate. (Point A in the figure). In other words, the point to be internally divided is a weighted coordinate corresponding to each representative coordinate which is a coordinate when the amount of weight is indicated in the vertical direction with respect to a straight line connecting the two representative coordinates at the position of each representative coordinate. The point A is an intersection of a straight line connecting G1 and G2 and a straight line connecting two representative coordinates. That is, the dividing line forming unit 25 sets a straight line passing through the point A and perpendicular to the straight line connecting the two representative coordinates as the area dividing line 2c. The region dividing line 2c may be a straight line that passes through the point A and is perpendicular to the base line direction. In this way, the area dividing line 2c reflecting the amount of movement of the person 2 can be formed.

  When the position of the area dividing line 2c is extremely different from the center coordinates of all the measurement points (with movement) ignoring, for example, ascending and descending, the center coordinates of all the measuring points are set to the area dividing line 2c. You may employ | adopt as a passing position. In addition, when the respective weights in the regions having different phases are extremely different, the position through which the region dividing line 2c passes the representative coordinate (center coordinate or barycentric coordinate) with the larger weight or the representative coordinates of all the measurement points. It is good to do. This is because it can be seen that all measurement points are moving in phase. When one representative coordinate is close to the end of the target region, the representative coordinate that is not the end of the target region, or the representative coordinates of all the measurement points may be set as the position where the region dividing line 2c passes.

  Further, the representative coordinate calculation unit 24 calculates the representative coordinates using only the coordinates in the one-dimensional direction of the position coordinates of the plurality of measurement points, and the dividing line forming unit 25 generates the area dividing line 2c perpendicular to the one-dimensional direction. You may make it form. The one-dimensional direction is, for example, the baseline direction of the FG sensor 10 or the spine direction of the person 2, in other words, the direction perpendicular to the straight line connecting the center of the chest and the center of the abdomen, or the line connecting the centers of the left and right lungs. For example, when the spine direction and the base line direction of the person 2 are approximately the same as in the present embodiment (see FIG. 9), the one-dimensional direction is the base line direction. The representative coordinate calculation unit 24 calculates the representative coordinates using only the baseline direction, that is, the y-axis coordinates (see FIG. 13). In this case, the dividing line forming unit 25 forms the area dividing line 2c perpendicular to the base line direction. In this way, the amount of computation by the computing device 20 can be reduced, so that the processing speed can be increased.

  The dividing line forming unit 25 may perform the formation of the area dividing line 2c based on the position of the area dividing line 2c formed in the past. Specifically, when the region dividing line 2c is formed, an average value of the positions of the current and the past several times of the region dividing line 2c may be adopted. If the area dividing line 2c is determined for each movement measurement by the FG sensor 10, the area dividing line 2c may fluctuate due to, for example, noise (not necessarily hand movement or the like). Therefore, by determining the position of the area dividing line 2c based on the average value of the positions of the past area dividing lines 2c, the influence of noise and the like can be removed. That is, in this case, the position of the area dividing line 2c is stabilized, and abrupt movement of the area dividing line 2c due to, for example, noise or movement of the hand of the person 2 can be prevented. For this reason, for example, the output of measurement data for each region by the movement amount waveform generation unit 142 (see FIG. 10), in other words, the chest region 2a (first region) and the abdominal region 2b (second region) of the person 2 is output. It can be done stably.

  Note that, as a modification, the respiratory monitor 1 according to the present embodiment is further modified so that the computing device 20 further divides a plurality of measurement points into a plurality of partial regions (see FIG. 16), and a plurality of heights for each partial region. It has a partial area averaging unit 29 (see FIG. 1, parentheses) that averages the movement in the direction, and the representative coordinate calculation unit 24 sets the averaged value as a target for calculating the representative coordinates of the position coordinate group. You may comprise so that it may be used as a motion of a plurality of measuring points. Here, the plurality of partial regions (see FIG. 16) are different concept regions from the chest region 2a (first region) and the abdominal region 2b (second region). The area is smaller than the area.

  In this case, the partial area averaging unit 29 (see FIG. 1, parentheses) is configured to divide the bright spots of the plurality of measurement points into a plurality of predetermined partial areas (see FIG. 16). Further, the partial area averaging unit 29 calculates the sum of the bright spot movement amounts (or height fluctuation amounts) in the predetermined partial area, or calculates the total bright spot movement amount as the number of bright spots in the partial area. In other words, the average of one bright spot movement amount is calculated, and a plurality of movements in the height direction, that is, the bright spot movement amount is averaged for each partial region. Note that the partial region averaging unit 29 may calculate the sum of the height fluctuation amounts obtained from the bright spot movement amount and average the upper limit fluctuation amount. Here, in the present embodiment, a case will be described in which the sum of the bright spot movement amounts is calculated and the bright spot movement amounts are averaged. Note that the height fluctuation amount can be obtained from the bright spot movement amount assuming that the distance h and the base line length d in FIG. 12 are known (or constant).

  The partial area averaging unit 29 sets the coordinates of the measurement points of the bright spots averaged in each partial area (see FIG. 16) with respect to the measurement points of the bright spots before being averaged in each partial area. , The center of each of the vertical and horizontal partial area widths, or the coordinates of the bright spot at the center, or calculated using the above-described formula (2) or (3) (unless otherwise noted) The sum of the coordinates of the measurement points of the averaged bright spots in the partial area and the bright spot movement amount is referred to as “average value of bright spots in the partial area”). The representative coordinate calculation unit 24 uses the averaged bright spot as a secondary bright spot, that is, a value averaged in each partial area (average value of bright spots in the partial area) as a representative of the position coordinate group. The dividing line forming unit 25 forms the area dividing line 2c based on the representative coordinates, using the movements of a plurality of measurement points to be subjected to coordinate calculation.

  With this configuration, the respiration monitor 1 does not effectively reduce the output related to the motion related to the respiration motion while effectively eliminating the influence of noise. The waveform pattern output corresponding to each region divided by the region dividing line 2c, i.e., the chest region 2a (first region) and the abdominal region 2b (second region), without deviating from a desired position, The effect of separating the movement related to the movement of breathing so as to correspond to each desired area is a pattern that is not diminished.

  FIG. 16 is a schematic plan view for explaining a specific example of the partial region classification by the partial region averaging unit 29 (see FIG. 1, parentheses) of the respiratory monitor 1 according to the modification of the first embodiment of the present invention. FIG. 4A is a first example, FIG. 2B is a second example, and FIG. 3C is a third example. Here, for the sake of explanation, the target region is the plane 102, and the illustration of the target (person 2 or the like) is omitted.

  In the first specific example shown in FIG. 16 (a), the partial area averaging unit 29 divides the measurement points on the plane 102 into a plurality of partial areas for each measurement point. Is configured to average including other measurement points in a predetermined range around the measurement point. Here, an example is shown that is divided so as to include approximately 3 × 3 = 9 measurement points.

