JP6435176B2 - Physical information acquisition apparatus, physical information acquisition method, and program - Google Patents

Description

  The present invention relates to a physical information acquisition device, a physical information acquisition method, and a program.

  For example, as a method for measuring physical information such as heart rate and respiration rate during sleep or the like, there is a method using a measuring device that performs measurement in contact or non-contact. For example, as a device for acquiring physical information by contact, a bedside monitor that collects a patient's electrocardiogram, respiratory rate, blood pressure value, brain wave, etc., a patient identification device that reads an identifier unique to the patient, and a plurality of bedside monitors And a medical data recording device comprising a centralized monitoring device each connected to the identification device. As another example, a system having a terminal and a server is known. In this system, the terminal receives sleep biometric information detected from the biometric information sensor and transmits sleep biometric information. The server receives sleep biological information transmitted from the terminal. The server stores a sleep analysis unit that receives sleep biometric information transmitted from the terminal, a sleep analysis unit that analyzes sleep state based on the received sleep biometric information, and obtains sleep analysis information, and sleep analysis information And a storage unit. Moreover, a server has a transmission part which transmits sleep analysis information to a terminal or a registration apparatus, and a registration part which registers a terminal (for example, refer patent document 1, 2).

  As a device for acquiring body information in a non-contact manner, a device having a receiving unit that receives an electromagnetic wave reflected from a living body and a feature extracting unit that extracts a plurality of features of a waveform of a received signal according to a predetermined pattern recognition rule is known. It has been. The apparatus further outputs an expiration / inspiration time estimation unit that estimates an expiration time and an inspiration time of a living body based on positions on the time axis of a plurality of features, and an estimated expiration time and an inspiration time. And an output unit. There is also known a microphone array that estimates a sound reception signal at an arbitrary position in the dimension axis direction with two microphones per dimension (see, for example, Patent Documents 3 and 4).

Japanese Patent Laid-Open No. 06-292657 JP 2010-099173 A JP 2013-202216 A JP 2001-045590 A

  However, the technique for measuring non-contact body information (heart rate, respiration rate) as described above is compatible only with a single measurement target. For example, when there are two measurement objects, a heart rate component and a respiratory component for two persons can be obtained as sensor signals, but there is a problem that it is difficult to specify which subject is the subject.

  Although it is conceivable to perform measurement by installing measurement devices for detecting physical information such as a person's heart rate for the number of subjects, the cost for the necessary measurement devices increases. For this reason, for example, when two people are sleeping at a close position, the signal obtained from the measurement device contains two people's breathing, heart rate components, etc., and it cannot be determined which information is individual. It is difficult to measure the sleep state.

  According to one aspect, an object of the present invention is to identify which subject is a set of heart rate and respiratory rate of a plurality of subjects obtained by electromagnetic wave measurement.

The physical information measuring device which is one aspect has a specific part. The identifying unit is configured to measure a time lag or amplitude difference of respiratory sounds at the different measurement locations from among a plurality of subject respiratory sounds acquired at different measurement locations, and a subject to be measured among the plurality of subjects with respect to the measurement locations. The respiratory sound of the subject to be measured is identified based on the position of the subject, and the first respiratory rate of the identified respiratory sound and the respiratory sound of the subject to be measured are identified. The specifying unit specifies at least one set of a heart rate and a second respiration rate for each subject from the movements of the plurality of subjects acquired from the reflected waves of the electromagnetic waves irradiated to the range including the plurality of subjects. The specifying unit specifies a set including the second respiration rate closest to the first respiration rate specified from the set as the heart rate and the respiration rate of the subject to be measured.

  According to one embodiment, it is possible to specify which test subject is a set of heart rate and respiration rate of a plurality of subjects obtained by measuring electromagnetic waves.

