JP5540662B2 - Respiration estimation device, respiration estimation method, and respiration estimation program - Google Patents

Description

  The present invention relates to a respiratory estimation device, a respiratory estimation method, and a respiratory estimation program.

  There is an identification device that identifies breathing intervals. The identification device detects the peak of the R wave included in the electrocardiogram and calculates an RR interval that is an interval between the peaks. Then, the identification device identifies the respiratory interval by identifying the calculated period of the RR interval.

  A conventional identification device will be briefly described with reference to FIG. FIG. 20 is a diagram illustrating a conventional identification device. (1) of FIG. 20 is an example of an electrocardiogram. The horizontal axis of (1) of FIG. 20 shows a time axis, and a vertical axis | shaft shows the strength of a heartbeat signal. 901 to 903 in FIG. 20 indicate the peaks of the R wave included in the heartbeat signal. Further, reference numerals 904 and 905 in FIG. 20 indicate RR intervals. (2) in FIG. 20 is an example of a diagram in which the RR intervals in (1) in FIG. 20 are plotted. The horizontal axis in (2) of FIG. 20 indicates the time axis, and the vertical axis indicates the value of the RR interval.

  As shown in (1) of FIG. 20, the identification device detects R wave peaks 901 to 903 and calculates RR intervals 904 and 905. And as shown to (2) of FIG. 20, an identification apparatus identifies a breathing interval by identifying the period of the calculated RR interval. In the example shown in (2) of FIG. 20, the identification device identifies respiratory intervals 906 and 907 that are times corresponding to one cycle of the RR interval. Some devices estimate the internal pressure in the thoracic cavity.

JP 2007-97678 A JP 2002-355227 A

  Here, a respiration rate calculation device that calculates the respiration rate using the respiration interval calculated by the identification device described above is conceivable. For example, when the respiration interval is “3 seconds”, the respiration rate calculation device calculates “20” by dividing “60 seconds” by “3 seconds”.

  However, the respiration rate calculating device has a problem that the respiration depth is not estimated. In other words, the depth of human breathing varies from breath to breath, and the amount of air drawn at a time varies from breath to breath. For example, when a person takes a deep breath, the person breathes and exhales more air than a normal breath. Here, the respiration rate calculation device only calculates the respiration rate and cannot estimate the depth of each respiration.

  The disclosed technology has been made in view of the above, and an object thereof is to provide a respiration estimation device, a respiration estimation method, and a respiration estimation program capable of estimating the depth of respiration.

  In one aspect, the disclosed respiratory estimation apparatus includes a light emitting element that irradiates light to a human body, a change amount of light that is emitted from the light emitting element and then passes through the human body, or a rear human body that is emitted from the light emitting element. A pulse wave that detects a pulse wave of the human body based on a change amount of light detected from an optical sensor having a light receiving element that detects a change amount of light reflected by the light, and converts the pulse wave into an electric signal A detection unit is provided. Further, the respiratory estimation device performs n-order differentiation (n is a natural number) on the electrical signal converted by the pulse wave detection unit, and from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identifying unit for identifying a plurality of feature points for each pulse wave is provided. In addition, the respiration estimation device includes a difference calculation unit that calculates an amplitude change amount for each of the plurality of feature points identified by the feature point identification unit. The respiration estimation device includes an estimation unit that estimates a respiration depth based on the amount of change calculated for each feature point by the difference calculation unit.

  According to one aspect of the disclosed respiratory estimation device, there is an effect that the depth of respiration can be estimated.

FIG. 1 is a block diagram illustrating an example of the configuration of the respiratory estimation apparatus according to the first embodiment. FIG. 2 is a block diagram illustrating an example of the configuration of the respiratory estimation apparatus according to the second embodiment. FIG. 3 is a diagram illustrating an example of usage of the optical sensor in the second embodiment. FIG. 4 is a diagram illustrating an example of the structure of the optical sensor according to the second embodiment. FIG. 5 is a diagram illustrating a detailed example of the mounting substrate in the second embodiment. FIG. 6 is a diagram illustrating an example of a waveform indicated by an electrical signal converted by the optical sensor according to the second embodiment. FIG. 7 is a diagram illustrating an example of processing performed by the pulse wave interval calculation unit according to the second embodiment. FIG. 8 is a diagram illustrating an example of a secondary differential waveform in the second embodiment. FIG. 9 is a diagram for further explaining the feature point identifying process by the feature point identifying unit in the second embodiment. FIG. 10 is a diagram illustrating an example of the amplitude calculated by the difference calculation unit according to the second embodiment. FIG. 11 is a diagram illustrating an example of the amount of change in the second embodiment. FIG. 12 is a diagram illustrating an example of the amount of change calculated by the difference calculation unit according to the second embodiment. FIG. 13 is a diagram illustrating an example of an estimation process performed by the respiratory depth estimation unit according to the second embodiment. FIG. 14 is a diagram illustrating an example of a time interval identification process performed by the respiration rate calculation unit according to the second embodiment. FIG. 15 is a flowchart illustrating an example of a process flow performed by the respiratory estimation apparatus according to the second embodiment. FIG. 16 is a schematic diagram illustrating an example of an effect obtained by the respiratory estimation device according to the second embodiment. FIG. 17 is a diagram illustrating an example of the amount of change calculated by the respiratory estimation device according to the third embodiment. FIG. 18 is a diagram illustrating an example of the amount of change calculated by the respiratory estimation device according to the third embodiment. FIG. 19 is a schematic diagram illustrating an example of a computer that executes a respiratory estimation program according to the second embodiment. FIG. 20 is a diagram illustrating a conventional identification device.

  Hereinafter, embodiments of the disclosed respiratory estimation apparatus, respiratory estimation method, and respiratory estimation program will be described in detail with reference to the drawings. Note that the invention disclosed by this embodiment is not limited. Each embodiment can be appropriately combined within a range in which processing contents do not contradict each other.

  An example of the configuration of the respiratory estimation apparatus 100 according to the first embodiment will be described with reference to FIG. FIG. 1 is a block diagram illustrating an example of the configuration of the respiratory estimation apparatus according to the first embodiment. In the example illustrated in FIG. 1, the respiratory estimation apparatus 100 includes an optical sensor 101, a pulse wave detection unit 102, a feature point identification unit 103, a difference calculation unit 104, and an estimation unit 105.

  The optical sensor 101 includes a light emitting element that irradiates light to the human body and a change amount of light that is emitted from the light emitting element and then transmitted through the human body, or a change amount of light that is reflected from the human body after being emitted from the light emitting element. And a light receiving element to detect.

  The pulse wave detection unit 102 converts the amount of change in light detected from the optical sensor 101 into an electrical signal. In addition, the feature point identification unit 103 performs n-order differentiation (n is a natural number) on the electrical signal converted by the pulse wave detection unit 102, and an n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. From this, a plurality of feature points are identified for each pulse wave.

  The difference calculation unit 104 calculates an amplitude change amount for each of the plurality of feature points identified by the feature point identification unit 103. Further, the estimation unit 105 estimates the breathing depth based on the amount of change calculated for each feature point by the difference calculation unit 104. Then, the estimation unit 105 outputs an estimation result.

  Thus, according to Example 1, it is possible to estimate the depth of respiration. That is, the depth of human breathing varies from breath to breath, and the amount of air drawn at a time varies from breath to breath. For example, when a person takes a deep breath, the person breathes and exhales more air than a normal breath. Here, even if there was a respiration rate calculation device that calculates the respiration rate using the respiration interval, only the respiration rate was calculated, and the depth of each respiration could not be estimated. Therefore, the first embodiment pays attention to the fact that the feature point having the maximum amplitude variation among the feature points of the pulse wave is different between deep breathing and normal breathing. According to the first embodiment, it is possible to estimate the depth of respiration by calculating the amount of change in amplitude for each feature point.

[Configuration of respiration estimation device]
Next, the respiration estimation apparatus 200 according to the second embodiment will be described. First, an example of the configuration of the respiratory estimation apparatus 200 according to the second embodiment will be described with reference to FIG. FIG. 2 is a block diagram illustrating an example of the configuration of the respiratory estimation apparatus according to the second embodiment. In the example illustrated in FIG. 2, the respiration estimation apparatus 200 includes an optical sensor 201, an output unit 202, and a control unit 300.

  The optical sensor 201 is connected to the control unit 300. In addition, the optical sensor 201 includes a light emitting element that irradiates light to the human body and a change amount of light that has been emitted from the light emitting element and then transmitted through the human body, or a change in light that is reflected from the human body after being emitted from the light emitting element. And a light receiving element for detecting the amount.