  For each luminescent spot, the partial area averaging unit 29 is a partial area in a range appropriately set based on the number of pixels, the luminescent spot interval, etc. of the entire plane 102 around each luminescent spot, for example, a range of about 20 × 20 pixels. Is set, and a bright spot is searched in the partial area. The partial area averaging unit 29 calculates the average value of the bright spots in the partial area based on all the bright spots included in the partial area for each partial area. In this case, the coordinate of the bright spot that is the center may be used as the average bright spot coordinate of the partial area. The representative coordinate calculation unit 24 uses the average value of the bright spots in the plurality of partial areas calculated for each partial area as the movement of the plurality of measurement points that are the targets for calculating the representative coordinates of the position coordinate group.

  In this way, for each luminescent spot, when the partial area is divided and the average value of the luminescent spots in the partial area is calculated, compared to other specific examples described below, an averaged secondary There is an advantageous effect that the density of bright spots does not decrease.

  In the second specific example shown in FIG. 16B, the partial area averaging unit 29 classifies the plurality of measurement points on the plane 102 so that the partial areas do not overlap each other. The bright spot moving amount is averaged. Further, typically, the partial region averaging unit 29 is configured to equally divide the plane 102.

  Unlike the first specific example in which the partial area is divided for each bright spot, the partial area averaging unit 29 of the second specific example substantially uniformly covers the entire plane 102 as the target area, for example, 20 ×. A partial area of about 20 pixels or about 40 × 40 pixels is divided so that the partial areas do not overlap with each other, and a bright spot is searched in the partial area. The partial area averaging unit 29 calculates the average value of the bright spots in the partial area based on all the bright spots included in the partial area for each partial area. In this case, it is preferable to use the centers of the vertical and horizontal widths of the partial areas as the coordinates of the average bright spots of the partial areas. The representative coordinate calculation unit 24 uses the average value of the bright spots in the plurality of partial areas calculated for each partial area as the movement of the plurality of measurement points that are the targets for calculating the representative coordinates of the position coordinate group. That is, one partial region including a plurality of original measurement points is used as one measurement point to be calculated.

  In this way, when the partial areas are divided so that the partial areas do not overlap with each other, and the average value of the bright spots in each partial area is calculated, the averaged secondary is compared with the first specific example. Although the density of bright spots is lowered, there is an advantageous effect that the amount of calculation is reduced accordingly. If a bright spot is associated with the boundary of the partial area, the bright spot may be included in the partial area on the side where the center of gravity of the bright spot is located.

  In the third specific example shown in FIG. 16C, the partial area averaging unit 29 is configured to divide a plurality of partial areas into a substantially strip shape in a certain direction and average the bright spot movement amount. The The constant direction is typically the direction in which the region is desired to be divided by the region dividing line 2c, for example, the direction perpendicular to the straight line connecting the center of the chest and the center of the abdomen, or the straight line connecting the centers of the left and right lungs. . In other words, it is the baseline direction of the FG sensor 10 (see FIG. 9) and the spine direction of the person 2 (see FIG. 9). This assumes in advance a case where the spine direction of the person 2 (see FIG. 9) on the bed 3 (see FIG. 9) is substantially parallel to the center line of the bed. Accordingly, the long side of the partial area having a substantially strip shape is substantially parallel to the area dividing line 2c, and the short side is substantially parallel to the base line. In the following description, it is assumed that the certain direction is the baseline direction.

  The partial area averaging unit 29 of the third specific example is different from the second specific example in that the entire plane 102 as the target area is divided only in a certain direction (baseline direction). The partial area averaging unit 29 typically has a rectangular shape with a constant width of, for example, about 20 pixels, in a fixed direction (baseline direction), for example, the entire plane 102 as a target area so that the partial areas do not overlap each other. A partial region is formed by dividing into two. The partial area averaging unit 29 searches for the bright spot in each partial area, and calculates the average value of the bright spots in the partial area based on all the bright spots included in the partial area for each partial area. To do. The representative coordinate calculation unit 24 uses the average value of the bright spots in the plurality of partial areas calculated for each partial area as the movement of the plurality of measurement points that are the targets for calculating the representative coordinates of the position coordinate group. Note that it is also possible to form many partial regions with the same strip width by overlapping substantially strip-shaped partial regions in the short side direction.

  In this way, dividing a plurality of partial areas into a substantially strip shape in a certain direction (baseline direction) makes it easy to increase the area of the partial area, and the random noise is reduced by including a large number of bright spots in the partial area. The effect becomes high (noise level becomes 1 / √N). Furthermore, when the direction in which the region is to be divided by the region dividing line 2c is determined in advance, the representative coordinate calculation unit 24 uses only a certain direction (baseline direction), that is, the y-axis coordinate (see FIG. 13). Since the representative line is calculated and the dividing line forming unit 25 has only to form the area dividing line 2c perpendicular to a certain direction (baseline direction), the brightness in the partial area calculated by the partial area averaging unit 29 is calculated. The average value of the points only needs to be calculated using only the y-axis coordinates (see FIG. 13), and the amount of calculation by the calculation device 20 can be reduced, so that the processing speed can be increased.

  As described above, by calculating the representative coordinates of the position coordinate group using the average value of the bright spots in the plurality of partial areas, and dividing the target area by the representative coordinate area dividing line 2c, the movement actually occurs. Reduce the influence of the noise of the part that does not have on the calculation of the representative coordinates, for example, can more accurately segment each region of the chest region 2a (first region) and abdominal region 2b (second region) of the person 2, Since the movement amount waveform generation unit 142 (see FIG. 10) can integrate the data of the measurement point group for each region and output the movement waveform, the state determination unit 26 (see FIG. 1) can more accurately determine the state of the person 2. Can grasp.

  Further, as a second modification, the respiratory monitor 1 according to the present embodiment further includes a calculation unit 20 based on a plurality of movements averaged by the partial region averaging unit 29, based on the representative coordinate calculation unit 24. A configuration having an effective partial area extraction unit 30 (see FIG. 1, parentheses) that extracts an effective partial area (see FIG. 17), which is a partial area used for calculating the representative coordinates of the position coordinate group, may be adopted.

  FIG. 17 is a schematic plan view for explaining a specific example of the extraction of the effective partial area by the effective partial area extraction unit 30 of the respiratory monitor 1 according to the second modification of the first embodiment of the present invention. Here, as in FIG. 16, the target region is the plane 102 for the sake of explanation, and the illustration of the target (person 2 or the like) is omitted. Further, in the following description, the partial area averaging unit 29 shown as the third specific example in FIG. 16C divides a plurality of partial areas into a substantially strip shape in a certain direction (baseline direction), and calculates the average. However, it is needless to say that the first and second specific examples may be used.

  The effective partial region extraction unit 30 (see FIG. 1, parentheses) typically extracts an effective partial region based on the average value of the bright spots in each partial region and a predetermined threshold value. The effective partial region extraction unit 30 determines and extracts a partial region in which the fluctuation width of the waveform pattern of the past several frames in each partial region exceeds a set threshold as an effective partial region. The threshold value is most simply determined from a noise level expected in advance. In addition, the maximum and minimum values of the sum of the movements of the averaged bright spots in the partial area from all the partial areas (excluding those with no bright spots or partial bright spots) , And a threshold value may be set at a certain ratio between the maximum value and the minimum value, typically a value close to the minimum value.