It is a figure which shows an example of the usage condition of the physical information measuring device by 1st Embodiment. It is a figure which shows an example of the hardware constitutions of the physical information measuring device by 1st Embodiment. It is a block diagram which shows an example of a functional structure of the control apparatus by 1st Embodiment. It is a figure explaining an example of the method of extracting the audio | voice of a specific direction with the microphone array by 1st Embodiment. It is a figure which shows an example of the method of specifying the respiration rate from the audio | voice containing the test subject's breathing sound extracted by 1st Embodiment. It is a figure which shows the example of a microwave sensor signal by 1st Embodiment. It is a figure explaining the general example of a heartbeat fluctuation. It is a figure explaining the example of analysis of heart rate fluctuation by a 1st embodiment. It is a figure which shows an example of the test subject registration information by 1st Embodiment. It is a figure which shows an example of the 1st respiration rate information by 1st Embodiment. It is a figure which shows an example of the detected value information by 1st Embodiment. It is a figure which shows an example of the test subject physical information by 1st Embodiment. It is a flowchart which shows an example of the main processes of the physical information measuring device by 1st Embodiment. It is a flowchart which shows an example of the detail of the test subject registration process by 1st Embodiment. It is a flowchart which shows an example of the detail of the microphone array signal analysis process by 1st Embodiment. It is a flowchart which shows an example of the detail of the microwave sensor signal analysis process by 1st Embodiment. It is a flowchart which shows an example of the heartbeat respiration component classification | category process by 1st Embodiment. It is a figure which shows an example of the usage condition of the body information measuring device by 2nd Embodiment. It is a flowchart which shows an example of a process of the physical information measuring device by 2nd Embodiment. It is a flowchart which shows an example of the detail of the microphone signal analysis process by 2nd Embodiment. It is a flowchart which shows an example of the heartbeat respiration component classification | category process by 2nd Embodiment.

(First embodiment)
Hereinafter, the physical information measuring apparatus 10 according to the first embodiment will be described with reference to the drawings. FIG. 1 is a diagram illustrating an example of a usage state of the physical information measuring apparatus 10 according to the first embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration of the physical information measuring apparatus 10 according to the first embodiment.

  As shown in FIG. 1, the physical information measuring device 10 measures physical information of at least one predetermined subject of a plurality of subjects (for example, subjects A and B) sleeping in the same room 11 or the like, for example. It is a body information measuring device used when doing.

  As shown in FIGS. 1 and 2, the body information measuring device 10 includes a microwave sensor 13, a microphone array 15, and a control device 50. The microwave sensor 13 is a sensor that irradiates a measurement target with a microwave, detects a reflected wave, and outputs a sensor signal corresponding to a change in the frequency of the reflected wave. The moving object is detected by the microwave sensor 13 using the Doppler effect.

  The microphone array 15 is a device that includes a plurality of microphones, collects voices, and specifies the direction of the sound source based on the time lag of the voices collected by the plurality of microphones. In the example of FIG. 1, the microphone array 15 includes two microphones 15-1 and 15-2.

  The control device 50 is a device that controls the operation of the physical information measuring device 10 and includes, for example, a processor 52 and a memory 54. The processor 52 is an arithmetic processing device that performs various arithmetic processes in the control device 50. The memory 54 is a storage device that stores information. The memory 54 may store a program 56 that controls the operation of the physical information measuring apparatus 10 and a database 58 that includes various types of information. The processor 52 may control the operation of the physical information measuring device 10 by reading and executing the program 56 from the memory 54.

  The control device 50 may be a standard computer including an input device such as a touch panel and a keyboard, an output device such as a display device, an external storage device, and a drive device for a portable storage medium. The program 56 and the database 58 may be stored in an external storage device or the like.

  In the example illustrated in FIG. 1, the microwave sensor 13 irradiates the microwave irradiation range 17 including the subject A and the subject B with microwaves and detects reflected waves. As in the example of FIG. 1, when there are a plurality of persons, such as when two persons are sleeping in the microwave irradiation range 17, for example, sensors in which a plurality of signals indicating heartbeat (beat) and respiration are mixed. A signal SRF is detected. In FIG. 1, for convenience, a reflected wave from the microwave irradiation range 17 is shown as a sensor signal SRF.

  As for the microphone 15-1 of the microphone array 15, each of the microphones 15-2 collects a sound including a breathing sound from the subject A and a breathing sound from the subject B. Then, the microphone 15-1 outputs a signal including a respiratory signal SA1 corresponding to the respiratory sound from the subject A and a respiratory signal SB1 corresponding to the respiratory sound from the subject B. Similarly, the microphone 15-2 outputs a signal including a respiratory signal SA2 corresponding to the respiratory sound from the subject A and a respiratory signal SB2 corresponding to the respiratory sound from the subject B. In FIG. 1, for convenience, voices from the subjects A and B are represented as respiratory signals SA1, SA2, SB1, and SB2.