  Here, the light emitting element corresponds to, for example, an LED (Light Emitting Diode). The light receiving element corresponds to, for example, PD (Photo Diode). The LED used as the light emitting element irradiates light having a wavelength near 760 nm, for example. In the following description, an example in which an LED used as a light emitting element emits light having a wavelength near 760 nm will be described, but the present invention is not limited to this. For example, an LED used as a light emitting element may irradiate light having a wavelength longer than 760 nm, or irradiate light having a wavelength shorter than 760 nm, and the user selects an arbitrary wavelength. good.

  An example of usage of the optical sensor 201 will be described with reference to FIG. FIG. 3 is a diagram illustrating an example of usage of the optical sensor in the second embodiment. FIG. 3 shows an example in which the respiration estimation device 200 is a mobile terminal. In the example shown in FIG. 3, the optical sensor 201 has a clip-like shape, and comes into contact with the ear when worn by the user on the earlobe. In the example illustrated in FIG. 3, the optical sensor 201 is connected to the housing of the respiratory estimation device 200 via a cable.

  In the following, unless otherwise specified, as shown in FIG. 3, the optical sensor 201 will be described as an example in which the optical sensor 201 has a clip shape and is attached to the ear. However, the present invention is not limited to this. For example, the optical sensor 201 may have a plate shape instead of a clip shape, and the user may select an arbitrary shape. Further, the optical sensor 201 may be attached to a place other than the ear, and may be attached to an arbitrary place such as a finger, for example.

  In the following, as illustrated in FIG. 3, the optical sensor 201 is described as an example in which the optical sensor 201 is connected to the housing of the respiratory estimation device 200 via a cable, but the present invention is not limited to this. Absent. For example, the optical sensor 201 may be connected to the casing of the respiration estimation device 200 via wireless, and may be in an arbitrary connection form. Similarly, the optical sensor 201 may be provided in the housing of the respiration estimation device 100.

  An example of the structure of the optical sensor 201 will be described with reference to FIG. FIG. 4 is a diagram illustrating an example of the structure of the optical sensor according to the second embodiment. FIG. 4 shows the optical sensor 201 after disassembling each part forming the optical sensor 201. As shown in FIG. 4, in the optical sensor 201, an opening control rubber 401 that is movable in a clip shape as a lid that can be opened and closed is attached to a cover case 404. In addition, a visible light cut filter 402 that cuts visible light and a mounting substrate 403 on which LEDs and PDs are mounted are sequentially stacked on the surface of the opening control rubber 401 on the cover case 404 side.

  Here, the visible light cut filter 402 blocks the visible light transmitted through the aperture control rubber 401 so that the PD mounted on the mounting substrate 403 does not detect the visible light transmitted through the aperture control rubber 401. . The LED mounted on the mounting substrate 403 corresponds to a light emitting element, and the PD mounted on the mounting substrate 403 corresponds to a light receiving element.

  In addition, a detailed example of the mounting substrate 403 will be described with reference to FIG. FIG. 5 is a diagram illustrating a detailed example of the mounting substrate in the second embodiment. In the example illustrated in FIG. 5, the case where the optical sensor 201 is the mounting substrate 403 is illustrated as an example. That is, in the example illustrated in FIG. 5, the case where the mounting substrate 403 is in contact with the earlobe 407 is illustrated as an example.

  As shown in FIG. 5, a light emitting element 405 such as an LED and a light receiving element 406 such as a PD are provided on the plane of the mounting substrate 403. In addition, the mounting substrate 403 is in contact with the earlobe 407. Here, as shown in FIG. 5, the light receiving element 406 receives the light reflected by the earlobe 407 out of the light emitted from the light emitting element 405, and detects the amount of change in the received light. Alternatively, the light receiving element 406 receives light transmitted through the earlobe 407 from the light emitted from the light emitting element 405 and detects the amount of change in the received light.

  The optical sensor 201 detects a pulse wave at the ear and converts it into an electrical signal. Here, the process in which the optical sensor 201 detects the pulse wave will be further described. First, there is a correlation between the blood flow volume at the location where the light emitting element irradiates light and the amount of light received by the light receiving element. Specifically, as the blood flow rate at the location where the light emitting element irradiates light increases, the amount of light absorbed by the blood flow at the location where the light emitting element illuminates increases, and the light receiving element receives light. The amount of light decreases. Next, when the pulse wave generated by the heartbeat arrives, the blood vessels widen, and the blood flow volume at the location where the light emitting element emits light increases. That is, the optical sensor 201 detects a change in blood flow at a location where the light emitting element emits light by detecting the amount of change in light received by the light receiving element. As a result, the optical sensor 201 detects a pulse wave by detecting a change in blood flow.

  An example of a waveform indicated by the electrical signal converted by the optical sensor 201 will be described with reference to FIG. FIG. 6 is a diagram illustrating an example of a waveform indicated by an electric signal converted by the optical sensor. The horizontal axis in FIG. 6 represents the time axis, and the vertical axis in FIG. 6 represents the amount of light received by the light receiving element. That is, the vertical axis in FIG. 6 indicates the blood flow rate at the location where the optical sensor 201 irradiates light. In FIG. 6, the portion where the blood flow rate is increased indicates that the pulse wave generated by the heartbeat has arrived.

  Returning to the description of FIG. The output unit 202 is connected to the control unit 300. The output unit 202 receives information from the control unit 300 and outputs the received information. The output unit 202 corresponds to, for example, a monitor, a display, a touch panel, or a speaker. The details of the information output by the output unit 202 will be omitted here, and will be described together with the description of each related unit.

  The control unit 300 is connected to the optical sensor 201 and the output unit 202. The control unit 300 has an internal memory that stores a program that defines various processing procedures and the like, and controls various processes. The control unit 300 is an electronic circuit such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a central processing unit (CPU), or a micro processing unit (MPU). In the example illustrated in FIG. 2, the control unit 300 includes a pulse wave interval calculation unit 301, an nth-order differentiation processing unit 302, a feature point identification unit 303, a difference calculation unit 304, a respiration depth estimation unit 305, and a respiration rate. And a calculation unit 306.

  Although not particularly mentioned below, the control unit 300 includes a storage unit. Further, the control unit 300 appropriately controls various processes by storing information in the storage unit and reading information from the storage unit. The storage unit corresponds to, for example, a semiconductor memory element such as a random access memory (RAM), a read only memory (ROM), and a flash memory.

  For example, the storage unit stores data obtained in the processing process by each unit of the control unit 300, data used in the processing process, data as a processing result, and the like. More specifically, the storage unit includes an nth-order differential waveform obtained as a result of processing by the nth-order differential processing unit 302, timings and feature points identified by the feature point identification unit 303, and changes calculated by the difference calculation unit 304. Memorize the amount. Note that details of the nth-order differential waveform, timing, feature points, amount of change, and the like will be described later, and thus description thereof is omitted here.

  The pulse wave interval calculation unit 301 calculates a pulse wave interval between two consecutive pulse waves from the waveform indicated by the electrical signal converted by the optical sensor 201. Specifically, the pulse wave interval calculation unit 301 identifies the maximum point in the waveform indicated by the electrical signal, and calculates the pulse wave interval by calculating the interval between adjacent maximum points.

  Further, the pulse wave interval calculation unit 301 identifies a place where a pulse jump has occurred using the pulse wave interval. An example of the process for identifying the place where the pulse skips will be described with reference to FIG. FIG. 7 is a diagram illustrating an example of processing performed by the pulse wave interval calculation unit according to the second embodiment. FIG. 7 shows waveforms for the optical sensor 201 and corresponds to FIG. The horizontal axis in FIG. 7 indicates the time axis, and the vertical axis in FIG. 7 indicates the blood flow rate. “Pulse wave n” (n is a natural number) in FIG. 7 indicates each pulse wave included in the waveform, and “Maximum point” in FIG. 7 indicates the maximum point included in each pulse wave. 7 indicates the pulse wave interval calculated by the pulse wave interval calculation unit 301.

  The pulse wave interval calculation unit 301 identifies a pulse skip when the pulse wave interval between two consecutive pulse waves is n times (n is a natural number) other pulse wave intervals. For example, in the example shown in FIG. 7, the pulse wave interval between two consecutive pulse waves between the pulse waves 2 to 5 has a similar value. On the other hand, the pulse wave interval between the pulse waves 1 and 2 is twice as large as the pulse wave interval between the pulse waves 2 and 5. In this case, the pulse wave interval calculation unit 301 identifies that there is a pulse jump between the pulse waves 1 and 2 and identifies that there is no pulse jump between the pulse waves 2 and 5. The processing result by the pulse wave interval calculation unit 301 is used by the feature point identification unit 303 as described later.