  The threshold value for the waveform pattern may be set for the peak value or the bottom value. In addition, the effective partial area extraction unit 30 (see FIG. 1, parentheses) does not determine whether each partial area is an effective partial area. Extract the partial area with the maximum sum of the movements of the averaged bright spots in the area as the effective partial area, and sum the movements of the averaged bright spots in the partial area in the surrounding partial areas. However, only a continuous area up to a partial area below the threshold value may be extracted as an effective partial area for the first time. This is because it can be estimated that the effective partial area in which breathing movement is typically continuous in, for example, an area centered on the chest and an area centered on the abdomen.

  The representative coordinate calculation unit 24 is an effective partial region extracted by the effective partial region extraction unit 30 (see FIG. 1, parentheses), and uses the average value of the bright points in the effective partial region to represent the representative coordinates of the position coordinate group. Calculation is performed, and the dividing line forming unit 25 forms the area dividing line 2c based on the representative coordinates.

  As described above, within the effective partial area extracted from the partial area, the representative coordinates of the position coordinate group are calculated using the average value of the bright spots in the effective partial area, and the area dividing line is calculated based on the representative coordinates. If 2c is formed, noise can be reduced extremely effectively, and the influence of noise in a portion where there is no actual movement on the calculation of representative coordinates is reduced. Therefore, for example, each region of the chest region 2a (first region) and the abdominal region 2b (second region) of the person 2 can be distinguished very accurately, and the movement amount waveform generation unit 142 (see FIG. 10) Since the data of the measurement point group can be integrated and the waveform of the movement can be output, the state of the person 2 can be determined very accurately by the state determination unit 26 (see FIG. 1). In addition, the representative coordinate calculation of the position coordinate group by the representative coordinate calculation unit 24 does not use the measurement points of the partial areas other than the effective partial area, but uses the average value of the bright spots in the effective partial area, so the calculation amount can be reduced. The processing speed can be increased.

  The movement amount waveform generation unit 142 (see FIG. 10) integrates the measurement point group data for each region divided by the region dividing line 2c into the chest region 2a (first region) and the abdominal region 2b ( For each (second region), the measurement may be performed only at the measurement points in the effective partial region, or may be performed including the measurement points in the partial region that is not the effective partial region.

  As shown in FIG. 18A, the partial region averaging unit 29 divides the plurality of first partial regions into a substantially strip shape in a certain direction (baseline direction), and averages the bright spot movement amount. Further, as shown in FIG. 18 (b), separately from the first partial region, a plurality of second partial regions are divided into substantially strips in a direction perpendicular to a certain direction (baseline direction), You may comprise so that a bright-point movement amount may be averaged. Here, as described above with reference to FIG. 16C, the first partial region has a substantially strip-shaped region whose short side is substantially parallel to the base line. The second partial region has a substantially strip shape, and the short side of the region is substantially perpendicular to the base line. In this case, the partial region averaging unit 29 typically corresponds to the first partial region, and the entire flat surface 102 as the target region in a direction perpendicular to a certain direction (baseline direction), for example, The second partial region is formed by dividing it into strips having a width of about 20 pixels.

  In this case, the effective partial region extraction unit 30 averages the plurality of movements for each first partial region by the partial region averaging unit 29, that is, the average value of the bright spots in each first partial region. Based on the above, the first effective partial area used for calculating the representative coordinates is extracted from the first partial area. The effective partial region extraction unit 30 further extracts a second effective partial region used for calculating the representative coordinates from the second partial region based on the average value of the bright spots in each second partial region. Composed.

  Furthermore, in this case, as shown in FIG. 18 (c), the representative coordinate calculation unit 24 measures the measurement points in the effective partial region that overlap in the first effective partial region and the second effective partial region. It is good to comprise so that a representative coordinate may be calculated. Here, for the extraction of the first and second effective partial areas and the overlapping effective partial areas by the effective partial area extracting unit 30, the average value of the bright spots in each partial area is used. In the representative coordinate calculation by the representative coordinate calculation unit 24, a value that is not averaged, that is, the bright spot movement amount is used as it is.

  With this configuration, when extracting the overlapping effective partial regions, the average value of the bright spots in each first partial region calculated by the partial region averaging unit 29 is the y-axis coordinate (see FIG. 13). ) And the effective partial region extraction unit 30 only needs to extract the first effective partial region using only the y-axis coordinates (see FIG. 13). Similarly, the average value of the bright spots in each second partial region calculated by the partial region averaging unit 29 is calculated using only the x-axis coordinates (see FIG. 13), and the effective partial region extracting unit 30 also calculates. It is sufficient to extract the second effective partial region using only the x-axis coordinates (see FIG. 13). Therefore, the calculation efficiency is improved as compared with the case where the effective partial area is extracted from the partial areas subdivided into two directions (baseline direction and direction perpendicular to the baseline). Further, since the representative coordinates calculated by the representative coordinate calculation unit 24 are obtained as two-dimensional coordinates of the x-axis and y-axis coordinates, the effective partial area is in the x-axis direction and y-axis direction in the entire measurement point group. Both can be dealt with even if they are located at biased positions, and the region dividing line 2c can be formed with an inclination with respect to the base line, for example.

  In the above modification, not only the value averaged by the partial region averaging unit 29, that is, the average value of the bright spots in each partial region, is used for calculating the representative coordinates, but also the movement amount waveform generating unit. 142 (see FIG. 10) may be used as measurement point group data for each of the chest region 2a (first region) and the abdominal region 2b (second region). In other words, the movement amount waveform generation unit 142 (see FIG. 10) uses the averaged value, that is, the average value of the bright spots in each partial region, as the measurement point group for each region divided by the region dividing line 2c. Can be used as integrated data. With this configuration, while effectively eliminating noise, the motion output related to breathing motion is not relatively reduced, and the accuracy of the integrated data, that is, each waveform data, etc., is also reduced. There is nothing to do.

  FIG. 19 is a flowchart showing an outline of processing steps by the respiratory monitor 1 according to the embodiment of the present invention. With reference to this figure, the analysis method of the state of the person 2 by the respiration monitor 1 is demonstrated. For the configuration of the respiratory monitor 1, refer to FIG. 1 or FIG. 10 as appropriate.

  First, as a measurement process, for each of a plurality of bright spots 11b (see FIG. 9) projected on the bed 3 by the movement amount calculation unit 141, a plurality of images are obtained from two frames of images acquired at different times by the imaging device 12. The amount of bright spot movement between the two frames of the bright spot 11b is calculated. The movement amount waveform generation unit 142 generates measurement data as movement amount waveform data in which the bright spot movement amounts calculated by the movement amount calculation unit 141 are arranged in time series (S1). Specifically, the movement amount waveform generation unit 142 generates the respiration data that is the movement amount waveform data of the sum of the bright spot movement amounts for each chest region 2a (first region) and abdominal region 2b (second region). Generate. The body movement data, which is the movement amount waveform data of the sum of the absolute values of the movement amounts of the bright spots, only needs to be able to detect body movement, so it is generated as total measurement data corresponding to the entire area (however, it is generated for each area) Needless to say. Data generation is performed for a certain period, for example, overnight (about 7 to 10 hours).