  The physical information measuring device 10 is identified as that of the subject A from a plurality of, for example, the breathing sounds of the subjects A and B acquired at different measurement locations such as two microphones of the microphone array 15 and the location of the subject A with respect to the measurement location. The first breathing rate of the breathing sound is identified. For example, the first respiratory rate may be a respiratory rate per unit time (for example, 1 minute) of the subject A. At this time, the body information measuring apparatus 10 determines, for example, the heart rate and the second respiratory rate for each subject from the movements of the subjects A and B acquired from the reflected waves of the electromagnetic waves irradiated to the range including the subjects A and B. At least one set is identified. The physical information measuring apparatus 10 determines a set including a second respiration rate that approximates the first respiration rate, which is specified from the set of heart rate and second respiration rate for each subject, for example, the subject A. Identify heart rate and breathing rate. Note that the above-described processing may be executed by, for example, the specifying unit 62 described later, which is realized by the processor 52 of the control device 50 reading and executing the program 56 from the memory 54.

  FIG. 3 is a block diagram illustrating an example of a functional configuration of the control device 50 according to the first embodiment. As illustrated in FIG. 3, the control device 50 includes a specifying unit 62. The specifying unit 62 includes a subject registration unit 64, a moving object detection unit 66, a respiratory sound analysis unit 68, a sensor signal analysis unit 70, and a heartbeat / respiration component classification unit 72.

  The subject registration unit 64 receives, for example, subject identification information, the subject's position with respect to the direction in which the microphones 15-1 and 15-2 of the microphone array 15 are arranged, and stores them in the memory 54 as, for example, subject registration information 110 described later. . The moving object detection unit 66 receives, for example, a sensor signal SRF from the microwave sensor 13. The breathing sound analysis unit 68 receives a voice signal including a breathing sound from the microphone array 15 and separates a plurality of breathing signals based on a time lag of the voice acquired for each microphone, thereby at least one subject. The first respiration rate is calculated. The respiratory sound analysis unit 68 stores the calculated first respiratory rate in the memory 54 as, for example, first respiratory rate information 112 described later.

  The sensor signal analysis unit 70 performs frequency analysis of a signal indicating the movement of the moving object detected by the moving object detection unit 66 and calculates the heart rate and the second respiration rate of the same subject. The heart rate and the second respiratory rate of the same subject may be referred to as a set of detection values. At this time, the second heart rate is detected as fluctuation of the heart rate. The sensor signal analysis unit 70 stores a set of detection values in the memory 54 as detection value information 114 described later, for example.

  Based on the first respiratory rate obtained by the respiratory sound analyzing unit 68 and the second respiratory rate obtained by the cardiac respiratory component classifying unit 72, the heart rate respiratory component classifying unit 72 performs predetermined processing on the microphone array 15. The heart rate of the subject located at the position of is identified. For example, the second respiration rate that approximates the first respiration rate is identified from the obtained second respiration rates, and the corresponding heart rate is the heart rate of the subject corresponding to the first respiration rate. Is identified. It is preferable that the approximation is the closest to the first respiration rate among the plurality of second respiration rates, or the difference from the first respiration rate is a predetermined value or less. The heartbeat / respiration component classifying unit 72 stores, for example, the specified set of detection values in the memory 54 as subject physical information 116 described later.

  Next, a method for measuring the first respiratory rate of the subject using the microphone array 15 will be described with reference to FIGS. 4 and 5. FIG. 4 is a diagram for explaining a method of extracting sound in a specific direction using a microphone array. FIG. 5 is a diagram illustrating an example of a method for specifying the respiratory rate from the extracted voice including the breathing sound of the subject.

