JP2019155071A - Event prediction system, sensor signal processing system, event prediction method, and program - Google Patents

Abstract

To provide an event prediction system, a sensor signal processing system, an event prediction method and a program that are capable of determining symptoms of an onset of a particular event related to a subject.SOLUTION: An event prediction system 10 includes an acquisition unit 13 and a symptom determination unit 121. The acquisition unit 13 acquires body movement data about a subject's body movement from a measuring device 2 that outputs the body movement data. The symptom determination unit 121 determines whether or not there are any symptoms of an onset of a particular event related to the subject on the basis of the body movement data acquired in a past reference period.SELECTED DRAWING: Figure 1

Description

  The present disclosure relates generally to an event prediction system, a sensor signal processing system, an event prediction method, and a program, and more specifically, an event prediction system, a sensor signal processing system, an event prediction method, and a method using body motion data related to a subject's body motion Regarding the program.

  The non-contact activity amount sensor (sensor processing system) described in Patent Literature 1 includes a Doppler sensor (measurement unit), a distance sensor, and a processor. Based on one or both of the amplitude and frequency of the detection signal of the Doppler sensor and the detection signal of the distance sensor, the processor calculates the amount of activity of the target included in the sensing range (air-conditioned space) of each sensor. Patent Document 1 describes that air conditioning control is performed based on the calculated amount of activity.

  Patent Document 1 also describes estimating the user's health based on the detected amount of activity. For example, the user's health condition such as poor physical condition or injury is grasped based on the change in the amount of activity of the user.

Japanese Unexamined Patent Publication No. 2017-484

  However, in Patent Document 1, since air conditioning control is a basic function, it is only necessary to be able to grasp the current state of the user, and it is assumed that the detected amount of activity is used for other estimations. Not.

  An object of the present disclosure is to provide an event prediction system, a sensor signal processing system, an event prediction method, and a program capable of determining up to a sign of occurrence of a specific event related to a subject.

  An event prediction system according to an aspect of the present disclosure includes an acquisition unit and a sign determination unit. The acquisition unit acquires the body motion data from a measuring device that outputs body motion data related to the body motion of the subject. The sign determination unit determines a sign of occurrence of a specific event related to the subject based on the body motion data acquired in a past reference period.

  A sensor signal processing system according to an aspect of the present disclosure includes an acquisition unit and an acceleration calculation unit. The acquisition unit acquires body motion data from the measurement device. The measurement device outputs the body motion data related to the body motion of the subject. The said acceleration calculating part calculates | requires the acceleration regarding the motion of the said subject's body based on the said body motion data.

  An event prediction method according to an aspect of the present disclosure acquires the body motion data from a measurement device that outputs body motion data related to a subject's body motion, and based on the body motion data acquired in a past reference period And predicting the occurrence of a specific event related to the subject.

  A program according to an aspect of the present disclosure is a program for causing a computer system to execute the event prediction method.

  According to the present disclosure, there is an advantage that it is possible to provide an event prediction system, a sensor signal processing system, an event prediction method, and a program capable of determining up to a sign of occurrence of a specific event related to a subject.

FIG. 1 is a block diagram illustrating configurations of a sensor signal processing system and an event prediction system according to Embodiment 1 of the present disclosure. FIG. 2 is an explanatory diagram of a facility where the event prediction system is used. FIG. 3 is a flowchart showing the operation of the event prediction system described above. FIG. 4 is a flowchart showing a specific example of acceleration calculation processing in the event prediction system same as above. FIG. 5 is a flowchart showing a specific example of the sign determination process in the event prediction system same as above. FIG. 6 is a graph showing an example of body motion data to which the above event prediction system is applied.

(Embodiment 1)
(1) Overview An overview of the sensor signal processing system 1 and the event prediction system 10 according to the present embodiment will be described with reference to FIGS. 1 and 2. The event prediction system 10 includes a sensor signal processing system 1.

  The sensor signal processing system 1 is a system that performs signal processing on the sensor signal output from the measuring device 2 that monitors the target space 100 (see FIG. 2). The measuring device 2 includes a sensor 21 (see FIG. 1) that generates body motion data related to the body motion of the subject (person) in the target space 100, and outputs a sensor signal including the body motion data. The sensor signal processing system 1 includes an acceleration calculation unit 111 (see FIG. 1) that obtains an acceleration regarding the movement of the subject's body based on the body motion data.

  The “target space” in the present disclosure is a specific space in a facility such as a housing for elderly people with a service, a care facility, or a hospital. If the target space 100 is a space in a private room for elderly with service or a care facility, the “target person” is a resident (caregiver) of the private room. If the target space 100 is a space in a hospital room, the “subject” is a patient admitted to the hospital room. In addition, “body movement” as used in the present disclosure is not only the movement of the body when the person is sleeping (such as turning over), but also, for example, a person who is standing, sitting, or walking Includes general body movements. The term “acceleration” as used in the present disclosure means a ratio of a change in speed (moving speed) of at least a part of the subject's body to time when at least a part of the subject's body moves. For example, in a subject who is walking, since movement occurs in the entire body, the ratio of the speed change of the entire body to time is “acceleration”. On the other hand, for example, in a sleeping subject, when a part of the body moves, the ratio of the speed change of the part of the moving body to the time becomes “acceleration”.

  In other words, the sensor signal processing system 1 can quantitatively analyze the movement of the subject as acceleration based on the body movement data acquired from the measurement device 2. The acceleration regarding the movement of the subject's body can be used, for example, for grasping the subject's state such as the subject's behavior and health, and for identifying the individual.

  The event prediction system 10 according to the present embodiment is a system that uses the processing result of the sensor signal processing system 1 to determine a sign of occurrence of a specific event related to a subject. The “specific event” in the present disclosure is a specific event selected from various events that may occur in relation to the subject, and includes, for example, terminal care (end-of-life care), walking Fall, sickness or injury requiring hospitalization, death, cognitive decline (cognitive impairment), and epilepsy. Furthermore, individual actions in the daily life of the subject, for example, getting up (getting out of bed), excretion, and going to bed are also included in the “specific event”.

  That is, the event prediction system 10 determines a sign of occurrence of these specific events based on the body motion data acquired from the measurement device 2, that is, a “feeling” that appears in advance before the specific event occurs. In other words, the event prediction system 10 can determine a specific event that is predicted to occur in the future, not the current state of the subject. Thus, according to the event prediction system 10 according to the present embodiment, there is an advantage that it is possible to determine a sign of occurrence of a specific event related to the subject.

(2) Details Details of the sensor signal processing system 1 and the event prediction system 10 according to the first embodiment will be described below with reference to the drawings. Below, as shown in FIG. 2, the case where the object space 100 is set in the private room 50 of the elderly house with service will be described as an example. In other words, the “target person” in this case is a resident in a private room.

(2.1) Configuration The event prediction system 10 includes the sensor signal processing system 1 as described above. In the present embodiment, as shown in FIG. 1, the event prediction system 10 further includes a second arithmetic processing unit 12. The sensor signal processing system 1 and the event prediction system 10 including the sensor signal processing system 1 are realized by, for example, a computer system installed in a manager room or the like of a facility where the target space 100 is installed (here, an elderly house with a service). .

  The sensor signal processing system 1 receives a sensor signal from the measuring device 2 that monitors the target space 100. As described above, the measurement device 2 includes the sensor 21 that generates body motion data related to the body motion of the subject (person) in the target space 100, and sends the sensor signal including the body motion data to the sensor signal processing system 1. Output.