  Next, as the body movement period detection step, measurement data (for example, body movement data) within a ninth predetermined period (here, about 1 to 2 minutes) before and after the determination target time point, typically measurement data (for example, A body motion reference value is calculated based on the distribution state of the body motion data), the body motion of the person 2 is determined, and a body motion period in which data indicating the body motion of the person 2 exists from the measurement data, Determination and detection are performed in the entire processing range of the acquired measurement data (all determination target points in the overnight measurement data) (S3).

  Next, as a threshold calculation step, a first predetermined period (here, about 2 minutes) excluding a body movement period that is a period in which body movement is detected by the body movement detection unit 27 among periods before and after the determination target time point. Based on the measurement data, the respiratory reference value is calculated for each of the chest region 2a (first region), the abdominal region 2b (second region), and the entire region, and the person 2 at the determination target time point is further calculated. The chest threshold value, the abdominal threshold value, the first overall threshold value, and the second overall threshold value that are used to determine the state are calculated (S5). Here, the threshold value calculation unit 28 calculates each threshold value based on the measurement data of the first predetermined period excluding the body movement period.

  Next, as described above, as described above, the chest measurement data and the chest threshold at the determination target time point are compared with the abdominal measurement data and the abdominal threshold, and the chest measurement data and the whole measurement data are compared with the abdomen. The measurement data is compared with the whole measurement data, and the whole measurement data is compared with the first whole threshold and the second whole threshold, and the obstructive apnea state of the person 2 at the determination target time point ( The first state), the central apnea state (second state), the mixed apnea state (third state), and the normal breathing state (fourth state) are determined (S7).

  When the determination is completed, the determination target time point is incremented, for example, the determination target time point is moved to a time point adjacent to the rear side (S9). Here, if the new attention time point is within the processing range by this movement, for example, within a certain period (here, overnight, about 7 to 10 hours) (S11; No), the process returns to the threshold value calculation step (S5) again. Repeat the above. In addition, when the new determination target time point is outside a certain period (here, overnight, about 7 to 10 hours) (S11; Yes), the obstructive apnea state (first state), the central The name of the detected state, such as sexual apnea state (second state), obstructive apnea mixed apnea state (third state), the time of start of the state, the time of end The statistical data such as the number of times of calculation is calculated and stored in a storage unit (not shown) (S13), and the process of analyzing the state of the person 2 is terminated. In addition, if it is the structure which also discriminate | determines a hypopnea state, what is necessary is just to calculate and preserve | save including the statistical data of a hypopnea state.

  Here, each process in each unit of the arithmetic unit 20 and the above-described method can be realized as a software program that is installed and executed in a computer, and can also be used as a recording medium for recording the software program. It is feasible. The software program may be recorded and used in a program unit (not shown) built in the computer, recorded in an external storage device or CD-ROM, and read and used by the program unit (not shown). Alternatively, it may be downloaded from the Internet to a program unit (not shown) and used.

  Next, with reference to the block diagram of FIG. 20, a respiratory monitor 1a as a state analyzing apparatus according to a second embodiment of the present invention will be described. The respiration monitor 1a is basically the same as the respiration monitor 1 described in the first embodiment described with reference to FIG. 1 and the like. However, instead of the representative coordinate calculation unit 24, a movement vector integration unit 24a is used to form dividing lines. The difference is that a second dividing line forming part 25a is provided instead of the part 25. Here, the description of the common configuration of the FG sensor 10 and the arithmetic unit 20 will be omitted as much as possible.

  The arithmetic unit 20 calculates an integral value obtained by integrating the movement vector at each measurement point in a direction perpendicular to a predetermined direction, and arranges the integral value in the predetermined direction to form an integrated waveform; And a second dividing line forming part 25a for forming the area dividing line 2c so as to pass through a point where the sign of the differential value of the formed integral waveform has changed. As in the first embodiment, among the plurality of measurement points measured by the FG sensor 10, the measurement points that do not move, that is, the measurement points that do not move the bright spot are ignored, and the representative coordinates are calculated. I do.

  Here, the movement vector is a vector indicated by (amount of movement (movement amount of bright spot), direction (±)). The predetermined direction is the baseline direction of the FG sensor 10. In addition, here, the region dividing line 2c is typically a straight line that passes through a point that is perpendicular to the baseline direction and where the positive / negative of the differential value of the integrated waveform has changed. This presupposes the case where the spine direction of the person 2 on the bed 3 is substantially parallel to the center line of the bed.

  Furthermore, with reference to FIG. 21, the formation of the integrated waveform by the movement vector integration unit 24a and the formation of the region dividing line 2c by the second dividing line forming unit 25a will be specifically described. In the figure, for the purpose of explanation, the captured image, the angle of view, the position of the measurement point within the angle of view (only the measurement point that has moved), and the person 2 (shown by a broken line in the figure) are shown. However, it is better to actually calculate only with the numerical value of coordinates. Further, the integral waveform of the movement vector shows a waveform obtained by interpolating between the measurement point sequences (bright spot sequences), but may actually be a waveform formed by points plotted at the bright spot sequence intervals. Moreover, (a) shows the case where the phase of movement is reversed between the chest and abdomen. In the figure, the measurement points are neatly arranged in the vertical direction in the figure. However, even if there is a slight deviation, the calculation may be performed with the same measurement point sequence. In this case, for example, measurement points falling within a predetermined width are regarded as the same measurement point sequence. The predetermined width may be, for example, about the interval between the measurement point sequences, and more specifically, about half the interval between the measurement point sequences on both sides of the center line of the measurement point sequence.

  As shown in (a), the movement vector integration unit 24a first calculates an integration value obtained by integrating the movement vector at each measurement point in the measurement point sequence (here, the left side in the figure) at the end of the target area, that is, the angle of view. Calculate. Then, the calculated integral value is plotted as an integral value of the measurement point sequence, plotted on the coordinate axis with the vertical axis representing the integral value and the horizontal axis representing the baseline direction position. The adjacent measurement point sequence similarly calculates an integral value obtained by integrating the movement vector. Here, the sum of the integration value of the measurement point sequence calculated previously and the integration value of the measurement point sequence is calculated, and this sum is plotted as the integration value of the measurement point sequence. This is repeated until the last measurement point sequence (right side in the figure). In this way, the movement vector integrator 24a forms an integrated waveform. The second dividing line forming unit 25a forms a straight line that passes through a point that is perpendicular to the base line direction and changes in the differential value of the formed integral waveform, and this straight line is defined as a region dividing line 2c. Since the figure shows a case where there are measurement points with different movement phases between the chest and abdomen of the person 2, the region dividing line 2c is formed between the abdomen and the chest of the person 2 and the chest region 2a (first Area) and abdominal area 2b (second area). In addition, the point where the positive / negative of the differential value of the integral waveform changed is a point where the positive / negative is switched as in the example of the differential value curve of the integral waveform as shown in (b). In other words, it is the inflection point of the integrated waveform. In addition, the example of the integrated waveform of (a) has shown the case where there exists a motion of a raise at the chest of the person 2, and a fall at the abdomen.