  In the example of FIG. 4, the microphone array 15 is arranged in a space where two subjects A and B exist. The microphone array 15 has microphones 15-1 and 15-2 arranged at different positions, and each microphone collects sound including the breathing sound of the subject A and the breathing sound of the subject B. In FIG. 4, for the sake of convenience, the respiratory sound signal of the subject A collected by the microphone 15-1 is the respiratory signal SA1, and the respiratory sound signal of the subject A collected by the microphone 15-2 is the respiratory signal SA2. Similarly, the signal of the breathing sound of the subject B picked up by the microphone 15-1 is the breathing signal SB1, and the signal of the breathing sound of the subject B picked up by the microphone 15-2 is the breathing signal SB2. In the example of FIG. 4, the respiration signal SA1 and the respiration signal SA2 are measured, for example, at times different by time t2. For example, the respiration signal SB1 and the respiration signal SB2 are measured at a time different from the time t1. Therefore, using this measurement time difference and the arrangement of a plurality of microphones, a respiratory signal including the breathing sound of the subject is identified and extracted. You may make it use the amplitude difference of the measured signal other than a measurement time difference.

  A specific method for specifying and extracting a respiratory signal of a subject at a predetermined position can be a method using an existing technique. For example, a method using the principle of beam forming, independent component analysis, or blind source separation using signal sparsity can be considered. For example, when the principle of beam forming is used, sound source separation is performed by setting an angle indicating the direction of the subject with respect to the microphone array in advance. Blind sound source separation does not require information such as microphone position or sound source. Further, as described in Patent Document 4, a method of estimating a received sound signal at an arbitrary position in the dimension axis direction using two microphones per dimension may be used. The method for extracting and specifying the sound source is not limited to the above, and other known methods may be used.

  The example shown in FIG. 5 is an example of a respiratory signal in which a subject is identified and separated by identifying the position of a sound source using the above-described technique. Hereinafter, the case where the physical information of the subject A is detected will be described as an example.

  In FIG. 5, a respiratory signal example 80 shows an example of the respiratory signal SA of the subject A with the horizontal axis representing time and the vertical axis representing amplitude. In the respiration signal SA, for example, a feature point represented by a respiration sound feature point 82 is extracted, and when the horizontal axis indicates time and the vertical axis indicates the time interval of the respiration sound feature point, a respiration sound interval time change 84 is obtained. A breathing sound interval time change 84 indicates a time change of the breathing interval BRIA of the subject A. Further, the breathing sound interval frequency time change 86 shows a time change with the reciprocal of the breathing sound interval BRIA as the breathing sound interval frequency BRFA. In the breathing sound interval frequency change 86, the horizontal axis represents time and the vertical axis represents frequency. As described above, the first respiration rate of the subject A who is one of the plurality of subjects is measured as a time change of the respiration frequency. The first respiratory rate is a respiratory rate acquired by analyzing a signal obtained by the microphone array 15, and the same name is used for different subjects.

  Next, a method for measuring the heart rate and the second respiration rate with the microwave sensor 13 will be described with reference to FIGS. The second respiration rate is a respiration rate acquired by analyzing a signal obtained by the microwave sensor 13, and the same name is used for different subjects. FIG. 6 is a diagram illustrating an example of a microwave sensor signal according to the first embodiment. As shown in FIG. 6, the microwave sensor signal example 90 shows an example of the sensor signal SRF with the horizontal axis representing time and the vertical axis representing sensor output voltage.

  The sensor signal analysis unit 70 obtains a sensor signal frequency spectrum example 92 by removing unnecessary components from the sensor signal SRF using, for example, a low-pass filter and performing frequency analysis. The sensor signal frequency spectrum example 92 is an example of a sensor signal frequency spectrum in which the horizontal axis represents frequency and the vertical axis represents signal intensity. In the sensor signal frequency spectrum example 92, microwave respiration frequencies BRF1 and BRF2 are obtained as respiration frequencies, and heart rate frequencies HRF1 and HRF2 are obtained as heart rate frequencies.