  The nth order differential processing unit 302 performs nth order differentiation (n is a natural number) on the waveform indicated by the electrical signal. Specifically, the nth-order differentiation processing unit 302 performs nth-order differentiation (n is a natural number) on the waveform of the optical sensor 201. In the following description, unless otherwise specified, the n-th order differentiation processing unit 302 will be described by taking an example of executing a second order differentiation, but the present invention is not limited to this. For example, the nth-order differentiation processing unit 302 may execute first-order differentiation or third-order differentiation, and the user may set an arbitrary number of differentiations.

  For example, the nth derivative processing unit 302 obtains a second derivative waveform as shown in FIG. 8 when the second derivative is executed. Here, FIG. 8 is a diagram for explaining an example of the secondary differential waveform in the second embodiment. (1) in FIG. 8 shows a waveform for the optical sensor 201 and corresponds to (1) in FIG. 6 and FIG. That is, (1) of FIG. 8 is a waveform about the optical sensor in contact with the ear. (2) in FIG. 8 is a secondary differential waveform obtained by performing secondary differentiation with respect to (1) in FIG. That is, (2) in FIG. 8 is a second-order differential waveform for the ear. The horizontal axis in FIG. 8 represents the time axis, and the vertical axis (1) in FIG. 8 represents the change in blood flow. The vertical axis | shaft of (2) of FIG. 8 shows the value obtained by the secondary differentiation. The horizontal axis of (1) in FIG. 8 corresponds to the horizontal axis of (2) in FIG.

  The feature point identifying unit 303 identifies a plurality of feature points for each pulse wave from the nth-order differential waveform that is a waveform obtained as a result of the nth-order differentiation. Specifically, the feature point identifying unit 303 identifies the timing at which the pulse wave is included in the secondary differential waveform based on the processing result by the pulse wave interval calculating unit 301. That is, the timing when the pulse does not fly is identified. Then, the feature point identification unit 303 identifies at least three feature points for each identified timing. That is, the feature point identifying unit 303 identifies at least three feature points for each pulse wave included in the secondary differential waveform.

  Hereinafter, a case where the feature point identifying unit 303 identifies four feature points for each pulse wave will be described as an example, but the present invention is not limited to this. For example, the feature point identifying unit 303 may identify three feature points for each pulse wave, may identify five feature points, or may identify an arbitrary number of feature points.

  The feature point identifying process by the feature point identifying unit 303 will be further described with reference to FIG. FIG. 9 is a diagram for further explaining the feature point identifying process by the feature point identifying unit in the second embodiment. FIG. 9 shows a second-order differential waveform for the ear and corresponds to (2) in FIG. As shown in FIG. 9, a case where there is a pulse jump between the pulse wave 1 and the pulse wave 2 will be described as an example.

  In addition, the horizontal axis in FIG. 9 represents a time axis, and the vertical axis in FIG. 9 represents a value obtained by the secondary differentiation process. “Pulse wave n” (n is a natural number) in FIG. 9 indicates each pulse wave included in the waveform, and “feature points a to d” in FIG. 9 are identified for each pulse wave by the feature point identification unit 303. Indicates feature points. In addition, as shown in “with pulse skip” in FIG. Note that the feature points a to d are also referred to as the first feature point to the fourth feature point, respectively. The first feature point is a feature point having the first appearance time among the feature points included in the pulse wave. Similarly, the second feature point to the fourth feature point indicate feature points whose appearance times are the second to fourth among the feature points included in the pulse wave, respectively.

  In the example illustrated in FIG. 9, the feature point identification unit 303 identifies that there are pulses 1 to 5 in the secondary differential waveform. In the example illustrated in FIG. 9, the feature point identification unit 303 identifies the feature points a to d for each of the pulse waves 1 to 5.

  Returning to the description of FIG. The difference calculation unit 304 calculates an amplitude change amount for each of the plurality of feature points identified by the feature point identification unit 303. Specifically, the difference calculation unit 304 calculates, for each of the plurality of feature points, the amount of change from the feature point corresponding to the feature point to be processed among the feature points in the pulse wave having the appearance time before. calculate.

  An example of the amplitude calculated by the difference calculation unit 304 will be described with reference to FIG. FIG. 10 is a diagram illustrating an example of the amplitude calculated by the difference calculation unit according to the second embodiment. FIG. 10 shows a second-order differential waveform for the ear, which corresponds to (2) of FIG. 8 and FIG. Further, the horizontal axis of FIG. 10 represents the time axis, and the vertical axis of FIG. 10 represents the value obtained by the secondary differentiation process. “Pulse wave n” (n is a natural number) in FIG. 10 indicates each pulse wave included in the waveform. Feature points a to c in FIG. 10 indicate feature points identified by the feature point identification unit 303.

  In the example illustrated in FIG. 10, the difference calculation unit 304 calculates the amplitude of the feature points a to c for each of the pulse waves 1 to 5. For example, the difference calculation unit 304 calculates the amplitude a1 for the feature point a of the pulse wave 1, calculates the amplitude b1 for the feature point b of the pulse wave 1, and calculates the amplitude c1 for the feature point c of the pulse wave 1. Similarly, the difference calculation unit 304 also calculates the amplitude for each feature point for the pulse waves 2 to 5.

  In the example illustrated in FIG. 10, the difference calculation unit 304 calculates, for each of the feature points a to c, the feature points corresponding to the feature points to be processed among the feature points in the previous pulse wave. The amount of change from the amplitude is calculated. For example, for the feature point of pulse wave 2, the amount of change from the feature point of pulse wave 1 is calculated. Similarly, the difference calculation unit 304 similarly calculates the amount of change from the feature points of pulse wave 2 to pulse wave 4 with respect to the feature points of pulse wave 3 to pulse wave 5. More specifically, when the feature point to be processed is the feature point a, the difference calculation unit 304 calculates the amount of change from the amplitude of the feature point a in the previous pulse wave. In other words, the pulse wave 3 will be described as an example. The difference calculation unit 304 calculates a change amount between the amplitude a3 and the amplitude a2 for the feature point a3, and changes between the amplitude b3 and the amplitude b2 for the feature point b3. An amount is calculated, and a change amount between the amplitude c3 and the amplitude c2 is calculated for the feature point c3.

  Here, an example of a change amount calculation method according to the second embodiment will be described with reference to FIG. FIG. 11 is a diagram illustrating an example of a change amount calculation method according to the second embodiment. In FIG. 11, the feature point a3 of the pulse wave 3 will be described as an example. FIG. 11 corresponds to the pulse wave 2 and the pulse wave 3 among the secondary differential waveforms for the ear shown in FIG. As shown by the change amount a3 in FIG. 11, the difference calculation unit 304 calculates the change amount for the feature point a3 by calculating the difference between the amplitude a3 and the amplitude a2.

  An example of the amount of change calculated by the difference calculation unit 304 will be described with reference to FIG. FIG. 12 is a diagram illustrating an example of the amount of change calculated by the difference calculation unit according to the second embodiment. (1) in FIG. 12 shows the amount of change calculated for the feature point a. That is, (1) of FIG. 12 plots the variation “a (n) −a (n−1)” (n is a natural number). Similarly, (2) of FIG. 12 shows the amount of change calculated for the feature point b, and plots the amount of change “b (n) −b (n−1)” (n is a natural number). (3) in FIG. 12 shows the amount of change calculated for the feature point c, and the amount of change “c (n) −c (n−1)” (n is a natural number) is plotted. The vertical axis in FIG. 12 indicates the magnitude of the change amount. If the amount of change is larger than 0, it indicates that the amplitude is larger than the amplitude of the feature point included in the previous pulse wave. If the amount of change is smaller than 0, the amplitude is larger than the amplitude of the feature point included in the previous pulse wave. Indicates that the amplitude has decreased. In addition, the horizontal axis of FIG. 12 indicates the time axis. The horizontal axes of (1) to (3) in FIG. 12 correspond to each other. In the example shown in FIG. 12, the envelope which connected each plot was shown collectively.