  In the present embodiment, the measurement device 2 is not included in the components of the sensor signal processing system 1 and the event prediction system 10. Therefore, the sensor signal processing system 1 and the event prediction system 10 can be applied in combination with various types of measuring devices 2. However, each of the sensor signal processing system 1 and the event prediction system 10 may include the measuring device 2 as a component.

  The measuring device 2 includes a sensor 21 and a signal processing circuit 22. The sensor 21 is a non-contact type sensor that detects the subject's body movement without contacting the subject, that is, without contact. In short, the measuring device 2 generates body movement data relating to the body movement of the subject in a non-contact manner. Therefore, the measuring device 2 can generate body movement data without interfering with the movement of the subject. The measuring device 2 is, for example, a radio wave type Doppler sensor.

  The sensor 21 is a transducer that can convert electrical signals and radio waves to each other. For example, the sensor 21 is a radio wave type sensor that transmits and receives microwave radio waves. For example, the sensor 21 transmits radio waves to the target space 100 at a predetermined time interval (one second interval as an example). The sensor 21 receives a reflected wave (radio wave) reflected by a person (including the target person) existing in the target space 100.

  The signal processing circuit 22 performs signal processing on the output signal (electrical signal corresponding to the reflected wave) of the sensor 21 when the reflected wave is received, and shows the body motion of the subject existing in the target space 100. Generate dynamic data. Specifically, the signal processing circuit 22 compares the frequency of the received radio wave (reflected wave) with the frequency of the transmitted radio wave to obtain the speed of movement of the subject's body using the Doppler effect. , Generate body movement data. Here, the speed of movement of the subject's body to be calculated by the signal processing circuit 22 indicates whether the body is moving toward or away from the sensor 21 by including a positive / negative sign. , "Body movement direction" information is included. That is, the body motion data generated by the signal processing circuit 22 can be handled as a vector value including a “direction” component. As a result, the signal processing circuit 22 can determine whether the body is moving toward or away from the sensor 21 and can detect, for example, the rising (leaving) of the subject. Further, the acceleration obtained based on the body motion data may be a vector value including the “direction” component.

  In the present embodiment, the signal processing circuit 22 has a function of generating measurement data indicating the heartbeat and breathing states of the subject by extracting a specific frequency component from the body motion data. Specifically, the signal processing circuit 22 filters the body motion data with a heart rate filter and extracts a frequency component of the body motion due to the heart beat, thereby measuring measurement data (hereinafter referred to as “heart rate measurement”). Data)). Furthermore, the signal processing circuit 22 filters the body motion data with a breathing filter and extracts the frequency component of the body motion due to breathing, whereby measurement data (hereinafter referred to as “breathing measurement data”) indicating the state of breathing. ) Is generated. Here, the cycle in which the signal processing circuit 22 generates the heartbeat measurement data and the respiration measurement data is longer than the cycle in which the signal processing circuit 22 generates the body motion data. As an example, the signal processing circuit 22 generates body movement data every second, and generates heart rate measurement data and respiration measurement data every 5 seconds.

  The signal processing circuit 22 outputs a sensor signal including body motion data, heart rate measurement data, and respiration measurement data to the sensor signal processing system 1. In the present embodiment, the signal processing circuit 22 is configured to be able to communicate with the sensor signal processing system 1 by wireless communication conforming to a wireless communication method such as Bluetooth (registered trademark). The measurement device 2 and the sensor signal processing system 1 are not limited to a configuration that directly communicates, and may be configured to communicate via a repeater, for example.

  As shown in FIG. 2, the measuring device 2 is installed in a private room 50 in which a target space 100 is set. In the example of FIG. 2, the private room 50 is provided with facilities such as a bed 51, a toilet 52, a wash basin 53, an entrance / exit sliding door 54, and a window 55. In the private room 50, facilities such as a bed 51, a toilet 52, a wash basin 53, a sliding door 54, and a window 55 are not essential, and can be omitted as appropriate. An air conditioner (air conditioner) 20 that adjusts the air environment in the private room 50 is installed on the wall of the private room 50. In this embodiment, the measuring apparatus 2 is arrange | positioned beside the air conditioning equipment 20 as an example (side).

  Here, as an example, the measuring apparatus 2 is installed in a posture (orientation) such that the target space 100 including at least the bed 51 is a monitoring target. In the present embodiment, the target space 100 is substantially the entire space in the private room 50 excluding the toilet 52. The target space 100 is not limited to this example, and may be, for example, the entire space in the private room 50 or may be changed as appropriate.

  As shown in FIG. 1, the sensor signal processing system 1 includes a first arithmetic processing unit 11, an acquisition unit 13, a storage unit 14, and an output unit 15.

  The acquisition unit 13 acquires body motion data related to the body motion of the subject person. That is, the acquisition unit 13 has a communication function with the measurement apparatus 2 (the signal processing circuit 22 thereof). In the present embodiment, the acquisition unit 13 is configured to be communicable with the measurement apparatus 2 by wireless communication conforming to a wireless communication method such as Bluetooth (registered trademark). The acquisition unit 13 acquires at least body motion data from the measurement device 2 by communicating with the measurement device 2 regularly or irregularly. Specifically, the acquisition unit 13 acquires body motion data together with heartbeat measurement data and respiration measurement data by receiving a sensor signal from the measurement device 2. When the acquisition unit 13 acquires data (body motion data, heart rate measurement data, and respiration measurement data) from the measurement device 2, the acquisition unit 13 outputs the acquired data to the first arithmetic processing unit 11.

  The first calculation processing unit 11 has at least functions of an acceleration calculation unit 111 and a presence determination unit 112. The first arithmetic processing unit 11 is realized by a computer system including a processor and a memory, for example. Functions of the acceleration calculation unit 111, the presence determination unit 112, and the like are realized by the processor of the first calculation processing unit 11 executing the program. The program may be recorded in advance in the memory or the storage unit 14 of the first arithmetic processing unit 11, or provided by being recorded on a non-temporary recording medium such as a memory card through an electric communication line such as the Internet. May be.

  The acceleration calculation unit 111 obtains acceleration regarding the body movement of the subject based on the body motion data. That is, when there is a movement in at least a part of the subject's body, the acceleration calculation unit 111 obtains a ratio of the speed (movement speed) change of at least a part of the subject's body as time. Here, the acceleration calculation unit 111 obtains data indicating acceleration (hereinafter referred to as “acceleration data”) by obtaining acceleration according to a predetermined algorithm based on the body motion data acquired by the acquisition unit 13 from the measurement device 2. The generated acceleration calculation process is executed. The acceleration calculation process in the acceleration calculation unit 111 will be described in detail in the section “(2.2.2) Acceleration calculation process”.

  The presence determination unit 112 determines the presence / absence of the subject in the target space 100 based on the body motion data. The presence determination unit 112 uses the body movement data acquired by the acquisition unit 13 from the measurement device 2 to generate data indicating the presence or absence of the subject (hereinafter referred to as “presence / absence data”) according to a predetermined algorithm. Execute.

  In the present embodiment, the presence determination unit 112 obtains a time series analysis analysis model that represents body motion data acquired at a predetermined timing by a plurality of body motion data acquired before the predetermined timing. Perform analysis processing. For example, the acquisition unit 13 acquires measurement data from the measurement device 2 every second. As an example, in the time-series analysis process, an analysis model for time-series analysis is obtained in which body motion data acquired at a predetermined timing is represented by a plurality of (for example, 30) body motion data before the predetermined timing. In the present embodiment, the presence determination unit 112 uses, for example, an auto-regressive (AR) model, and the body motion data acquired at a predetermined timing is represented by 30 body motion data in the past 30 seconds. Obtain an analysis model of the correlation function. The analysis model of the time series analysis performed by the presence determination unit 112 is not limited to the autoregressive model but may be another analysis model such as an extended Kalman model, and the analysis model can be appropriately changed in consideration of the calculation amount and the like.