  In addition, as shown in (c), for example, in the case of an upward movement in both the chest and abdomen, the integrated waveform only increases (indicated by a solid line in the figure), and in the case of a downward movement in both the chest and abdomen. The integral waveform only increases (indicated by a broken line in the figure). Actually, for example, when the region dividing line 2c is formed only when the polarity of the integrated waveform is changed after the differential polarity change (after positive / negative changes), or the integrated waveform is subjected to low-pass processing. Good. Further, similarly to the above-described dividing line forming unit 25, the second dividing line forming unit 25a may perform the formation of the region dividing line 2c based on the position of the region dividing line 2c formed in the past.

  As described above, in the respiratory monitor 1a according to the second embodiment, the computing device 20 computes an integral value obtained by integrating the movement vector at each measurement point in a direction perpendicular to a predetermined direction, and this integral value is calculated. A moving vector integration unit 24a that forms an integrated waveform arranged in the predetermined direction, and a second dividing line forming unit that forms a region dividing line 2c so as to pass through a point where the sign of the differential value of the formed integrated waveform changes. Since the state determination unit 26 can distinguish the movement of the person 2 in each of the chest region 2a and the abdominal region 2b of the person 2, the state determination unit 26 can accurately grasp the state of the person 2.

  Further, the respiration monitor 1a calculates an integral value obtained by integrating the movement vector at each measurement point in a predetermined direction, that is, a direction perpendicular to the base line direction of the FG sensor 10, by the movement vector integration unit 24a. Since the integrated waveforms are formed side by side in the direction, for example, an integrated waveform reflecting the amount and direction of movement of the person 2 measured at each measurement point can be formed. Further, since the area dividing line 2c is formed by the second dividing line forming unit 25a so as to pass through the point where the positive / negative of the differential value of the formed integral waveform has changed, the person can be accurately and accurately processed while being relatively simple processing. Measurement data of two chest regions 2a and abdominal regions 2b can be obtained.

  Next, with reference to the block diagram of FIG. 22, a respiratory monitor 1b as a state analyzing apparatus according to a third embodiment of the present invention will be described. The respiration monitor 1b is basically the same as the respiration monitor 1 described in the first embodiment described with reference to FIG. 1 and the like, but instead of the representative coordinate calculation unit 24 and the dividing line formation unit 25, a region formation unit 24b. It differs in that it is equipped with. Here, the description of the common configuration of the FG sensor 10 and the arithmetic unit 20 will be omitted as much as possible.

  In the respiratory monitor 1b according to the present embodiment, the computing device 20 includes a region forming unit 24b that forms a region in which the phases of a plurality of movements measured by the measuring unit 14 (see FIG. 10) are substantially the same phase. Prepare.

  The region forming unit 24b moves as a measurement result including the moving amount of each bright spot measured based on images at two different time points in the moving amount calculating unit 141 (see FIG. 10) of the measuring unit 14 (see FIG. 10). From the information, it is configured to form a region where the phases of movement of the plurality of measurement points are substantially the same phase. Here, the phase is a concept including the direction of movement, and the substantially same phase (hereinafter simply referred to as the same phase) is a concept including that the directions of movement are simply the same. Further, here, the region forming unit 24b identifies whether the phase is the same phase by whether the movement measured at each measurement point by the measurement unit 14 (see FIG. 10) is an upward movement or a downward movement. Identify by That is, an in-phase region is formed by the identified in-phase measurement points.

  Furthermore, the region forming unit 24b determines that the predetermined region is the phase of the type if the number of measurement points of the same type of movement phase is equal to or greater than a predetermined value for the measurement points in the predetermined region. It is comprised so that it may become an area | region with a motion. The predetermined value is, for example, a number (value) of about 1/5 to 1/20, preferably about 1/10 of the number of measurement points existing in the predetermined area.

  As a specific example, the area forming unit 24b first determines the phase of movement of the area to be formed. Here, the phase is first formed from the region in the upward direction. As described above, each bright point corresponds to a measurement point here. Next, paying attention to one measurement point, if the amount of movement of the bright spot of the target measurement point is equal to or greater than a predetermined value and the movement phase of the person 2 is upward, the region including the target measurement point is selected. And set as a search area as a predetermined area. Here, the size of the search area is a size including, for example, about 9 to 25 measurement points. The predetermined value is a threshold value Th3 described later.

  Of the measurement points in the search area, if the amount of movement of the bright spot is equal to or greater than the threshold Th3 and the number of measurement points whose movement phase is in the upward direction is equal to or greater than a predetermined value, the predetermined region is moved upward. This search area is labeled as an area where there is movement. Next, paying attention to one of the measurement points outside the search area, the same processing as described above is performed. This is performed until there are no measurement points of interest. In other words, for all the measurement points, the process is repeated until it is confirmed whether the moving amount of the bright spot is equal to or greater than a predetermined value and the phase of movement is upward. As a result, when there are a plurality of labeled search areas and the search areas are adjacent to each other, the adjacent search areas are connected and labeled again. As described above, when the process when the movement phase of the person 2 is in the upward direction is completed, the process when the movement phase of the person 2 is in the downward direction is performed in the same manner as in the case of the upward direction. In this way, the area forming unit 24b forms an upward movement area and a downward movement area in the person 2. That is, it is possible to form a region where the phase of movement is both upward and downward, or any one of them. In this way, it is possible to know which part on the body of the person 2 is moving upward or downward by forming an area in which the movement by the area forming unit 24b has the same phase.

  In addition, the region forming unit 24b searches for measurement points of the same type of motion phase at measurement points in a predetermined region, and based on the result of this search, the measurement is performed with the same phase of motion. A point cloud may be formed, and the measurement point cloud thus formed may be configured as a region where the phase of movement is the same phase.

  As a specific example in such a case, the area forming unit 24b first determines the phase of the movement of the area to be formed in the same manner. Here, the phase is first formed from the region in the upward direction. Next, attention is paid to one measurement point, and an area including the measurement point of interest is set as a search area as a predetermined area. Here, the size of the search area is a size including several measurement points, for example, about 4 to 9, for example. In this search area, a measurement point whose motion phase is upward is searched.

  Then, if there is a measurement point in the search area that has an upward movement phase, the same labeling as the target measurement point is performed, and the search area is set with this measurement point as the new target measurement point. To do. Then, again, a measurement point whose phase is upward is searched among the measurement points in the search area, and if there is, the same labeling is performed. In addition, the search is stopped if a measurement point whose motion phase is upward is not found in the search area. However, if there is a measurement point having a plurality of movement phases upward in the search area, the search area is set for each measurement point with this measurement point as a new measurement point of interest. By repeating the setting of the search area, the search for the measurement point whose phase is upward, and the labeling, a measurement point group of the measurement points whose movement phase is upward is formed.