  As shown in (1) to (3) of FIG. 12, the amount of change calculated for each of the feature points a to c varies in the same cycle. That is, the amount of change is maximized for the same pulse wave, and the amount of change is minimized for the same pulse wave. In the example shown in FIG. 12, the values on the horizontal axis of the plots do not match in (1) to (3) of FIG. For example, the leftmost plot among the plots in (1) to (3) in FIG. 12 is the amount of change calculated for each feature point included in the same pulse wave, but the values on the horizontal axis do not match. This is because the feature points a to c are different in appearance time.

  In the following description, a case where the amount of change is calculated for each of the feature points a to c will be described as an example unless otherwise specified, but the present invention is not limited to this. For example, the difference calculation unit 304 may calculate the change amount for the feature point a and the feature point c and may not calculate the change amount for the feature point b. For example, the difference calculation unit 304 may calculate the change amount for the feature point a and the feature point b and may not calculate the change amount for the feature point c.

  Returning to the description of FIG. The respiration depth estimation unit 305 estimates the respiration depth based on the amount of change calculated for each feature point by the difference calculation unit 304. Specifically, the respiration depth estimation unit 305 compares the amount of change of the subsequent feature point, which is a feature point that appears later than the other feature points among the plurality of feature points, with the other feature points. Then, it is determined whether the appearance time is smaller than the change amount of the previous feature point. Here, when determining that the respiration depth is small, the respiration depth estimation unit 305 estimates that respiration is deeper than when the change amount is large. On the other hand, if it is not determined that the respiration depth is small, the respiration depth estimation unit 305 estimates that the respiration is shallow compared to the case where the change amount is small.

  That is, when the amount of change in light is detected by the ear, the magnitude relationship of the amount of change calculated for each feature point during normal breathing is “feature point c> feature point b> feature point a”. . That is, the change amount of the feature point c is larger than the change amount of the feature point b, and the change amount of the feature point b is larger than the change amount of the feature point a. Further, during deep breathing, the magnitude relationship of the amount of change calculated for each feature point is “feature point a> feature point b> feature point c”. That is, the change amount of the feature point a is larger than the change amount of the feature point b, and the change amount of the feature point b is larger than the change amount of the feature point c. Based on this, the respiration depth estimation unit 305 estimates the respiration depth. Note that the magnitude relationship between the changes is found from experimental data.

  When the amount of change in light is detected with a finger, the magnitude relationship of the amount of change calculated for each feature point during normal breathing is “feature point b> feature point c> feature point a”. . That is, the change amount of the feature point b is larger than the change amount of the feature point c, and the change amount of the feature point c is larger than the change amount of the feature point a. Further, during deep breathing, the magnitude relationship of the amount of change calculated for each feature point is “feature point a> feature point b> feature point c”. That is, the change amount of the feature point a is larger than the change amount of the feature point b, and the change amount of the feature point b is larger than the change amount of the feature point c. Based on this, the respiration depth estimation unit 305 estimates the respiration depth.

  For example, the respiration depth estimation unit 305 estimates the respiration depth using the first feature point as the front feature point and the second feature point or the third feature point as the rear feature point. That is, the respiration depth estimation unit 305 determines whether the change amount of the feature point b or the feature point c is smaller than the change amount of the feature point a. In other words, by using the first feature point as the front feature point and the second feature point or the third feature point as the rear feature point, the respiration estimation device 200 allows the place where the optical sensor 201 is attached to the ear. Even a finger can be determined using the same feature point.

  Here, the respiration depth estimation unit 305 estimates that respiration is deep when the change amount of the feature point b or the feature point c is smaller than the change amount of the feature point a. For example, the breathing depth estimation unit 305 estimates that the breathing is deep. On the other hand, when the change amount of the feature point b or the feature point c is larger than the change amount of the feature point a, the respiration depth estimation unit 305 estimates that respiration is shallow. For example, the breathing depth estimation unit 305 estimates that the breathing is normal.

  In the following, the case where the first feature point is the front feature point and the second feature point or the third feature point is the back feature point will be described as an example, but the present invention is not limited to this. Any feature point may be used. For example, when detecting the amount of light change with the ear, the second feature point may be the front feature point and the third feature point may be the rear feature point.

  An example of the estimation process performed by the respiratory depth estimation unit 305 according to the second embodiment will be described with reference to FIG. FIG. 13 is a diagram illustrating an example of an estimation process performed by the respiratory depth estimation unit according to the second embodiment. The horizontal axis in FIG. 13 indicates the number of beats, in other words, each pulse wave. The vertical axis | shaft of FIG. 13 shows the value of change amount. In the example illustrated in FIG. 13, the value on the vertical axis corresponding to the horizontal axis is “0”, and the amount of change increases as the distance from the horizontal axis increases. In addition, FIG. 13 illustrates an envelope in which the amount of change calculated by the difference calculation unit 304 is connected for each of the feature points a to c. Specifically, “Ya” in FIG. 13 indicates an envelope for the feature point a, “Yb” in FIG. 13 indicates an envelope for the feature point b, and “Yc” in FIG. 13 indicates the feature point c. The envelope of is shown. Further, reference numerals 501 to 503 in FIG. 13 denote the maximum point and the minimum point of the envelope. 504 in FIG. 13 indicates a period in which the change amount of the feature point b or the feature point c is larger than the change amount of the feature point a, and 505 in FIG. 13 indicates the change amount of the feature point b or the feature point c. A period smaller than the amount of change is shown.

  In the following description, a case where the respiration depth estimation unit 305 executes an estimation process using an envelope will be described as an example for convenience of explanation unless otherwise specified, but the present invention is not limited to this. For example, the respiration depth estimation unit 305 may execute the estimation process using the magnitude relationship of the amount of change in each pulse wave without using an envelope.

  Returning to the description of FIG. The respiration depth estimation unit 305 identifies the maximum point and the minimum point of the envelope. In the example illustrated in FIG. 13, the local maximum points 501 and 502 and the local minimum point 503 are identified. Then, the respiration depth estimation unit 305 determines whether the change amount of the feature point b or the feature point c is smaller than the change amount of the feature point a at the maximum point or the minimum point. Here, in the example shown by the maximum point 501 and the minimum point 503 in FIG. 13, the change amount of Yb and Yc is larger than the change amount of Ya, and the respiration depth estimation unit 305 determines that it is not small. As a result, the respiration depth estimation unit 305 estimates that the respiration is normal. That is, as indicated by reference numeral 504 in FIG. 13, the respiration depth estimation unit 305 estimates normal respiration for a period in which the amount of change of the feature point b or feature point c is greater than the amount of change of the feature point a. On the other hand, in the example indicated by the maximum point 502 in FIG. 13, the amount of change in Yb and Yc is smaller than the amount of change in Ya, and the respiration depth estimation unit 305 determines that it is small. As a result, the breathing depth estimation unit 305 estimates that the breathing is deep. That is, as shown by 505 in FIG. 13, the respiration depth estimation unit 305 estimates that the respiration is normal respiration for a period in which the change amount of the feature point b or the feature point c is smaller than the change amount of the feature point a.

  Returning to the description of FIG. The respiration rate calculation unit 306 identifies the time interval in which the amplitude changes with respect to the amplitude of the feature point identified by the feature point identification unit 303, and divides the time interval by a predetermined time to respiration per predetermined time. Calculate the number.

  An example of the time interval identification process performed by the respiration rate calculation unit 306 according to the second embodiment will be described with reference to FIG. FIG. 14 is a diagram illustrating an example of a time interval identification process performed by the respiration rate calculation unit according to the second embodiment. The horizontal axis in FIG. 14 represents the time axis. The vertical axis | shaft of FIG. 14 shows the amplitude of a feature point. In FIG. 14, the envelope which connected the plot which shows the amplitude of the feature point a was shown. Here, reference numerals 601 to 605 in FIG. 14 indicate time intervals between two continuous maximum points.

  In the example illustrated in FIG. 14, the respiration rate calculation unit 306 identifies the maximum point from the envelope. And the respiration rate calculation part 306 identifies the time interval between two continuous maximum points about the identified maximum point. That is, the time intervals indicated by reference numerals 601 to 605 in FIG. 14 are time intervals that are periods in which the amplitude changes. For example, the respiration rate calculation unit 306 calculates the time interval “3 seconds”. Then, the respiration rate calculation unit 306 calculates the respiration rate per predetermined time by dividing the time interval by the predetermined time. For example, when calculating the respiration rate per minute, the respiration rate “20” is calculated by dividing “60 seconds” by “3 seconds”.