  The presence determination unit 112 determines the presence / absence of the subject at a predetermined timing based on a determination condition including a condition related to a coefficient of the analysis model obtained in the time series analysis process. For example, the presence determination unit 112 satisfies the determination condition that the coefficient of the analysis model obtained in the time series analysis process exceeds a predetermined threshold, or the size of the acquired body motion data exceeds a predetermined determination value. Based on this, it is determined whether or not the subject exists at a predetermined timing. That is, if the primary coefficient of the autoregressive model exceeds the threshold value or the size of the body motion data exceeds the determination value, the presence determination unit 112 has the target person in the target space 100 (in-room state). ). If the first coefficient of the autoregressive model is equal to or smaller than the threshold value and the size of the measurement data is equal to or smaller than the determination value, the presence determination unit 112 determines that the target person does not exist in the target space 100 (is absent) judge.

  As described above, in the event prediction system 10 according to the present embodiment, the presence determination unit 112 determines the presence / absence of the target person at a predetermined timing based on the determination condition regarding the coefficient of the analysis model obtained in the time series analysis process. Judgment. Therefore, the presence determination unit 112 is less susceptible to temporary fluctuations in measurement data, and the determination accuracy of the presence determination unit 112 can be improved.

  Further, the presence determination unit 112 determines the presence or absence of the subject in the target space 100 using at least one of the heartbeat measurement data, the respiration measurement data, and the acceleration data instead of or together with the body motion data. May be. That is, the presence determination unit 112 may execute a process of determining the presence or absence of the subject in the target space 100 based on at least one of body motion data, heart rate measurement data, respiration measurement data, and acceleration data. Heart rate measurement data, respiration measurement data, and acceleration data are all data based on body motion data. Therefore, for example, even when the presence / absence of the subject is determined based on the acceleration data, the presence determination unit 112 indirectly determines the presence / absence of the subject in the target space 100 based on the body motion data. In short, the presence determination unit 112 may determine whether or not the subject person exists in the target space 100 based on the body motion data directly or indirectly.

  Here, when determining the presence / absence of the target person based on the acceleration data, the presence determination unit 112 can also determine the presence / absence of the target person by identifying an individual. That is, the presence determination unit 112 can identify whether or not it is a target person (a resident of the private room 50) from the acceleration data. Thereby, when a person other than the target person (for example, a staff of the facility) is in the target space 100, the presence determination unit 112 does not have the target person in the target space 100 or is a person other than the target person in the target space 100. Can be determined to exist.

  The second arithmetic processing unit 12 has at least functions of a sign determination unit 121 and a state determination unit 122. Similar to the first arithmetic processing unit 11, the second arithmetic processing unit 12 is realized by, for example, a computer system including a processor and a memory. When the processor of the second arithmetic processing unit 12 executes the program, functions such as the sign determination unit 121 and the state determination unit 122 are realized. The program may be recorded in advance in the memory or the storage unit 14 of the second arithmetic processing unit 12, or may be provided through a telecommunication line such as the Internet or recorded in a non-temporary recording medium such as a memory card. May be. In the present embodiment, the second arithmetic processing unit 12 is configured by a computer system different from the first arithmetic processing unit 11.

  Output data of the first arithmetic processing unit 11 is input to the second arithmetic processing unit 12. In the present embodiment, the output data of the first arithmetic processing unit 11 includes body movement data, heart rate measurement data, and respiration measurement data acquired by the acquisition unit 13 from the measurement device 2. Furthermore, in the present embodiment, the output data of the first arithmetic processing unit 11 includes acceleration data (acceleration data) about the movement of the subject's body obtained by the acceleration calculation unit 111 based on the body motion data. It is out. The output data of the first arithmetic processing unit 11 includes data (presence / absence data) indicating the determination result of the presence determination unit 112. Therefore, heart rate measurement data, respiration measurement data, acceleration data, and presence / absence data are input to the second arithmetic processing unit 12 in addition to body motion data. The heartbeat measurement data, the respiration measurement data, and the acceleration data are all obtained based on the body motion data, in other words, are data based on the body motion data.

  The sign determination unit 121 determines a sign of occurrence of a specific event related to the subject based on the body motion data. That is, the sign determination unit 121 executes a sign determination process for determining a sign of occurrence of a specific event such as terminal care, based on the body motion data acquired by the acquisition unit 13 from the measurement device 2 according to a predetermined algorithm. To do. Here, the sign determination unit 121 determines a sign based on the body motion data acquired in the past reference period. The “reference period” in the present disclosure is a period in the past (before the reference time) when the time when the sign determination unit 121 performs the determination is set as the reference time. In the present embodiment, the reference period is a period of a certain time (for example, 30 minutes, 1 hour, or 1 day) whose end point is the reference time point. In other words, the sign determination unit 121 is a set of body motion data acquired by the acquisition unit 13 in a reference period starting from a certain time before the reference time and ending at the reference time, that is, a time series of body motion data. The sign determination process is executed based on the data.

  Further, as with the presence determination unit 112, the predictor determination unit 121 also uses a specific event using at least one of heart rate measurement data, respiration measurement data, and acceleration data instead of or together with body motion data. A sign of occurrence may be determined. That is, the sign determination unit 121 predicts the occurrence of a specific event based on at least one of body motion data, heart rate measurement data, respiration measurement data, and acceleration data included in the output data of the first arithmetic processing unit 11. The sign determination process for determining may be executed. Heart rate measurement data, respiration measurement data, and acceleration data are all data based on body motion data. In short, the sign determination unit 121 may determine a sign of occurrence of a specific event based on body motion data directly or indirectly.

  The determination result of the sign determination unit 121 is output to the output unit 15 as sign data. In the present embodiment, the sign determination unit 121 determines only the presence or absence of a sign of occurrence of a specific event. In other words, the sign determination unit 121 determines the presence / absence of a specific event occurrence without distinguishing the type of the specific event for which the sign is determined. Therefore, the sign data generated by the sign determination unit 121 indicates the presence or absence of a sign of occurrence of a specific event.

  The sign determination unit 121 determines a sign based on a change in the amount of physical activity of the subject based on the body motion data. The “physical activity amount” referred to in the present disclosure is represented by the product of the strength (intensity) of physical activity and the time when the physical activity was performed. “Physical activity” as used in the present disclosure is an activity performed by the subject, and is not limited to exercise aimed at maintaining or improving physical fitness, but all activities (actions) that consume more energy than in a resting state. Means. The sign determination process in the sign determination unit 121 will be described in detail in the column “(2.2.3) Sign determination process”.

  The state determination unit 122 determines the current state of the subject such as the health state, sleep state, and mental state of the subject based on the body motion data. That is, the state determination unit 122 determines, based on the body motion data acquired by the acquisition unit 13 from the measurement device 2, the current state of the subject such as good physical condition or poor physical condition, for example, according to a predetermined algorithm. Execute the process.

  Further, as with the sign determination unit 121, the state determination unit 122 uses at least one of heart rate measurement data, respiration measurement data, and acceleration data instead of or together with the body movement data. You may determine the current state of That is, the state determination unit 122 determines the current state of the subject based on at least one of body motion data, heart rate measurement data, respiration measurement data, and acceleration data included in the output data of the first arithmetic processing unit 11. A state determination process for determining may be executed. In short, the state determination unit 122 may determine the current state of the subject based on the body motion data directly or indirectly.