  Further, labeling is performed in the same manner using a measurement point that has never been the measurement point of interest and is not included in the measurement point group as the measurement point of interest. At this time, labeling is performed so that the label can be distinguished from the previous label (for example, the name is changed). Further, the above is repeated, and when there is no measurement point that has never been a target measurement point and is not included in the formed measurement point group, the search for the measurement point whose movement phase is upward is terminated. In other words, the labeling ends. Each measurement point group formed in this way is set as a region where the phase of motion is upward. Furthermore, the measurement point group having the maximum number of measurement points included in the measurement point group formed in this way may be set as the region in which the phase of motion is upward. Further, by performing the processing when the motion phase is in the downward direction in the same manner as in the above-described case, the region having the same motion phase can be formed. That is, it is possible to form a region where the phase of movement is both upward and downward, or any one of them.

  Furthermore, the region forming unit 24b is configured to be able to calculate a boundary at which the phase of movement is reversed based on the formed region. The calculation of this boundary is configured to be performed when both the upward movement area and the downward movement area are formed in the person 2 in the above-described area formation. Hereinafter, the boundary calculation will be specifically described. Here, the boundary will be described in the case of a line. That is, the region forming unit 24b calculates the region boundary 2d (see FIG. 25).

  First, the region forming unit 24b identifies the region having the same type of motion phase, that is, the maximum number of motion measurement points in the same phase, from the formed regions by forming the region. The maximum area is identified for each phase. That is, here, the maximum regions are identified because the motion phase is upward and downward. And the average coordinate of the measurement point in the identified largest area | region is calculated. This is performed for each identified maximum area, that is, an upper phase area and a lower phase area. Next, an intermediate point of each calculated average coordinate is calculated, and this intermediate point is set as a boundary point. Here, a straight line passing through the boundary point and having a direction perpendicular to the base line direction in FIG. 9 is defined as a region boundary line 2d (see FIG. 25). This region boundary line 2d (see FIG. 25) is used as the region dividing line 2c between the chest region 2a (first region) and the abdominal region 2b (second region) of the person 2 described above with reference to FIG.

  In the present embodiment, the arithmetic unit 20 further analyzes the information including the region formed by the three-dimensional shape generation unit 31 that generates shape information that can recognize the shape of the person 2 and the region formation unit 24b. An output information generation unit 32 that generates information may be provided.

  The shape information generated by the three-dimensional shape generation unit 31 is, for example, an image representing the three-dimensional shape of the person 2, but may be an image obtained by simply capturing the person 2. Furthermore, the image which showed the outline of the external shape of the body of the person 2 may be sufficient. In the present embodiment, the shape information will be described in the case of an image showing a three-dimensional shape (hereinafter simply referred to as a three-dimensional shape). FIG. 23 shows an example of a three-dimensional shape. Note that the illustrated three-dimensional shape is an image of the image displayed on the display 40.

  Note that the three-dimensional shape generation unit 31 may generate the above-described three-dimensional shape based on the measurement result of the measurement unit 14 of the FG sensor 10 (see FIG. 10). In this case, the measurement of the moving amount of the bright spot by the measurement unit 14 (see FIG. 10), the images at two different points in time, the image at an arbitrary point (current), and the point at which the person 2 does not exist on the bed 3 To be the image of And the measurement part 14 (refer FIG. 10) outputs the said measurement result in each measurement point measured based on such an image of two different time points as height information. And the three-dimensional shape production | generation part 31 produces | generates a three-dimensional shape based on the height information which is a measurement result of the measurement part 14 (refer FIG. 10).

  The height information, which is the measurement result of the movement amount calculation unit 141 (see FIG. 10) of the measurement unit 14 (see FIG. 10), corresponds to the height of the person 2 at a plurality of measurement points. Further, the height is actually calculated from the height information. In this case, the height of the person 2 at each measurement point is calculated by the triangulation method based on the height information, that is, the moving amount of the bright spot at each measurement point. More specifically, the height from above the bed 3 is calculated.

Specifically, the height is calculated, for example, by setting the distance (baseline length) between the projection device 11 and the imaging device 12 to d, the focal length of the imaging lens 12a of the imaging device 15 to 1 (el), and the imaging lens 12a. If the distance from the bed 3 to the upper surface of the bed 3 is h and the amount of movement of the bright spot is δ, the following equation (4) can be used.

Z = (h 2 · δ) / (d · l + h · δ) (4)

  In this case, the height of the person 2 is not known because the measurement points are spaced apart from each other. For this reason, if the three-dimensional shape is generated as it is from the calculated height of the person 2 at each measurement point, the outer shape of the person 2 is difficult to understand. In order to compensate for this, the three-dimensional shape generation unit 31 performs interpolation for a portion of the person 2 whose height is insufficient. By arranging the heights of the persons 2 obtained in this way on the coordinates, for example, a three-dimensional shape as shown in FIG. 23 can be generated. In addition, when calculating the height, another CCD camera (imaging device) is installed at a position where the baseline length d is shorter, separately from the CCD camera installed for detecting respiratory motion. It is preferable to calculate using the bright spot position data obtained by the CCD camera (imaging device). Thereby, the dynamic range of measurement can be substantially increased.

  Returning to FIG. 22, the output information generation unit 32 performs analysis for displaying the region formed by the region forming unit 24b on the shape information generated by the three-dimensional shape generation unit 31, that is, the three-dimensional shape here. Information is generated. The generated analysis information is output to the display 40 and displayed on the display 40. The output information generating unit 32 generates, as analysis information, an image in which the region formed by the region forming unit 24b is superimposed on the three-dimensional shape so that each coordinate (position) corresponds.

  Here, with reference to the schematic diagram of FIG. 24, an example in which the formed region is displayed superimposed on the three-dimensional shape, in other words, an example of generated analysis information will be described. For the sake of explanation, an example of the generated analysis information is shown as an image when displayed on the display 40. As shown in the figure, the position of the region formed by the region forming unit 24b is overlapped with the three-dimensional shape described in FIG. In this way, when the formed region is superimposed on the three-dimensional shape, the phase of movement in the formed region can be identified. FIG. 24A shows a case where the abdominal region 2b (second region) of the person 2 moves downward, more specifically, a case of abdominal breathing. FIG. 24B shows a case where the chest region 2a (first region) of the person 2 is moving upward, and more specifically, a case of chest-type breathing.

  Furthermore, here, in the case where the phase of the movement is upward and downward, each phase region is filled with a different pattern, but each color may be changed (for example, the upward direction is blue, The downward direction is red). Further, for example, the phase of movement may be indicated by an arrow (indicated by a broken line in the figure). By doing in this way, it is possible to easily recognize which part on the body of the person 2 is moving upward or downward. At this time, depending on the magnitude of the change in waveform movement, if you change the pattern color intensity, change the pattern width, or change the thickness or length of the arrow, the movement will increase. The change is easy to understand. Similarly, in the case of the height change data obtained by integrating the change in movement, for example, the high part is reflected by increasing the color by reflecting the amount, or by making the arrow longer. The change in height is easy to understand.

  Furthermore, as shown in the schematic diagram of FIG. 25, the boundary between the chest region 2a (first region) and the abdominal region 2b (second region) calculated by the region forming unit 24b is also overlapped in a three-dimensional shape. Generate analysis information. The boundary is indicated by a region boundary line 2d. In this way, for example, the boundary between the chest region 2a (first region) and the abdominal region 2b (second region) of the person 2 can be easily recognized. This is effective when diagnosing the sleep apnea syndrome as described above.