  For example, the respiration rate calculation unit 306 may calculate each of the time intervals indicated by reference numerals 601 to 605 in FIG. 14, and may use an average value of the calculated time intervals as the time interval. In the example shown in FIG. 14, the case where the feature point a is used has been described as an example. However, the present invention is not limited to this, and an arbitrary feature point such as the feature point b or the feature point c is used. good. In the example shown in FIG. 14, the case where one feature point is used has been described as an example, but the present invention is not limited to this. For example, a time interval may be calculated for each of a plurality of feature points, and an average value of the calculated time intervals may be used as the time interval. In the example illustrated in FIG. 14, the case where the time interval is calculated using the maximum point is described as an example, but the calculation may be performed using the minimum point.

  The respiration rate calculation unit 306 outputs the respiration rate and the respiration depth to the user via the output unit 202. For example, the respiration rate calculation unit 306 outputs information indicating that the respiration rate is “20” and each respiration is a normal respiration or a deep respiration.

  Note that the respiration estimation device 200 may be realized using a known personal computer, workstation, server, mobile phone, PHS (Personal Handyphone System) terminal, mobile communication terminal, or PDA (Personal Digital Assistant).

  For example, the functions of the pulse wave interval calculation unit 301, the nth-order differentiation processing unit 302, the feature point identification unit 303, the difference calculation unit 304, the respiration depth estimation unit 305, and the respiration rate calculation unit 306 are included in the server. It may be realized by mounting. In this case, for example, when the server receives data from the optical sensor used by the user, the server estimates the respiration depth and the respiration rate, and outputs the estimation result to the information terminal used by the user.

  Further, for example, the state of the blood vessel may be estimated by distributing and mounting each unit illustrated in FIG. 2 in a plurality of apparatuses. For example, an information terminal (for example, a mobile phone) used by a user includes an optical sensor 201, a pulse wave interval calculation unit 301, an nth-order differentiation processing unit 302, and feature point identification among the units illustrated in FIG. The unit 303 and the difference calculation unit 304 are mounted. Moreover, you may implement | achieve by mounting the respiration depth estimation part 305 and the respiration rate calculation part 306 in a server.

  In this case, the information terminal used by the user transmits the difference data calculated by the difference calculation unit 304 to the server. When the server receives the difference data, the server estimates the respiration depth and the respiration rate based on the received difference data. Thereafter, when the estimation process ends, the server transmits the breathing depth and the breathing rate to the information terminal used by the user. Further, for example, the server may store the breathing depth and the respiratory rate in a database, and when the user accesses the server, the breathing rate and the breathing depth may be read from the database and output. In addition, although the case where the information that the information terminal used by the user transmits to the server is a difference calculated by the difference calculation unit 304, the present invention is not limited to this. For example, the information terminal used by the user may calculate the amount of change and transmit it to the server, and transmit which stage of the data executed by the pulse wave interval calculation unit 301 to the difference calculation unit 304 You may do it.

[Processing by respiration estimation device]
Next, an example of the flow of processing performed by the respiratory estimation apparatus 200 according to the second embodiment will be described with reference to FIG. FIG. 15 is a flowchart illustrating an example of a process flow performed by the respiratory estimation apparatus according to the second embodiment.

  As shown in FIG. 15, when the optical sensor 201 detects a pulse wave of the human body based on the amount of change in light (Yes at Step S101), the optical sensor 201 converts the pulse wave into an electrical signal (Step S102).

  Then, the pulse wave interval calculation unit 301 calculates the pulse wave interval for the waveform indicated by the electrical signal converted by the optical sensor 201 (step S103). That is, the pulse wave interval calculation unit 301 identifies the maximum point in the waveform indicated by the electrical signal, and calculates the pulse wave interval by calculating the interval between adjacent maximum points. Then, each pulse wave interval calculation unit 301 identifies a place where a pulse jump occurred using the pulse wave interval (step S104).

  Then, the nth-order differentiation processing unit 302 performs nth-order differentiation (n is a natural number) on the waveform indicated by the electrical signal (step S105). For example, the nth-order differentiation processing unit 302 performs second-order differentiation on the electrical signal converted by the optical sensor 201.

  Then, the feature point identifying unit 303 identifies a plurality of feature points for each pulse wave from the nth-order differential waveform that is a waveform obtained as a result of the nth-order differentiation (step S106). For example, in the example illustrated in FIG. 9, the feature point identifying unit 303 identifies feature points a to d for each pulse wave included in the secondary differential waveform for the ear.

  Then, the difference calculation unit 304 calculates an amplitude change amount for each of the plurality of feature points (step S107). That is, for example, the difference calculation unit 304 calculates the amplitude of the feature points a to c for each pulse wave. Then, the difference calculation unit 304 calculates, for each of the feature points a to c, the amount of change in the amplitude of the feature point corresponding to the feature point to be processed among the feature points in the previous pulse wave. To do. More specifically, the difference calculation unit 304 calculates the amount of change from the amplitude of the feature point a1 for the feature point a2.

  Then, the respiration depth estimation unit 305 determines whether the change amount of the rear feature point is smaller than the change amount of the previous feature point (step S108). That is, the respiration depth estimation unit 305 determines whether the change amount of the feature point b or the feature point c is smaller than the change amount of the feature point a. Here, when the change amount of the feature point b or the feature point c is smaller than the change amount of the feature point a, the respiration depth estimation unit 305 determines that the respiration depth is small (Yes in step S108) and estimates that respiration is deep. (Step S109). That is, the breathing depth estimation unit 305 estimates that the breathing is deep. On the other hand, when the change amount of the feature point b or feature point c is larger than the change amount of the feature point a, the respiration depth estimation unit 305 does not determine that the respiration depth estimation unit 305 is small (No in step S108). ), It is estimated that breathing is shallow (step S110). That is, the respiration depth estimation unit 305 estimates that the respiration is normal.

  And the respiration rate calculation part 306 estimates a respiration rate (step S111). For example, the respiration rate calculation unit 306 identifies a time interval that is a period in which the amplitude changes, and calculates the respiration rate per predetermined time by dividing the time interval by a predetermined time. Then, the respiration rate calculation unit 306 outputs the respiration rate and the respiration depth (step S112).

  For the series of processing shown in FIG. 15, the processing order may be arbitrarily changed within a range that does not contradict the processing contents. For example, a series of processing from steps S108 to S110 and step S111 may be performed in parallel.

[Effect of Example 2]
As described above, according to the second embodiment, the respiration estimation device 200 detects the pulse wave of the human body based on the change amount of the light detected from the optical sensor 201, and converts the pulse wave into an electrical signal. Then, the respiratory estimation device 200 performs n-order differentiation (n is a natural number) on the converted electrical signal, and from the n-order differential waveform, which is a waveform obtained as a result of the n-order differentiation, for each pulse wave, Identify feature points. Then, the respiration estimation device 200 calculates an amplitude change amount for each of the plurality of feature points. Then, the respiration estimation apparatus 200 estimates the respiration depth based on the amount of change calculated for each feature point. As a result, the depth of respiration can be estimated. That is, it is noted that the feature point with the maximum amplitude change amount among the feature points of the pulse wave is different between the deep breathing time and the normal breathing time, and the respiration estimation apparatus 200 determines the amplitude change amount as the feature point. By calculating each time, it is possible to estimate the depth of respiration.

  Further, according to the second embodiment, the respiration estimation apparatus 200 determines, for each of a plurality of feature points, the amplitude of the feature point corresponding to the feature point to be processed among the feature points in the pulse wave having the appearance time before. The amount of change is calculated. Then, when the change amount of the rear feature point is smaller than the change amount of the previous feature point, the respiration estimation apparatus 200 estimates that the respiration is deeper than the case where the change amount is large, and the change amount of the rear feature point Is larger than the change amount of the previous feature point, it is estimated that the respiration is shallower than the case where the change amount is small. As a result, it is possible to estimate whether respiration is deep or shallow.

  Further, according to the second embodiment, the respiration estimation device 200 performs second order differentiation on the electrical signal, and at least for each pulse wave included in the second order differential waveform that is a waveform obtained as a result of the second order differentiation. Three feature points are identified. Then, the respiratory estimation apparatus 200 has the first feature point whose appearance time is the first from among the at least three feature points and the second feature point whose appearance time is the second from the front or the appearance time is 3 from the front. The amount of change is calculated for the third feature point that is the second. The respiration estimation apparatus 200 estimates the respiration depth using the first feature point as the front feature point and the second or third feature point as the rear feature point. As a result, since the depth of respiration is estimated using feature points that are considered to be strongly influenced by the amount of change in pressure in the chest cavity, the depth of respiration can be accurately estimated. In other words, when deep breathing is performed, the effect of the amount of change in pressure in the chest cavity is strongly generated at the previous feature point. Based on this, according to the second embodiment, since the depth of respiration is estimated using the first feature point and the second feature point or the third feature point, it is possible to accurately estimate the respiration depth. .