  The determination result of the state determination unit 122 is output to the output unit 15 as state data. Further, output data (body motion data, heartbeat measurement data, respiratory measurement data, acceleration data, and presence / absence data) of the first calculation processing unit 11 is also output from the second calculation processing unit 12 to the output unit 15.

  The storage unit 14 includes, for example, an electrically rewritable nonvolatile memory such as an EEPROM (Electrically Erasable Programmable Read Only Memory), and a volatile memory such as a RAM (Random Access Memory). The storage unit 14 stores body motion data, heart rate measurement data, respiration measurement data, and the like acquired by the acquisition unit 13. Here, the storage unit 14 stores at least body motion data over the reference period. Further, the storage unit 14 also stores the calculation result (including acceleration data and presence / absence data) of the first calculation processing unit 11, the calculation result (including predictive data and state data) of the second calculation processing unit 12, and the like.

  The output unit 15 outputs the determination result of the sign determination unit 121. Further, the output unit 15 outputs the determination results of the presence determination unit 112 and the state determination unit 122. Here, the output unit 15 has a communication function with devices such as the display 3 and the information terminal 4, for example. The information terminal 4 is, for example, a smartphone, a tablet terminal, or a personal computer. The output unit 15 outputs the data input from the second arithmetic processing unit 12 to these devices.

  That is, the presence / absence data generated by the presence determination unit 112, the sign data generated by the sign determination unit 121, and the state data generated by the state determination unit 122 are output from the output unit 15 to the display 3, the information terminal 4, and the like. Is done. The output unit 15 may output the determination result when there is a change in the determination result of the presence determination unit 112, the sign determination unit 121, or the state determination unit 122. For example, when there is a request from the information terminal 4 The determination result may be output to

  As a result, the determination result of the presence determination unit 112, the determination result of the predictor determination unit 121, and the determination result of the state determination unit 122 are presented to the manager on the display 3 and the information terminal 4 or the like. The administrator can check the determination results of the presence determination unit 112, the sign determination unit 121, and the state determination unit 122 on the display 3, the information terminal 4, and the like. The manner of presentation on the display 3 and the information terminal 4 may be any of display, audio output, printout (printing), writing to a non-temporary recording medium, and transmission to another information terminal, for example. Good.

  The output unit 15 may output the calculation result (acceleration data) of the acceleration calculation unit 111. Furthermore, the output unit 15 is not limited to a configuration that outputs the determination result to devices such as the display 3 and the information terminal 4 through communication. For example, the output unit 15 itself may output the determination result by display, audio output, printout (printing), writing to a non-temporary recording medium, transmission to an information terminal, or the like.

(2.2) Operation (2.2.1) Overall Operation The overall operation of the sensor signal processing system 1 according to the present embodiment and the event prediction system 10 including the sensor signal processing system 1 will be described with reference to the flowchart of FIG. To do.

  The acquisition part 13 performs the acquisition process which acquires body movement data from the measuring apparatus 2 regularly or irregularly (S1). In the present embodiment, as an example, the acquisition unit 13 acquires body motion data, heart rate measurement data, and respiration measurement data from the measurement device 2 every second. The acquisition unit 13 outputs the data (body motion data, heart rate measurement data, and respiration measurement data) acquired from the measurement device 2 to the first arithmetic processing unit 11. Here, it is assumed that the measuring apparatus 2 updates the body movement data every second, and updates the heart rate measurement data and the respiration measurement data every 5 seconds. Therefore, the heartbeat measurement data and the respiration measurement data acquired by the acquisition unit 13 from the measurement device 2 are updated every 5 seconds.

  When data (body motion data, heart rate measurement data, and respiration measurement data) is input from the acquisition unit 13, the first calculation processing unit 11 performs preprocessing such as noise cut and moving average calculation on these data. (S2). The first arithmetic processing unit 11 stores the data after the preprocessing in the storage unit 14.

  The first calculation processing unit 11 executes acceleration calculation processing in the acceleration calculation unit 111 based on the body motion data after the pre-processing, and obtains acceleration data indicating acceleration (S3). The acceleration calculation process will be described in detail in the section “(2.2.2) Acceleration calculation process”.

  Next, the 1st arithmetic processing part 11 performs presence determination processing in the presence determination part 112 based on the body motion data etc. after pre-processing, and the presence or absence of the subject in the target space 100 is determined by time series analysis. Determine (S4).

  As a result of the presence determination process, if it is determined that the target person is present in the target space 100 (S5: Yes), the second calculation processing unit 12 is based on the body motion data after the pre-processing and the like determination unit 121. The sign determination process is executed at, and a sign of occurrence of the specific event is determined (S6). Details of the sign determination process will be described in the column “(2.2.3) Sign determination process”.

  Next, the 2nd arithmetic processing part 12 performs a state determination process in the state determination part 122 based on the body motion data etc. after a pre-processing, and determines a subject's present state (S7).

  And the output part 15 performs the output process which outputs the determination result of the presence determination part 112, the determination result of the precursor determination part 121, and the determination result of the state determination part 122 (S8). If it is determined as a result of the presence determination process that the target person does not exist (absent) in the target space 100 (S5: No), the sign determination process and the state determination process are skipped, and the process proceeds to the output process. In this case, the output unit 15 outputs only the determination result of the presence determination unit 112 in the output process.

  The sensor signal processing system 1 and the event prediction system 10 including the sensor signal processing system 1 perform processing from step S1 to step S8, for example, at intervals of 1 second.

  However, the order of processing illustrated in FIG. 3 is merely an example, and the order of processing can be changed as appropriate.

(2.2.2) Acceleration Calculation Processing The acceleration calculation processing (S3 in FIG. 3) performed by the acceleration calculation unit 111 will be described.

  In the acceleration calculation process, the acceleration calculation unit 111 obtains an acceleration by performing a differentiation process on a plurality of body motion data arranged in time series. Specifically, the acceleration calculation unit 111 performs differential calculation on a plurality of body motion data arranged in time series stored in the storage unit 14, so that the movement of the subject's body that has occurred in a predetermined period is obtained. Find the acceleration of.

  That is, the acceleration is the ratio of the speed (movement speed) change of at least a part of the subject's body to the time when there is a movement in at least a part of the subject's body. On the other hand, the body motion data is data reflecting the speed of movement of the subject. Therefore, in the acceleration calculation process, the acceleration calculation unit 111 obtains the differential value of the body motion data as acceleration and generates acceleration data. Thereby, when there is no time for the speed of movement of the subject reflected by the body motion data, that is, when the speed of movement is constant, the acceleration obtained by the acceleration calculation process is zero. The acceleration obtained by the acceleration calculation process increases as the temporal change in the speed of movement of the subject reflected by the body motion data increases.

  FIG. 4 is a flowchart showing a specific example of acceleration calculation processing.

  As shown in FIG. 4, when the acceleration calculation process starts, the acceleration calculation unit 111 calculates a time difference dt between two adjacent body movement data among a plurality of body movement data arranged in time series (S31). Here, attention is focused on the body motion data acquired at the predetermined timing “t” and the body motion data acquired at the timing “t−1” immediately before the predetermined timing. In this case, the acceleration calculation unit 111 calculates the “time (t)” corresponding to the predetermined timing “t” and the “time (t−1)” corresponding to the previous timing “t−1”. Let the difference be the time difference dt.