  According to the respiratory monitor 1, 1a, 1b or the software program according to the embodiments of the first, second, and third inventions described above, the chest region 2a, the abdominal region 2b, and the entire region as described above. By determining the state of the person 2 accordingly, for example, it is possible to perform accurate state analysis with respect to complex respiratory movements as a whole, such as correct evaluation, detailed state analysis, and improvement in analysis accuracy. . For example, in the conventional determination of only the entire signal corresponding to the entire region of the person 2, a more detailed type determination of an apnea state that is difficult to determine, that is, an obstructive apnea state (first state), centrality An apnea state (second state) and an obstructive apnea mixed apnea state (third state) can be distinguished and distinguished. Furthermore, since the threshold value calculation unit 28 calculates each threshold value excluding the measurement data of the body movement period, it is possible to prevent the state determination unit 26 from erroneously determining the state of the person 2.

  Further, since the respiratory monitors 1, 1a, 1b use the FG sensor 10 as a measuring device, the measurement data indicating the state of the person 2 can be acquired without touching the person 2, so that the person 2 sleeps without burdening the person 2. The type of time apnea can be identified and automatically detected, and the breathing state of the person 2 can be determined more simply. Further, for example, compared with a PSG device that determines the state based on the amplitude of the flow sensor and the chest or abdominal movement detected by the chest-abdominal motion sensor, the measurement data indicating the state of the person 2 is contacted with the person 2 Therefore, there is no possibility that data in a natural state during sleep cannot be obtained or data is lost due to a sensor being detached during sleep. In addition, although it can detect the presence or absence of airflow, it is poor in quantitativeness, and it cannot accurately determine hypopnea compared to conventional PSG devices that use flow sensors and thoracoabdominal motion sensors that can detect only part of the upper body. Without the need for manual operation to attach a sensor such as a flow sensor or thoracoabdominal motion sensor to a person's body. Moreover, the load can be reduced psychologically. Furthermore, even small movements of the person 2 such as breathing can be accurately measured.

  In the above description, the respiration monitors 1, 1 a, and 1 b include the representative coordinate calculation unit 24 (see FIG. 1), the dividing line forming unit 25 (see FIG. 1), the movement vector integration unit 24 a (see FIG. 20), Two dividing line forming portions 25a (see FIG. 20) or region forming portions 24b (see FIG. 22), which separate the chest region 2a (first region) from the abdominal region 2b (second region). Although explained, it is good also as a respiration monitor without these composition. For example, when it is known that the person 2 does not move much, the chest region 2a (first region) and the abdominal region 2b (second region) are manually manually in advance via the input device 35 (see FIG. 1). May be set in advance.

It is a block diagram which shows the structural example of the respiration monitor which concerns on the 1st Embodiment of this invention. It is the schematic shown about the example of the waveform pattern which the respiration data and body movement data which are used for the respiration monitor which concerns on the 1st Embodiment of this invention form. It is a diagram which shows the example of the histogram of measurement data used for the 1st respiration monitor which concerns on embodiment of this invention. It is a figure explaining the case where a body movement period overlaps in the 1st predetermined period with the 1st respiration monitor concerning an embodiment of the invention. It is a diagram explaining the respiration data of the whole area | region used for the 1st respiration monitor which concerns on embodiment of this invention, body motion data, and a baseline. It is a figure which shows the 1st specific example of the respiratory waveform (respiration data) of obstructive apnea, an upper stage shows whole measurement data, a middle stage shows chest measurement data, and a lower stage shows abdominal measurement data. It is a figure which shows the 1st specific example of the respiratory waveform (respiration data) of central apnea, an upper stage shows whole measurement data, a middle stage shows chest measurement data, and a lower stage shows abdominal measurement data. It is a figure which shows the 2nd specific example of the respiratory waveform (respiration data) of obstructive apnea, an upper stage shows whole measurement data, a middle stage shows chest measurement data, and a lower stage shows abdominal measurement data. It is a typical perspective view of the FG sensor of the respiration monitor which concerns on the 1st Embodiment of this invention. It is a block diagram which shows schematic structure of the FG sensor of the respiration monitor which concerns on the 1st Embodiment of this invention. It is a typical perspective view explaining the projection apparatus of the respiration monitor which concerns on the 1st Embodiment of this invention. It is a conceptual perspective view explaining the concept of the movement of the bright spot of the respiratory monitor according to the first embodiment of the present invention. It is a schematic diagram explaining the bright spot imaged on the image receiving surface in the case of FIG. It is a typical top view explaining the example of calculation of the representative coordinate by the representative coordinate calculating part of the respiration monitor which concerns on the 1st Embodiment of this invention, and formation of the area dividing line by a dividing line formation part. It is a diagram explaining the point internally divided by the value weighted with the quantity regarding the representative coordinate which concerns on the 1st Embodiment of this invention. It is a typical top view explaining the specific example of the division | segmentation of the partial region by the partial region averaging part of the respiration monitor which concerns on the modification of the 1st Embodiment of this invention, (a) is a 1st specific example, (B) is a second specific example, and (c) is a third specific example. It is a typical top view explaining the example of extraction of the effective partial area | region by the effective partial area | region extraction part of the respiration monitor which concerns on the modification of the 1st Embodiment of this invention. It is a figure explaining the respiration monitor which concerns on the modification of the 1st Embodiment of this invention. It is a flowchart which shows the outline of the process process by the respiration monitor which concerns on the 1st Embodiment of this invention. It is a block diagram which shows the structural example of the respiration monitor which concerns on the 2nd Embodiment of this invention. It is a typical top view explaining the specific example of formation of the integral waveform by the movement vector integration part of the respiration monitor which concerns on the 2nd Embodiment of this invention, and formation of the area parting line by a 2nd parting line formation part. . It is a block diagram which shows the structural example of the respiration monitor which concerns on the 3rd Embodiment of this invention. It is a schematic diagram explaining the three-dimensional shape produced | generated by the three-dimensional shape production | generation part of the respiration monitor which concerns on the 3rd Embodiment of this invention. 23 shows a case where the region formed by the region forming unit is overlaid on the three-dimensional shape in the case of FIG. 23, (a) is a schematic diagram showing a case where the abdomen has a downward movement, and (b) is an upper part on the chest. It is a schematic diagram which shows the case where there exists a motion of a direction. It is a figure which shows the case where the area | region formed in the area | region formation part in the case of FIG. 23 was overlapped, and is a schematic diagram which shows the case where a boundary is calculated by the area | region formation part.