  Further, according to the second embodiment, the respiration estimation device 200 calculates a time interval that is a period in which the amplitude changes with respect to the amplitude of the feature point, and divides the time interval by a predetermined time to thereby calculate the respiration rate per predetermined time. Is calculated. As a result, the respiration rate can be calculated without being affected by body motion noise.

  An example of the effect of the respiratory estimation device 200 according to the second embodiment will be described with reference to FIG. FIG. 16 is a schematic diagram illustrating an example of an effect obtained by the respiratory estimation device according to the second embodiment. The horizontal axis in FIG. 16 represents the time axis. The vertical axis in FIG. 16 indicates the amplitude value. Reference numeral 701 in FIG. 16 indicates an envelope obtained by connecting plots indicating the amplitudes of the change amounts of light. 702 in FIG. 16 indicates an envelope obtained by connecting plots indicating the amplitude of the feature point a. Further, a case where there is body motion noise in 703 in FIG. 16 will be described as an example. That is, the case where the user whose respiratory rate is measured has moved the body in 703 of FIG. 16 will be described as an example.

  As indicated by reference numeral 701 in FIG. 16, the amplitude of the amount of change in light changes greatly when there is body motion noise. As a result, with the method of calculating the respiratory rate by calculating the time interval from the amplitude of the change amount of light, it is considered that the respiratory rate cannot be accurately calculated for a period with body motion noise. On the other hand, as indicated by 702 in FIG. 16, the amplitude of the feature point does not change even if there is body motion noise. As a result, by calculating the respiration rate using the amplitude of the feature point of the nth-order differential waveform, the respiration rate can be calculated without being affected by body motion noise.

  In the above-described embodiment, when the amount of change is calculated, the amount of change between the respiratory estimation device and the amplitude of the feature point corresponding to the feature point to be processed among the feature points in the pulse wave with the appearance time before. The case of calculating is described as an example. In the third embodiment, a case will be described in which the respiration estimation device calculates a change amount by calculating a difference from the amplitude of another feature point in the same pulse wave.

  Here, the same pulse wave corresponds to a pulse wave including a feature point to be processed. Further, the other feature points in the same pulse wave correspond to feature points other than the feature point to be processed among a plurality of feature points included in the pulse wave. For example, the case where the pulse wave 1 includes feature points a1 to d1, the pulse wave 2 includes feature points a2 to d2, and the feature point a1 is a feature point to be processed will be described. In this case, the same pulse wave corresponds to the pulse wave 1 including the feature point a1. Further, other feature points in the same pulse wave correspond to the feature points b1 to d1 among the feature points a1 to d1 included in the pulse wave 1.

  An example of the amount of change calculated by the respiratory estimation apparatus according to the third embodiment will be described with reference to FIG. FIG. 17 is a diagram illustrating an example of the amount of change calculated by the respiration estimation device. FIG. 17 is a second-order differential waveform for the ear. Moreover, the horizontal axis of FIG. 17 shows a time axis. The vertical axis | shaft of FIG. 17 shows the value obtained by the secondary differentiation. In the example illustrated in FIG. 17, a case where the amount of change is calculated for the feature point a and the feature point c will be described as an example.

  As illustrated in FIG. 17, the respiratory estimation apparatus according to the third embodiment calculates the amount of change in amplitude when the feature point a and the feature point c are based on the amplitude of another feature point in the same pulse wave. To do. In the example shown in FIG. 17, the case where the feature point b in the same pulse wave is used as a reference is shown as an example. That is, as indicated by reference numeral 801 in FIG. 17, the respiration estimation device calculates the change amount for the feature point a1 by calculating the difference between the amplitude of the feature point a1 and the amplitude of the feature point b1. Further, as indicated by reference numeral 802 in FIG. 17, the amount of change is calculated for the feature point c1 by calculating the difference between the amplitude of the feature point c1 and the amplitude of the feature point b1. Similarly, as indicated by reference numerals 803 to 806 in FIG. 17, the respiration estimation apparatus also calculates feature points a2 and c2, a3 and c3.

  In the example shown in FIG. 17, the case where the change amount of the feature point a and the feature point c is calculated using the feature point b has been described as an example, but the present invention is not limited to this. For example, the respiration estimation device may use any one of the feature points a to d and other feature points, and may use any feature point. Note that the feature point d is not shown in FIG. In the example illustrated in FIG. 17, the case where the amount of change is calculated for the feature point a and the feature point c has been described as an example, but the present invention is not limited to this. For example, the respiration estimation device may calculate a change amount for the feature point a and the feature point b, may calculate a change amount for the feature point b and the feature point c, and may change the change amount for an arbitrary feature point. May be calculated.

  An example of the amount of change calculated by the respiratory estimation device will be described with reference to FIG. FIG. 18 is a diagram illustrating an example of the amount of change calculated by the respiration estimation device. FIG. 18 shows an example in which the amount of change is calculated for feature point b and feature point c using feature point a as a reference. The vertical axis in FIG. 18 indicates the magnitude of the amount of change. The horizontal axis in FIG. 18 represents the time axis. Reference numeral 807 in FIG. 18 indicates a change amount of the feature point b calculated using the feature point a. Further, reference numeral 808 in FIG. 18 indicates a change amount of the feature point c calculated using the feature point a. In FIG. 18, reference numeral 809 denotes a portion where the change amount of the feature point c is smaller than the change amount of the feature point a.

  And the respiration estimation apparatus which concerns on Example 3 will estimate the depth of respiration like Example 2, if a variation | change_quantity is calculated. That is, the respiration estimation device estimates the respiration depth by determining whether the change amount of the rear feature point is larger than the change amount of the previous feature point. That is, the respiration depth is estimated by determining whether or not the change amount of the feature point c is smaller than the change amount of the feature point b. In the example shown in FIG. 18, it is determined that the change amount of the feature point c is smaller than the change amount of the feature point a in the portion 809 in FIG. As a result, the respiration estimation device determines that respiration is deep for a period corresponding to 807 in FIG.

[Effect of Example 3]
In this way, the difference with the amplitude of other feature points in the same pulse wave is calculated, and the difference calculated for the feature point corresponding to the feature point to be processed among the feature points in the previous pulse wave Even if the amount of change is calculated, the depth of respiration can be estimated.

  Although the first and second embodiments of the present invention have been described so far, the present invention may be implemented in other embodiments in addition to the first and second embodiments described above. Therefore, other embodiments will be described below.

[Output contents]
For example, in the above-described embodiment, the case where the respiration estimation device outputs the respiration rate and the respiration depth as an estimation result has been described as an example, but the present invention is not limited to this. For example, the respiration estimation device may output the respiration rate while not outputting the respiration depth. Further, for example, the respiration estimation device may output the respiration depth while not outputting the respiration rate. Further, for example, when the respiration rate is not output, the respiration depth estimation unit 305 among the units illustrated in FIG. 2 may not be provided. Further, for example, when the breathing depth is not output, the breathing rate calculation unit 306 may not be provided among the units illustrated in FIG.

[Optical sensor mounting position]
For example, in the above-described embodiment, the case where the optical sensor 201 detects the amount of change of light with the earlobe has been described as an example, but the present invention is not limited to this. For example, the optical sensor 201 may detect the amount of change in light from a finger, or may detect the amount of change in light at an arbitrary position. When detecting the amount of change of light from the finger, the magnitude relationship of the amount of change calculated for each feature point is “feature point b <feature point c <feature point a” during normal breathing. Further, during deep breathing, the magnitude relationship of the amount of change calculated for each feature point is “feature point a <feature point b <feature point c”. Based on this, the respiration depth estimation unit 305 estimates the respiration depth.

[System configuration]
In addition, among the processes described in the present embodiment, all or part of the processes described as being automatically performed can be manually performed. For example, in the above-described embodiment, the case where the respiration estimation apparatus 200 identifies the local maximum point and the local minimum point has been described as an example. However, the present invention is not limited to this, and the user can use the local maximum point or local minimum point. May be set manually.