  Next, the acceleration calculation unit 111 calculates a change amount dv of the value (magnitude) of body motion data between two adjacent body motion data (S32). At this time, the acceleration calculation unit 111 acquires “body motion (t)” acquired at a predetermined timing “t” and “body motion (t−1) acquired at the timing“ t−1 ”immediately before the“ body motion (t−1) ”. The difference from “)” is defined as a change amount dv.

  Next, the acceleration calculation unit 111 calculates acceleration data by dividing the change amount dv by the time difference dt (S33). Thereby, in the acceleration calculating part 111, the absolute value of an acceleration can be specified (S34), and also the positive / negative (sign) of an acceleration can be specified (S35). The absolute value of acceleration corresponds to the magnitude of the subject's movement. The positive / negative acceleration depends on the direction in which the subject moves (the direction in which the body approaches or leaves the sensor 21) and the acceleration / deceleration of the subject.

  However, the order of processing illustrated in FIG. 4 is merely an example, and the order of processing can be changed as appropriate.

  Here, the body motion data to be subjected to the differential processing may be body motion data before the pre-processing, or for example, body motion data after the pre-processing such as noise cut and moving average calculation. Also good. By executing the acceleration calculation process using the body motion data after the pre-processing, it is possible to suppress the influence of noise.

(2.2.3) Predictor Determination Process The predictor determination process (S6 in FIG. 3) performed by the predictor determination unit 121 will be described.

  In the sign determination process, the sign determination unit 121 determines a sign of occurrence of a specific event based on body motion data acquired in a past reference period. Specifically, the sign determination unit 121 analyzes the size, amount of variation, frequency of change, time zone of change, and the like for a plurality of body motion data arranged in time series stored in the storage unit 14 and performs analysis. Predict the occurrence of a specific event from the result.

  That is, before a specific event occurs, a characteristic tendency often appears in the subject's body movement data. In particular, characteristic trends tend to appear in the time series data of body motion data. Therefore, in the sign determination process, the sign determination unit 121 analyzes the average value (magnitude), the amount of variation, the frequency of change, the time zone of change, and the like for the time series data of body movement data, and the occurrence of a specific event. The predictor is determined and the predictor data is generated. Thereby, for example, when a characteristic tendency appears, for example, the average value of the body motion data continuously decreases significantly, it is determined in the sign determination process that there is a sign of occurrence of terminal care.

  FIG. 5 is a flowchart illustrating a specific example of the sign determination process.

  As shown in FIG. 5, when the sign determination process starts, the sign determination unit 121 first sets various variables (S61). The variables set at this time include a threshold value V1 to be compared with statistical values such as body movement data, a start date of predictive judgment, a totaling period, and a specified value N1 which will be described later.

  Next, the sign determination unit 121 calculates a statistical value such as body movement data in a counting period after the start date (S62). The “statistical value” as used in the present disclosure is a value that is statistically obtained from time series data of body motion data (including acceleration data) in the counting period, and is, for example, an average value (moving average value of body motion data) ), Median, mode, minimum and variance.

  The sign determination unit 121 compares the calculated statistical value with the threshold value V1 (S63). If the statistical value is less than the threshold value V1 (S63: Yes), the predictor determination unit 121 changes the value of the alert flag from “0” to “1” (S64). Further, the sign determination unit 121 calculates the cumulative value of the alert flag whose value is “1” within the counting period (S65).

  The sign determination unit 121 compares the calculated cumulative value of the alert flag with the specified value N1 (S66). If the cumulative value is equal to the specified value N1 (S66: Yes), the sign determination unit 121 determines that “there is a sign” of the specific event (S67). On the other hand, when the statistical value is greater than or equal to the threshold value V1 (S63: No) or when the cumulative value is less than the specified value N1 (S66: No), the predictor determination unit 121 determines that there is “no sign” of the specific event ( S68).

  However, the order of processing illustrated in FIG. 5 is merely an example, and the order of processing can be changed as appropriate.

  The flowchart shown in FIG. 5 shows a sign determination process for a specific event that occurs in the long term (as an example, several days to several weeks or more) such as a disease requiring hospitalization. However, it is not limited to this type of specific event, for example, it is possible to realize predictive judgment processing for a specific event that occurs in the short-term (for example, several minutes to several hours or less) such as rising from a sleeping state (getting out of bed). It is. For a specific event that occurs in the short term (for example, several minutes to several hours or less), for example, the direction of the inequality sign in the process of comparing the statistical value with the threshold value V1 (S63) is opposite to the example of FIG. . Furthermore, whether the long-term specific event or the short-term specific event is used as the specific event of the predictor determination process can be switched, for example, by changing the length of the reference period set in step S61. is there. As an example, when the reference period is set to 60 days, the sign determination unit 121 can determine whether there is a sign of occurrence of a long-term specific event from the change point of the 60-day cycle. On the other hand, when the reference period is set to 10 seconds, the sign determination unit 121 can determine whether or not there is a sign of occurrence of a short-term specific event from the change point of the 10-second period.

  Next, as an example, the sign determination process in the case illustrated in FIG. 6 will be described. FIG. 6 is a graph showing changes in body motion data, where the horizontal axis is the time axis and the vertical axis is the average value (moving average value) of the body motion data. In FIG. 6, one scale on the horizontal axis corresponds to “5 days”.

  In the example of FIG. 6, the average value of the body motion data maintains the threshold value V1 or more before the time point t1, whereas the average value of the body motion data becomes less than the threshold value V1 after the time point t1. In other words, when the average value of the body motion data falls below the threshold value V1 at time t1, a tendency different from that before time t1 appears. The subject (subject) in this example has a disease requiring hospitalization several days after time t1. That is, a sign of a specific event of illness requiring hospitalization appears as a characteristic tendency of the body motion data of the subject at time t1.

  In particular, in this embodiment, as described above, the sign determination unit 121 can determine a sign based on a change in the amount of physical activity of the subject based on the body motion data. Here, from the equation of motion (F = ma), the product of acceleration and mass is force (F), and in the physical activity using the entire body of the subject, the weight of the subject corresponds to the mass. Assuming that the increase or decrease in the weight of the subject is negligible, the acceleration is considered to correspond to the muscle strength exerted by the subject in the physical activity. Therefore, the acceleration calculated by the acceleration calculation unit 111 corresponds to the intensity of physical activity, and the amount of physical activity in a certain period is represented by an accumulated value of acceleration in the certain period. Therefore, in this embodiment, in the sign determination process, the sign determination unit 121 determines a sign of occurrence of a specific event based on acceleration data obtained by the acceleration calculation process.

  As a determination condition in the sign determination process, for example, there are the following first to fourth conditions. The first condition is that the state in which the amount of physical activity is equal to or less than the first threshold continues for a first time (for example, several days or weeks). The second condition is that the amount of decrease in the amount of physical activity during the second time (for example, several tens of minutes or several hours) is equal to or greater than the second threshold. The third condition is that the amount of physical activity is equal to or less than a third threshold value (smaller than the first threshold value). The fourth condition is that the difference between the average value of the amount of physical activity at rest such as during sleep and the average value of the amount of physical activity at rest is equal to or less than the fourth threshold value. Conditional expressions and parameters (including threshold values) for realizing these determination conditions are stored in the storage unit 14, for example.

  As another example, in the sign determination process, a sign of occurrence of a specific event may be determined using body motion data as a vector value. In this case, the sign determination unit 121 analyzes acceleration data as a vector value including a “direction” component in addition to a scalar amount such as an average value of body motion data. Accordingly, the sign determination unit 121 determines that there is a sign of occurrence of a specific event such as wrinkles or a decline in cognitive function, for example, when movement of a subject in a direction that exits the private room 50 is detected in the private room 50. To do.