Explanation of symbols

1, 1a, 1b Respiration monitor 2 Person 2a Thoracic region 2b Abdominal region 2c Region dividing line 2d Region boundary 3 Bed 10 FG sensor 11 Projection device 11a Pattern 11b Bright spot 12 Imaging device 14 Measurement unit 20 Calculation unit 24 Representative coordinate calculation unit 24a movement vector integration unit 24b region forming unit 25 dividing line forming unit 25a second dividing line forming unit 26 state determining unit 27 body motion detecting unit 28 threshold acid calculating unit 29 partial region averaging unit 30 effective partial region extracting unit 31 tertiary Original shape generation unit 32 Output information generation unit 141 Movement amount calculation unit 142 Movement amount waveform generation unit

Claims (11)

A threshold value for calculating a threshold value for determining the state of the target object based on the measurement data of the first predetermined period before and after the determination target time point among the measurement data indicating the state of the target object having a periodic movement. Calculating means;
Comparing the first measurement data corresponding to the first region of the object or the second measurement data corresponding to a second region different from the first region of the object and the threshold value; State discriminating means for discriminating the state of the object;
The threshold value calculating means calculates the first threshold value corresponding to the first region and the second threshold value corresponding to the second region,
The state determination unit is configured such that the magnitude of the periodic movement at the determination target time point of the first measurement data is continuously lower than the first threshold for a second predetermined period, and the second measurement data If the magnitude of the periodic movement at the determination target time does not fall below the second threshold, or the magnitude of the periodic movement at the determination target time in the first measurement data If the magnitude of the periodic movement at the determination target time point of the second measurement data is continuously reduced below the second threshold for a second predetermined period without lowering the threshold of 1. Determining that the object is in the first state;
State analysis device. First measurement data corresponding to the first region of the object or a second region different from the first region of the object based on measurement data indicating a state of the object having a periodic motion A state determination unit that determines the state of the object by comparing the second measurement data corresponding to 1 and the entire measurement data corresponding to substantially the entire object;
The state determination unit is configured such that the magnitude of the periodic movement at the determination target time point of the first measurement data or the magnitude of the periodic movement at the determination target time point of the second measurement data is When the magnitude of the periodic movement at the determination target time point of measurement data is substantially equal to or exceeds the continuous period of a third predetermined period, it is determined that the object is in the first state.
State analysis device. A threshold value for calculating a threshold value for determining the state of the target object based on the measurement data of the first predetermined period before and after the determination target time point among the measurement data indicating the state of the target object having a periodic movement. Calculating means;
Comparing the first measurement data corresponding to the first region of the object or the second measurement data corresponding to a second region different from the first region of the object and the threshold value; State discriminating means for discriminating the state of the object;
The threshold value calculation means calculates the first threshold value corresponding to the first region, the second threshold value corresponding to the second region, and the third threshold value corresponding to substantially the entire object. ,
The state determination means is configured such that the magnitude of the periodic movement at the determination target time point of the entire measurement data corresponding to substantially the entire target is continuously lower than the third threshold for a fourth predetermined period. ,
The magnitude of the periodic movement of the first measurement data at the determination target time point is the first threshold value, or the magnitude of the periodic movement of the second measurement data at the determination target time is When the second threshold value is continuously increased for a fifth predetermined period, it is determined that the object is in the first state.
State analysis device. The state determination unit is configured such that the magnitude of the periodic movement of the first measurement data at the determination target time point is the first threshold value, and the periodic measurement at the determination target time point of the second measurement data. When the magnitude of the movement is continuously below the second threshold for a sixth predetermined period, it is determined that the object is in the second state.
The state analysis apparatus according to claim 1 or 3. A threshold value calculation means for calculating a threshold value for determining the state of the target object based on the measurement data of the first predetermined period before and after the determination target time point in the measurement data;
The threshold value calculating means calculates the first threshold value corresponding to the first region and the second threshold value corresponding to the second region,
The state determination unit is configured such that the magnitude of the periodic movement of the first measurement data at the determination target time point is the first threshold value, and the periodic measurement at the determination target time point of the second measurement data. When the magnitude of the movement is continuously below the second threshold for a sixth predetermined period, it is determined that the object is in the second state.
The state analysis apparatus according to claim 2. The state determination means includes a period in which the first state and the second state are substantially continuous, and a period in which the object is in the first state and a period in which the object is in the second state is a seventh state. When the object is substantially continuous for a predetermined period, it is determined that the object is in the third state.
The state analysis apparatus according to claim 4 or 5. A threshold value for calculating a threshold value for determining the state of the target object based on the measurement data of the first predetermined period before and after the determination target time point among the measurement data indicating the state of the target object having a periodic movement. Calculating means;
Comparing the first measurement data corresponding to the first region of the object or the second measurement data corresponding to a second region different from the first region of the object and the threshold value; State discriminating means for discriminating the state of the object;
The threshold value calculating means calculates the first threshold value corresponding to the first region and the second threshold value corresponding to the second region,
The state determination unit is configured such that the magnitude of the periodic movement of the first measurement data at the determination target time point is the first threshold value, and the periodic measurement at the determination target time point of the second measurement data. When the magnitude of the movement is continuously below the second threshold for a sixth predetermined period, it is determined that the object is in the second state.
State analysis device. The state discriminating unit is configured such that the magnitude of the periodic movement of the whole measurement data corresponding to substantially the entire object at the determination target time point corresponds to substantially the entire object calculated by the threshold value calculating unit. If the fourth threshold is not continuously lowered for an eighth predetermined period, it is determined that the object is in the fourth state.
The state analysis apparatus according to claim 1 or any one of claims 3 to 7. A threshold value calculation means for calculating a threshold value for determining the state of the target object based on the measurement data of the first predetermined period before and after the determination target time point in the measurement data;
The state discriminating unit is configured such that the magnitude of the periodic movement of the whole measurement data corresponding to substantially the entire object at the determination target time point corresponds to substantially the entire object calculated by the threshold value calculating unit. When the fourth threshold value is not continuously lowered for the eighth predetermined period, it is determined that the object is in the fourth state.
The state analysis apparatus according to claim 2. A measuring device for acquiring the measurement data;
The measurement apparatus includes: a projection apparatus that projects a plurality of bright spots on a target area; an imaging apparatus that images a target area on which the plurality of bright spots are projected; and two frames acquired at different times by the imaging unit. A movement amount calculating means for calculating a movement amount between the two frames of the plurality of bright spots from an image; and a movement amount waveform generating means for generating movement amount waveform data in which the movement amounts are arranged in time series.
The state analysis apparatus according to any one of claims 1 to 9. A software program installed on a computer and operating the computer as a state analysis device;
A threshold for determining the state of the object is calculated based on the measurement data of the first predetermined period before and after the determination target time point among the measurement data indicating the state of the object with periodic movement. Processing and;
Comparing the first measurement data corresponding to the first region of the object or the second measurement data corresponding to a second region different from the first region of the object and the threshold value; Controlling the computer to perform a process of determining the state of the object;
The calculation process includes a process of calculating the first threshold value according to the first area and the second threshold value according to the second area,
In the determination process, the magnitude of the periodic movement at the determination target time point of the first measurement data is continuously lower than the first threshold for a second predetermined period, and the second measurement data If the magnitude of the periodic movement at the determination target time does not fall below the second threshold, or the magnitude of the periodic movement at the determination target time in the first measurement data If the magnitude of the periodic movement at the determination target time point of the second measurement data is continuously reduced below the second threshold for a second predetermined period without lowering the threshold of 1. Including a process of determining that the object is in the first state,
Software program.