  In addition, the processing procedures, control procedures, specific names, and information including various data and parameters (FIGS. 1 to 18) shown in the document and drawings are arbitrarily changed unless otherwise specified. be able to. For example, the respiration estimation device may not include the respiration depth estimation unit 305 in the configuration illustrated in FIG. That is, the respiration estimation device may calculate the respiration rate without estimating the respiration depth. Further, for example, the respiratory estimation device may not include the respiratory rate calculation unit 306 in the configuration illustrated in FIG. That is, the respiration estimation device may estimate the respiration depth without calculating the respiration rate. With respect to the series of processes shown in FIG. 15, the order of the processes may be arbitrarily changed within a range that does not contradict the process contents. For example, a series of processing from steps S108 to S110 and step S111 may be performed in parallel.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or a part of the distribution / integration may be functionally or physically distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. For example, the optical sensor 201 and the output unit 202 may be connected as an external device of the respiration estimation device 200 via a network.

[Computer]
The various processes described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer or a workstation. Therefore, in the following, an example of a computer that executes a respiration estimation program having the same function as in the above embodiment will be described with reference to FIG. FIG. 19 is a schematic diagram illustrating an example of a computer that executes a respiratory estimation program according to the second embodiment.

  As illustrated in FIG. 19, the computer 3000 according to the second embodiment includes an operation unit 3001, a microphone 3002, a speaker 3003, a display 3004, an optical sensor 3005, a communication unit 3006, a CPU 3010, and a ROM 3011. The computer 3000 has a RAM (Random Access Memory) 3013. The computer 3000 includes an operation unit 3001 to a RAM 3013 connected by a bus 3009 or the like.

  The ROM 3011 stores a control program that exhibits the same functions as those of the pulse wave interval calculation unit 301 to the respiration rate calculation unit 3066 described in the second embodiment. That is, in the example shown in FIG. 19, the pulse wave interval calculation program 3011a, the nth-order differentiation processing program 3011b, and the feature point identification program 3011c are stored in the ROM 3011 in advance. The ROM 3011 stores in advance a difference calculation program 3011d, a respiration depth estimation program 3011e, and a respiration rate calculation program 3011f. In addition, about these programs 3011a-3011f, you may integrate or isolate | separate suitably like each component of the respiration estimation apparatus 200 shown in FIG.

  Here, the pulse wave interval calculation program 3011 a is a control program that exhibits the same function as the pulse wave interval calculation unit 402. Similarly, the n-th order differential processing program 3011b to the respiration rate calculation program 3011f are control programs that exhibit the same functions as the n-th order differential processing unit 302 to the respiration rate calculation unit 306, respectively.

  The CPU 3010 reads out the programs 3011a to 3011b from the ROM 3011 and executes them, thereby functioning as a pulse wave interval calculation process 3010a and an nth-order differentiation process 3010b. Similarly, the CPU 3010 functions as an nth-order differentiation process 3011c to a respiration rate calculation process 3011f by reading and executing the feature point identification program 3011c to the respiration rate calculation program 3011f from the ROM 3011.

  Each of the processes 3010a to 3010f includes a pulse wave interval calculation unit 301, an nth-order differentiation processing unit 302, a feature point identification unit 303, a difference calculation unit 304, and a respiration depth estimation unit 305 illustrated in FIG. , Respectively corresponding to the respiration rate calculation unit 306.

  The CPU 3010 executes the respiration estimation program using the light change amount data 3013a, the electrical signal waveform data 3013b, the second-order differential wave meter data 3013c, and the change amount data 3013d.

[Others]
The respiration estimation program described in the present embodiment can be distributed via a network such as the Internet. The respiration estimation program can also be executed by being recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, and a DVD, and being read from the recording medium by the computer.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Appendix 1) A light emitting element that irradiates light to a human body and a change amount of light emitted from the light emitting element and transmitted through the human body, or a change amount of light reflected by the human body after being emitted from the light emitting element. A pulse wave detection unit that detects a pulse wave of the human body based on a change amount of light detected from an optical sensor having a light receiving element for detecting the pulse wave, and converts the pulse wave into an electrical signal;
An nth-order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection unit, and a plurality of pulse waves are obtained for each pulse wave from an n-order differentiation waveform that is a waveform obtained as a result of the nth-order differentiation. A feature point identification unit for identifying feature points;
A difference calculation unit that calculates an amount of change in amplitude for each of a plurality of feature points identified by the feature point identification unit;
A respiration estimation device comprising: an estimation unit that estimates a respiration depth based on a change amount calculated for each feature point by the difference calculation unit.

(Additional remark 2) The said difference calculation part calculates the variation | change_quantity with respect to the amplitude of the feature point corresponding to the feature point used as a process target among the feature points in the pulse wave before appearance time for every some feature point. ,
The estimation unit compares the amount of change calculated for a subsequent feature point, which is a feature point having an appearance time later than another feature point, among a plurality of feature points with another feature point. If the amount of change is smaller than the amount of change calculated for the previous feature point before the appearance time, it is estimated that breathing is deeper than the amount of change, and the amount of change calculated for the rear feature point is The respiration estimation apparatus according to appendix 1, wherein when the amount of change is greater than the amount of change calculated for the feature point, it is estimated that respiration is shallower than when the amount of change is small.

(Additional remark 3) The said difference calculation part calculates variation | change_quantity by calculating the difference with the amplitude of the other feature point in the same pulse wave for every some feature point,
The estimation unit compares the amount of change calculated for a subsequent feature point, which is a feature point having an appearance time later than another feature point, among a plurality of feature points with another feature point. If the amount of change is smaller than the amount of change calculated for the previous feature point before the appearance time, it is estimated that breathing is deeper than the amount of change, and the amount of change calculated for the rear feature point is The respiration estimation apparatus according to appendix 1, wherein when the amount of change is greater than the amount of change calculated for the feature point, it is estimated that respiration is shallower than when the amount of change is small.

(Additional remark 4) The said feature point identification part performs a secondary differentiation with respect to the electric signal converted by the said pulse wave detection part, and is contained in the secondary differential waveform which is the said waveform obtained as a result of the secondary differentiation Identify at least three feature points for each pulse wave
The difference calculation unit is the first feature point whose appearance time is the first from among the at least three feature points and the second feature point whose appearance time is the second from the front or the third feature point from which the appearance time is the third. The amount of change is calculated for the third feature point
The respiration estimation according to appendix 2 or 3, wherein the estimation unit estimates the respiration depth using the first feature point as a front feature point and the second feature point or the third feature point as a rear feature point. apparatus.

(Additional remark 5) The said feature point identification part performs a secondary differentiation with respect to the electric signal converted by the said pulse wave detection part, identifies a feature point for every pulse wave contained in a secondary differential waveform,
For the amplitude of the feature point identified by the feature point identification unit, a respiration is calculated by calculating a time interval that is a period in which the amplitude changes and dividing the time interval by a predetermined time. The respiratory estimation device according to any one of supplementary notes 1 to 4, further comprising a number calculation unit.

(Appendix 6) A method executed by an information processing apparatus,
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection step of detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electric signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection step, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identifying step for identifying feature points;
A difference calculating step for calculating an amount of change in amplitude for each of a plurality of feature points identified by the feature point identifying step;
A respiration estimation method comprising: an estimation step of estimating a respiration depth based on a change amount calculated for each feature point by the difference calculation step.

(Supplementary note 7)
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection procedure for detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electrical signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection procedure, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identification procedure for identifying feature points;
A difference calculation procedure for calculating an amount of change in amplitude for each of a plurality of feature points identified by the feature point identification procedure;
A respiration estimation program that executes an estimation procedure for estimating a respiration depth based on a change amount calculated for each feature point by the difference calculation procedure.

(Appendix 8) A light emitting element that irradiates light to the human body, and a change amount of light emitted from the light emitting element and transmitted through the human body, or a change amount of light reflected by the human body after being emitted from the light emitting element. A pulse wave detection unit that detects a pulse wave of the human body based on a change amount of light detected from an optical sensor having a light receiving element for detecting the pulse wave, and converts the pulse wave into an electrical signal;
An nth-order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection unit, and a plurality of pulse waves are obtained for each pulse wave from an n-order differentiation waveform that is a waveform obtained as a result of the nth-order differentiation. A feature point identification unit for identifying feature points;
For the amplitude of the feature point identified by the feature point identification unit, a respiration is calculated by calculating a time interval that is a period in which the amplitude changes and dividing the time interval by a predetermined time. A respiratory estimation device comprising: a number calculation unit.