  In addition, the determination conditions (including the threshold value and the length of the period) used for the sign determination process may be set for each target person. Therefore, in a service-oriented elderly home or the like having a plurality of private rooms 50, determination conditions used for the sign determination process may be set for each private room 50.

(3) Modification Example 1 is only one of various embodiments of the present disclosure. The first embodiment can be variously modified according to the design or the like as long as the object of the present disclosure can be achieved. Functions similar to those of the sensor signal processing system 1 may be embodied by a sensor signal processing method, a (computer) program, a non-transitory recording medium storing the program, or the like. The sensor signal processing method according to one aspect acquires body motion data from the measurement device 2 that outputs body motion data related to the subject's body motion (step S1 in FIG. 3), and based on the body motion data, The acceleration for the body movement is obtained (step S3 in FIG. 3). A program according to an aspect is a program for causing a computer system to execute the sensor signal processing method. The functions similar to those of the event prediction system 10 may be embodied by an event prediction method, a (computer) program, a non-transitory recording medium that records the program, or the like. The event prediction method according to one aspect obtains body motion data related to a subject's body motion (step S1 in FIG. 3), and determines an indication of occurrence of a specific event related to the subject based on the body motion data. (Step S6 in FIG. 3). A program according to an aspect is a program for causing a computer system to execute the event prediction method.

  Hereinafter, modifications of the first embodiment will be listed. The modifications described below can be applied in appropriate combinations.

  The execution subject of the sensor signal processing system 1, the event prediction system 10, the sensor signal processing method, or the event prediction method in the present disclosure includes a computer system. The computer system mainly includes a processor and a memory as hardware. By executing the program recorded in the memory of the computer system by the processor, the function as the execution subject of the sensor signal processing system 1, the event prediction system 10, and the sensor signal processing method or event prediction method in the present disclosure is realized. The The program may be recorded in advance in a memory of a computer system, may be provided through a telecommunication line, or recorded in a non-transitory recording medium such as a memory card, an optical disk, or a hard disk drive that can be read by the computer system. May be provided. A processor of a computer system includes one or more electronic circuits including a semiconductor integrated circuit (IC) or a large scale integrated circuit (LSI). The plurality of electronic circuits may be integrated on one chip, or may be distributed on the plurality of chips. The plurality of chips may be integrated into one device, or may be distributed and provided in a plurality of devices.

  In addition, the fact that a plurality of functions in the sensor signal processing system 1 are integrated in one casing is not an essential configuration for the sensor signal processing system 1, and the constituent elements of the sensor signal processing system 1 are a plurality of casings. It may be provided in a dispersed manner. Furthermore, at least a part of the functions of the sensor signal processing system 1 may be realized by, for example, a server device and cloud (cloud computing). Similarly, it is not an essential configuration for the event prediction system 10 that a plurality of functions in the event prediction system 10 are integrated in one case, and the components of the event prediction system 10 are distributed to a plurality of cases. May be provided. Furthermore, at least a part of the functions of the event prediction system 10 may be realized by, for example, a server device and a cloud (cloud computing). On the contrary, in the first embodiment, functions distributed to a plurality of devices, for example, the measurement device 2 and the sensor signal processing system 1 may be integrated in one housing.

  In addition, the facility where the target space 100 is set is not limited to a facility where staff such as a housing for elderly people with a service, a care facility, or a hospital lives, but may be a daycare facility such as a nursery school. In this case, the “subject” is an infant or an infant to be raised. Furthermore, the facility where the target space 100 is set may be a general residential (detached house or collective housing) facility. In this case, the “target person” is a resident (resident) of the housing facility. In a situation where the target person lives alone in the facility where the target space 100 is set, the determination result of the event prediction system 10 is, for example, the target person's family living away from the target person or the target person's residence Notify local community supporters (care managers, life counselors, etc.).

  The measuring device 2 may be configured to output at least body motion data to the sensor signal processing system 1, and outputting data other than body motion data (heart rate measurement data and respiration measurement data) is sensor signal processing. It is not an essential configuration for the system 1.

  The measuring device 2 is not limited to a radio wave type Doppler sensor, and may be, for example, an ultrasonic type Doppler sensor that transmits ultrasonic waves. Furthermore, the measuring apparatus 2 may generate body motion data related to the body motion of the subject in the target space 100. Therefore, the measuring apparatus 2 may be a sensor other than a Doppler sensor, such as a radio wave sensor using a frequency modulation continuous wave radar system, a sensor using a TOF (Time Of Flight) system, or an image sensor. Furthermore, the measuring device 2 is not limited to a non-contact type sensor that generates non-contact body motion data related to the subject's body motion. It may be a sensor.

  The sign determination unit 121 only needs to determine a sign of the occurrence of the specific event based on at least the body movement data, and the sign determination unit 121 determines the sign of the occurrence of the specific event based on the acceleration data. This is not an essential configuration for the prediction system 10. When the sign determination unit 121 does not use acceleration data, the acceleration calculation unit 111 can be omitted as appropriate.

  In addition, the sensor signal processing system 1 including the acquisition unit 13, the storage unit 14, and the output unit 15 is not an essential configuration for the event prediction system 10, and the event prediction system 10 is separate from the sensor signal processing system 1. The acquisition unit 13, the storage unit 14, and the output unit 15 may be provided. About the memory | storage part 14 and the output part 15, it is not an essential structure for the event prediction system 10, and at least one of the memory | storage part 14 and the output part 15 may be abbreviate | omitted suitably.

  The acceleration data generated by the acceleration calculation unit 111 is not necessarily used by the sign determination unit 121, the state determination unit 122, and the like, and may be used only for determination by the presence determination unit 112, for example. Furthermore, the calculation result of the acceleration calculation unit 111 may be output only by the output unit 15. In this case, the sign determination unit 121 and the state determination unit 122 are not essential components in the event prediction system 10, and at least one of the sign determination unit 121 and the state determination unit 122 may be omitted as appropriate.

  Further, the communication method between the measuring device 2 and the sensor signal processing system 1 is not limited to wireless communication, but may be wired communication (including power line carrier communication).

  Moreover, the 1st arithmetic processing part 11 and the 2nd arithmetic processing part 12 may be comprised by one computer system. Conversely, the first arithmetic processing unit 11 and the second arithmetic processing unit 12 may be configured by three or more computer systems.

  In addition, a technique such as machine learning may be used for some processes of the sensor signal processing system 1 and the event prediction system 10, for example, an acceleration calculation process and a sign determination process. For example, it is preferable that a technique such as machine learning is used under the determination condition that the sign is determined based on the body motion data acquired in the past reference period.

  As an example, in determining the threshold value V1, in addition to the basal metabolic rate that can be estimated from the age, height, weight, etc. of the subject (subject), the threshold value is determined using machine learning from the measured respiratory rate or heart rate. V1 may be determined. The threshold value V1 is not limited to a constant value, and the threshold value V1 may have a gradient with respect to the time axis.

  In addition, machine learning may be used when setting the time range for calculating the specified value N1 or the statistical value based on age, height, weight, meal amount, or the like.

  In the first embodiment, in the binary comparison, “more than” includes both the case where the two values are equal and the case where one of the two values exceeds the other, but is not limited thereto. It may be “greater than”. That is, whether or not the case where the two values are equal can be arbitrarily changed depending on the setting of the reference value or the like, so there is no technical difference between “greater than” and “more than”. Similarly, “less than” may be “below”.