(Supplementary note 9) A method executed by an information processing apparatus,
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection step of detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electric signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection step, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identifying step for identifying feature points;
For the amplitude of the feature point identified by the feature point identification step, a respiration for calculating a respiration rate per predetermined time by calculating a time interval having a period in which the amplitude changes and dividing the time interval by a predetermined time A respiration estimation method comprising: a number calculation step.

(Supplementary note 10)
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection procedure for detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electrical signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection procedure, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identification procedure for identifying feature points;
For the amplitude of the feature point identified by the feature point identification procedure, the respiration rate is calculated by calculating a time interval that is a cycle in which the amplitude changes and dividing the time interval by a predetermined time. A respiration estimation program characterized by causing a number calculation procedure to be executed.

DESCRIPTION OF SYMBOLS 100 Respiration estimation apparatus 101 Optical sensor 102 Pulse wave detection part 103 Feature point identification part 104 Difference calculation part 105 Estimation part 200 Respiration estimation apparatus 201 Optical sensor 202 Output part 300 Control part 301 Pulse wave interval calculation part 302 nth-order differentiation process part 303 Feature point identification unit 304 Difference calculation unit 305 Respiration depth estimation unit 306 Respiration rate calculation unit 401 Aperture control rubber 402 Visible light cut filter 403 Mounting substrate 404 Cover case 405 Light emitting element 406 Light receiving element

Claims (7)

A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection unit that detects a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converts the pulse wave into an electrical signal;
An nth-order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection unit, and a plurality of pulse waves are obtained for each pulse wave from an n-order differentiation waveform that is a waveform obtained as a result of the nth-order differentiation. A feature point identification unit for identifying feature points;
For each of the plurality of feature points identified by the feature point identification unit, a difference calculation that calculates the amount of change in the amplitude of the feature point corresponding to the feature point to be processed among the feature points in the pulse wave with the appearance time before. And
The amount of change calculated by the difference calculation unit for a subsequent feature point that is a feature point that appears later than another feature point among a plurality of feature points is compared with another feature point among the feature points. If the change amount is smaller than the change amount calculated by the difference calculation unit for the previous feature point with the appearance time before, it is estimated that the respiration is deeper than the case where the change amount is large, and is calculated for the rear feature point. And a estimator that estimates that the respiration is shallower than when the change is small when the change is greater than the change calculated for the previous feature point .   A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection unit that detects a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converts the pulse wave into an electrical signal;
  An nth-order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection unit, and a plurality of pulse waves are obtained for each pulse wave from an n-order differentiation waveform that is a waveform obtained as a result of the nth-order differentiation. A feature point identification unit for identifying feature points;
  For each of a plurality of feature points identified by the feature point identification unit, a difference calculation unit that calculates the amount of change in amplitude by calculating a difference with the amplitude of another feature point in the same pulse wave;
  The amount of change calculated by the difference calculation unit for a subsequent feature point that is a feature point that has an appearance time later than other feature points among a plurality of feature points is compared with the subsequent feature point among the feature points. If the change amount is smaller than the change amount calculated by the difference calculation unit for the previous feature point with the appearance time before, it is estimated that the respiration is deeper than the case where the change amount is large, and is calculated for the rear feature point. An estimator that estimates that breathing is shallower than when the amount of change is smaller than the amount of change calculated for the previous feature point.
  A respiratory estimation device comprising:   A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection unit that detects a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converts the pulse wave into an electrical signal;
  Secondary differentiation is performed on the electrical signal converted by the pulse wave detector, and at least three characteristic points are obtained for each pulse wave included in the secondary differential waveform that is the waveform obtained as a result of the secondary differentiation. A feature point identification unit for identifying;
  Of the at least three feature points identified by the feature point identification unit, the first feature point whose appearance time is the first from the front and the second feature point whose appearance time is the second from the front or the appearance time from the front A difference calculation unit for calculating the amount of change for the third feature point that is third;
  The first feature point is the previous feature point, the second feature point or the third feature point is the rear feature point, and the amount of change calculated by the difference calculation unit for the rear feature point is the difference calculation for the previous feature point. If the amount of change is smaller than the amount of change calculated by the unit, it is estimated that respiration is deeper than when the amount of change is large, and the amount of change calculated for the rear feature point is the change calculated for the front feature point. An estimator that estimates that breathing is shallower than when the amount of change is small.
  A respiratory estimation device comprising: A method executed by an information processing apparatus,
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection step of detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electric signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection step, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identifying step for identifying feature points;
For each of a plurality of feature points identified in the feature point identification step, a difference calculation that calculates an amount of change in the amplitude of the feature point corresponding to the feature point to be processed among the feature points in the pulse wave having the appearance time before. Steps,
The amount of change calculated by the difference calculation step for the later feature point that is the feature point whose appearance time is later than the other feature points among the plurality of feature points is compared with the other feature points of the feature points. If the amount of change is smaller than the amount of change calculated by the difference calculation step for the previous feature point before the appearance time, it is estimated that the respiration is deeper than the case of the large amount of change, and the calculation is performed for the rear feature point. An estimated step of estimating that the amount of change is greater than the amount of change calculated for the previous feature point and that the respiration is shallower than when the amount of change is small. Method. A method executed by an information processing apparatus,
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection step of detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electric signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection step, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identifying step for identifying feature points;
For each of a plurality of feature points identified by the feature point identification step, a difference calculation step of calculating a change in amplitude by calculating a difference with the amplitude of other feature points in the same pulse wave;
The amount of change calculated by the difference calculation step for the subsequent feature point that is the feature point that has an appearance time later than the other feature points among the plurality of feature points is compared with the subsequent feature point among the feature points. If the amount of change is smaller than the amount of change calculated by the difference calculation step for the previous feature point before the appearance time, it is estimated that the respiration is deeper than the case of the large amount of change, and the calculation is performed for the rear feature point. An estimation step for estimating that breathing is shallower than when the amount of change is larger than the amount of change calculated for the previous feature point,
Respiration estimation method characterized by including. In the information processing device,
A light emitting element that irradiates light to the human body, and a light receiving element that detects the amount of change in light transmitted through the human body after being emitted from the light emitting element, or the amount of change in light reflected by the human body after being emitted from the light emitting element. A pulse wave detection procedure for detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electrical signal;
An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection procedure, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identification procedure for identifying feature points;
For each of a plurality of feature points identified by the feature point identification procedure, a difference calculation that calculates the amount of change in the amplitude of the feature point corresponding to the feature point to be processed among the feature points in the pulse wave with the appearance time before. Procedure and
The amount of change calculated by the difference calculation procedure for the rear feature point that is the feature point whose appearance time is later than the other feature points among the plurality of previous feature points is compared with the other feature points of the feature points. If the change amount is smaller than the change amount calculated by the difference calculation procedure for the previous feature point before the appearance time, it is estimated that the respiration is deeper than the case where the change amount is large. A respiration characterized in that when the calculated amount of change is larger than the amount of change calculated for the previous feature point, an estimation procedure for estimating that the respiration is shallower than when the amount of change is small is performed. Estimation program.   In the information processing device,
  A light emitting element that irradiates light to the human body, and a light receiving element that detects a change amount of light transmitted through the human body after being emitted from the light emitting element, or a change amount of light reflected by the human body after being emitted from the light emitting element. A pulse wave detection procedure for detecting a pulse wave of the human body based on a change amount of light detected from an optical sensor having an element, and converting the pulse wave into an electrical signal;
  An n-th order differentiation (n is a natural number) is performed on the electrical signal converted by the pulse wave detection procedure, and a plurality of pulse waves are obtained for each pulse wave from the n-order differentiation waveform that is a waveform obtained as a result of the n-order differentiation. A feature point identification procedure for identifying feature points;
  For each of the plurality of feature points identified by the feature point identification procedure, a difference calculation procedure for calculating the amount of change in amplitude by calculating a difference from the amplitude of other feature points in the same pulse wave;
  The amount of change calculated by the difference calculation procedure for the subsequent feature point that is the feature point that appears later than the other feature points among the plurality of feature points is compared with the subsequent feature point among the feature points. If the change amount is smaller than the change amount calculated by the difference calculation procedure with respect to the previous feature point before the appearance time, it is estimated that the respiration is deeper than the case where the change amount is large, and the calculation is performed for the rear feature point. An estimated procedure for estimating that breathing is shallower than when the amount of change is greater than the amount of change calculated for the previous feature point, compared to when the amount of change is small;
  A respiration estimation program characterized in that

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