(Embodiment 2)
The event prediction system 10 according to the present embodiment is different from the event prediction system 10 according to the first embodiment in the contents of the sign determination process in the sign determination unit 121. Hereinafter, the same configurations as those of the first embodiment are denoted by common reference numerals, and description thereof is omitted as appropriate.

In the present embodiment, the sign determination unit 121 distinguishes the types of specific events for determining a sign.
In other words, the sign determination unit 121 not only determines the presence / absence of a specific event occurrence, but also distinguishes and determines the type of the specific event. That is, when the predictor determination unit 121 determines that there is a “predictor” of a specific event, which type of specific event is determined as “predictor” among various events that can occur in relation to the subject. Identify.

  Examples of types of specific events include terminal care, falls during walking, illness or injury requiring hospitalization, death, cognitive decline, epilepsy, getting up from bed (getting out of bed), excretion, and going to bed is there. However, specific events are not limited to specific categories such as terminal care and falls during walking. For example, long-term events (for example, several days to several weeks or more) or short-term events (for example, several minutes to several minutes) It may be an abstract classification such as whether the event occurs at or below time. Therefore, the predictor data generated by the predictor determination unit 121 indicates the presence / absence of the occurrence of a specific event, and the type of the specific event when there is a predictor of the occurrence.

  Specifically, the predictor determination unit 121 determines a predictor of occurrence of a specific event by using a determination condition associated with each type of specific event, distinguishing even the specific event type. The determination conditions are stored in the storage unit 14, for example. Here, in the sign determination process, a score is calculated for each determination condition, and when a plurality of determination conditions are satisfied, for example, a specific event corresponding to the determination condition with the highest score may be employed.

  In addition, the predictor determining unit 121 is not limited to the configuration that specifies which type of specific event is determined to be “predictive” after determining that there is a “predictor” of the specific event, and distinguishes the type of specific event. In addition, the presence or absence of a sign of occurrence of the specific event may be determined. In this case, it is possible to narrow down specific events that are targets for determination of presence / absence of signs. For example, in the case where a specific event of terminal care is to be determined for the presence / absence of a sign, the sign determination unit 121 does not determine the presence / absence of a sign other than the target specific event, such as a decline in cognitive function.

  In addition, the determination conditions used for the sign determination process may include conditions regarding external information input to the event prediction system 10 from the outside of the event prediction system 10 such as the degree of care of the subject, past medical history, and care records. Good. Thereby, the sign determination part 121 can aim at the improvement of the determination precision of a sign determination process based on information, such as a care level, a past history, and a care record.

  Further, based on the external information as described above, for example, the output unit 15 may change the order and mode of presentation of the determination result of the predictor determination unit 121 to the manager or the like. As an example, when the output unit 15 determines that “there is a sign” of a specific event for a plurality of subjects substantially simultaneously, based on the external information about each subject, the judgment results in order from the subject with the highest priority May be presented.

  In addition, the sign determination unit 121 may change the determination condition used for the sign determination process for each subject in accordance with past body movement data or a determination result. Thereby, the sign determination unit 121 can determine a sign of occurrence of a specific event in accordance with a change in life rhythm and a change in physical condition that are different for each subject, and can improve the determination accuracy of the sign determination process.

  Moreover, in the event prediction system 10, based on the determination result of the predictor determination process, the sign of the care performed by the caregiver (the person who cares for the target person) and the change in the state of the target person after the care is performed, etc. An event resulting from the determination result of the determination process may be used as feedback information. Accordingly, the event prediction system 10 can execute, for example, a suggestion of care to be performed corresponding to the determination result, confirmation of correctness of the determination result of the sign determination process, or the like based on the feedback information.

  Moreover, the event prediction system 10 may output the determination result of the sign determination process or the like to an external care service system or the like. Further, the output unit 15 may change the output destination of the determination result of the sign determination process according to the determination result of the sign determination process. As a result, the output unit 15 determines the sign determination process to an appropriate person according to the specific event, such as a doctor, a nurse, or a caregiver, depending on which type of specific event is determined to be “predictive”. Results can be presented.

  The configuration described in Embodiment 2 can be applied in appropriate combination with the various configurations (including modifications) described in Embodiment 1.

(Summary)
As described above, the event prediction system (10) according to the first aspect includes the acquisition unit (13) and the sign determination unit (121). An acquisition part (13) acquires the body motion data regarding a subject's body motion. The sign determination unit (121) determines a sign of occurrence of a specific event related to the subject based on the body motion data.

  According to this aspect, based on the body motion data, it is possible to determine a sign of occurrence of a specific event that may occur in association with the subject, that is, a “feeling” that appears in advance before the specific event occurs. Therefore, according to the event prediction system (10), there is an advantage that it is possible to determine a sign of occurrence of a specific event related to the subject.

  In the event prediction system (10) according to the second aspect, in the first aspect, the sign determination unit (121) determines a sign based on body motion data acquired in the past reference period.

  According to this aspect, since the body motion data acquired in the past reference period can be used, the sign can be determined relatively. Therefore, even if the body motion data varies for each subject, the sign determination accuracy can be improved. Reduction can be suppressed.

  In the event prediction system (10) according to the third aspect, in the first or second aspect, the sign determination unit (121) is based on the change in the physical activity amount of the subject based on the body motion data. Determine.

  According to this aspect, it is possible to improve the determination accuracy of the sign. That is, since a sign of occurrence of a specific event is likely to appear in a change in the amount of physical activity of the subject, the sign determination unit (121) makes a determination based on such a change in the amount of physical activity, thereby improving the determination accuracy. Improvements can be made.

  The event prediction system (10) according to the fourth aspect further includes a presence determination unit (112) in any one of the first to third aspects. The presence determination unit (112) determines the presence / absence of the subject in the target space (100) based on the body motion data.

  According to this aspect, the body motion data can be used for both the determination of the sign of the occurrence of the specific event and the determination of the presence / absence of the target person in the target space (100).

  The event prediction system (10) according to the fifth aspect further includes an output unit (15) in any one of the first to fourth aspects. The output unit (15) outputs the determination result of the sign determination unit (121).

  According to this aspect, the determination result of the sign determination unit (121) can be notified to, for example, an administrator.

  In the event prediction system (10) according to the sixth aspect, in any one of the first to fifth aspects, the sign determination unit (121) distinguishes the type of specific event for determining a sign.

  According to this aspect, since not only the presence / absence of the occurrence of the specific event but also the type of the specific event for determining the sign, the determination result of the sign determination unit (121) can be easily analyzed.

  The event prediction method according to the seventh aspect acquires body motion data related to the body motion of the subject, and determines a sign of occurrence of a specific event related to the subject based on the body motion data.

  According to this aspect, based on the body motion data, it is possible to determine a sign of occurrence of a specific event that may occur in association with the subject, that is, a “feeling” that appears in advance before the specific event occurs. Therefore, according to the event prediction method, there is an advantage that it is possible to determine the sign of occurrence of a specific event related to the subject.

  A program according to the eighth aspect is a program for causing a computer system to execute the event prediction method according to the seventh aspect.

  According to this aspect, based on the body motion data, it is possible to determine a sign of occurrence of a specific event that may occur in association with the subject, that is, a “feeling” that appears in advance before the specific event occurs. Therefore, according to the above program, there is an advantage that it is possible to determine a sign of occurrence of a specific event related to the subject.

  The various configurations (including modifications) of the event prediction system (10) according to the first and second embodiments are not limited to the above-described aspects, and the event prediction method, the program, and a non-temporary recording medium that records the program are used. It can be realized.

  About the structure which concerns on the 2nd-6th aspect, it is not an essential structure for the event prediction system (10) which concerns on a 1st aspect, and can be abbreviate | omitted suitably.

  Moreover, the sensor signal processing system (1) which concerns on a 9th aspect is provided with an acquisition part (13) and an acceleration calculating part (111). The acquisition unit (13) acquires body motion data from the measurement device (2). The measuring device (2) outputs body motion data relating to the body motion of the subject. The acceleration calculation unit (111) obtains acceleration regarding the body movement of the subject based on the body motion data.

  According to this aspect, based on the body motion data acquired from the measuring device (2), it is possible to obtain the acceleration of the subject's body movement. From the equation of motion, the product of acceleration and mass is force, and in the physical activity using the whole body of the subject, the weight of the subject corresponds to the mass. Assuming that the increase or decrease in the weight of the subject is negligible, the acceleration is considered to correspond to the muscle strength exerted by the subject in the physical activity. Therefore, the acceleration calculated by the acceleration calculation unit (111) corresponds to the intensity of physical activity, and the amount of physical activity in a certain period is represented by an accumulated value of acceleration in the certain period. Therefore, in the sensor signal processing system (1), the physical activity amount can be quantitatively evaluated by obtaining the acceleration from the body motion data, and the detection accuracy of the physical activity amount can be improved.

  In the sensor signal processing system (1) according to the tenth aspect, in the ninth aspect, the acceleration calculation unit (111) obtains the acceleration by performing a differentiation process on the plurality of body motion data arranged in time series.

  According to this aspect, the acceleration can be obtained by a relatively simple calculation called differential processing, and the processing load on the acceleration calculation unit (111) can be reduced.

  In the sensor signal processing system (1) according to the eleventh aspect, in the ninth or tenth aspect, the measuring device (2) is a non-contact type sensor (21) for detecting body movement data without contact with the subject. Have

  According to this aspect, the acceleration can be obtained without obstructing the movement of the subject for the acquisition of the body movement data.

  In the sensor signal processing system (1) according to the twelfth aspect, in the eleventh aspect, the sensor (21) is a radio wave type sensor that transmits and receives radio waves.

  According to this aspect, since minute movements of the subject can be detected by the sensor (21), the detection accuracy of the amount of physical activity can be further improved.

  In the sensor signal processing system (1) according to the thirteenth aspect, in the twelfth aspect, the measuring device (2) uses the frequency of the radio wave transmitted by the sensor (21) and the frequency of the radio wave received by the sensor (21). And the speed of movement of the subject is obtained.

  According to this aspect, since the measuring device (2) can obtain the speed of movement of the subject, the sensor signal processing system (1) can obtain the acceleration by a relatively simple process.

  The sensor signal processing system (1) according to the fourteenth aspect further includes an output unit (15) in any one of the ninth to thirteenth aspects. The output unit (15) outputs the calculation result of the acceleration calculation unit (111).

  According to this aspect, the calculation result of the acceleration calculation unit (111) can be notified to an administrator or the like, for example.

  The sensor signal processing method according to the fifteenth aspect acquires body motion data from the measurement device (2) that outputs body motion data related to the subject's body motion, and based on the body motion data, moves the subject's body. Find the acceleration for.

  According to this aspect, based on the body motion data acquired from the measuring device (2), it is possible to obtain the acceleration of the subject's body movement. From the equation of motion, the product of acceleration and mass is force, and in the physical activity using the whole body of the subject, the weight of the subject corresponds to the mass. Assuming that the increase or decrease in the weight of the subject is negligible, the acceleration is considered to correspond to the muscle strength exerted by the subject in the physical activity. Therefore, the acceleration obtained by the acceleration calculation process corresponds to the intensity of physical activity, and the amount of physical activity in a certain period is represented by an accumulated value of acceleration in the certain period. Therefore, in the sensor signal processing method, the physical activity amount can be quantitatively evaluated by obtaining the acceleration from the body motion data, and the detection accuracy of the physical activity amount can be improved.

  A program according to the sixteenth aspect is a program for causing a computer system to execute the sensor signal processing method according to the fifteenth aspect.

  According to this aspect, based on the body motion data acquired from the measuring device (2), it is possible to obtain the acceleration of the subject's body movement. From the equation of motion, the product of acceleration and mass is force, and in the physical activity using the whole body of the subject, the weight of the subject corresponds to the mass. Assuming that the increase or decrease in the weight of the subject is negligible, the acceleration is considered to correspond to the muscle strength exerted by the subject in the physical activity. Therefore, the acceleration obtained by the acceleration calculation process corresponds to the intensity of physical activity, and the amount of physical activity in a certain period is represented by an accumulated value of acceleration in the certain period. Therefore, in the above program, the physical activity amount can be quantitatively evaluated by obtaining the acceleration from the body motion data, and the detection accuracy of the physical activity amount can be improved.

  Various configurations (including modifications) of the sensor signal processing system (1) according to the first embodiment and the second embodiment are not limited to the above aspect, and the sensor signal processing method, the program, and the non-temporary recording medium recording the program Can be realized.

  The configurations according to the tenth to fourteenth aspects are not essential to the sensor signal processing system (1) according to the ninth aspect and can be omitted as appropriate.

DESCRIPTION OF SYMBOLS 10 Event prediction system 2 Measuring apparatus 13 Acquisition part 15 Output part 21 Sensor 111 Acceleration calculating part 112 Presence judgment part 121 Predictive judgment part

Claims (13)

An acquisition unit that acquires the body movement data from a measurement device that outputs body movement data related to the body movement of the subject;
An event prediction system comprising: a sign determination unit that determines a sign of occurrence of a specific event related to the subject based on the body motion data acquired in a past reference period. Based on the body movement data, further comprising an acceleration calculation unit for obtaining acceleration about the movement of the subject's body,
The event prediction system according to claim 1, wherein the sign determination unit determines the sign based on a change in the amount of physical activity of the subject based on the body motion data. The event prediction system according to claim 2, wherein the acceleration calculation unit obtains the acceleration by performing a differentiation process on the plurality of body motion data arranged in time series. The event prediction system according to claim 2, further comprising an output unit that outputs a calculation result of the acceleration calculation unit. The event prediction system according to claim 1, further comprising an output unit that outputs a determination result of the sign determination unit. The event prediction system according to any one of claims 1 to 5, wherein the measurement device includes a non-contact sensor that detects the body movement data without contact with the subject. The event prediction system according to claim 6, wherein the sensor is a radio wave type sensor that transmits and receives radio waves. The event prediction system according to claim 7, wherein the measurement device obtains the moving speed of the subject by comparing the frequency of the radio wave transmitted by the sensor and the frequency of the radio wave received by the sensor. The event prediction system according to claim 1, further comprising a presence determination unit that determines the presence / absence of the subject in the target space based on the body motion data. The event prediction system according to claim 1, wherein the sign determination unit distinguishes the type of the specific event for determining the sign. An acquisition unit that acquires the body movement data from a measurement device that outputs body movement data related to the body movement of the subject;
An acceleration calculation unit that obtains an acceleration for the movement of the subject's body based on the body motion data. A sensor signal processing system. Obtaining the body motion data from a measuring device that outputs body motion data relating to the body motion of the subject,
An event prediction method for determining a sign of occurrence of a specific event related to the subject based on the body motion data acquired in a past reference period. A program for causing a computer system to execute the event prediction method according to claim 12.

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