  FIG. 7 is a diagram for explaining a general example of heartbeat fluctuation. In FIG. 7, the heartbeat signal example 94 is an example of a time change of the heartbeat signal with the horizontal axis representing time and the vertical axis representing signal amplitude. For example, a heartbeat interval T is calculated from the heartbeat signal example 94. The heartbeat fluctuation spectrum example 96 shows an example of the result of frequency analysis of the time change of the heartbeat interval T calculated from the heartbeat signal example 94. In the heartbeat fluctuation spectrum example 96, the horizontal axis represents frequency and the vertical axis represents spectral density. For example, in the heartbeat fluctuation spectrum example 96, a relatively low frequency band, for example, 0.05 to 0.15 Hz is a low frequency region (LF) 98, and a frequency band higher than the low frequency region 98 is, for example, 0.15 to 0.4 Hz. The region (HF) 99 is called. The heart rate fluctuation appears as a peak in the low frequency region 98 and the high frequency region 99. It is known that the peak Respiratory Sinus Arrhythmia (RSA) in the high frequency region 99 is a fluctuation representing the respiratory rate. Therefore, the respiratory rate corresponding to the heart rate is specified by analyzing this RSA.

  FIG. 8 is a diagram for explaining an analysis example of heartbeat fluctuation according to the first embodiment. In FIG. 8, the heartbeat interval time change 102 is extracted as follows, for example. First, by extracting a signal in a predetermined frequency band centered on the heartbeat frequency HRF1 analyzed in the sensor signal frequency spectrum example 92 from the sensor signal SRF in FIG. 6 by using a bandpass filter, the heartbeat signal example shown in FIG. A heart rate component signal SHRF1 similar to 94 is obtained. Next, by extracting the heartbeat interval RRI1 from the obtained heartbeat component signal SHRF1, a signal such as a heartbeat interval time change 102 in FIG. 8 is obtained. In the heartbeat interval time change 102, the horizontal axis indicates time, and the vertical axis indicates the time change of the heartbeat interval RRI1.

  A heartbeat fluctuation spectrum SP_HRF1 shown in the heartbeat fluctuation spectrum example 104 is obtained by frequency-converting the time change of the heartbeat interval RRI1. In the heartbeat fluctuation spectrum example 104, the horizontal axis represents frequency and the vertical axis represents signal intensity. The heartbeat fluctuation spectrum SP_HRF1 has a peak at the heartbeat fluctuation respiratory frequency BRHRF1. The respiration rate corresponding to the heartbeat fluctuation respiration frequency BRHRF1 is specified as the second respiration rate corresponding to the heartbeat interval RRI1. Thereby, a set of detection values of the heart rate and the second respiratory rate of the same subject is specified.

  As described above, the first respiratory rate of the subject is specified based on the detection result of the microphone array 15. Further, based on the detection result of the microwave sensor 13, at least one set of detection values of the heart rate and the second respiratory rate of the same subject is specified. By extracting a second respiration rate that approximates the first respiration rate, the heart rate of the subject is specified. Further, according to the analysis based on FIG. 8, the heart rate frequency HRF2 and the heart rate fluctuation respiration frequency BRHRF2 in the sensor signal frequency spectrum example 92 of FIG. 6 are obtained as a set of detection values of another set of heart rate and second respiration rate. Identified. Of course, it is also possible to analyze the heart rate frequency HRF2 in the same manner as the heart rate frequency HRF1 and specify a set of detection values.

  Next, registration information and processing result information for performing the above processing will be described. FIG. 9 is a diagram illustrating an example of subject registration information according to the first embodiment. As illustrated in FIG. 9, the subject registration information 110 is information indicating a subject to be subjected to physical information measurement, and has subject identification IDentification (ID) and a position. The position is, for example, information indicating whether the subject is on the left or right side with respect to the up / down / left / right direction defined in the microphone array 15, and may be represented by an angle with respect to the reference direction. The subject registration information 110 is registered by the subject registration unit 64 and stored in the database 58, for example.

  FIG. 10 is a diagram illustrating an example of the first respiration rate information 112 according to the first embodiment. As shown in FIG. 10, the first respiration rate information 112 is information indicating a first respiration rate for each detection time calculated for a subject based on the detection result of the microphone array 15. The first respiratory rate information 112 has a subject ID, a detection time, and a first respiratory rate. For example, the first respiration rate information 112 is preferably recorded sequentially while the measurement is continued. The first respiratory rate information 112 is stored in the database 58, for example.

  FIG. 11 is a diagram illustrating an example of the detection value information 114 according to the first embodiment. The detection value information 114 is information indicating a set of detection values of the second respiratory rate and the heart rate of the same subject analyzed based on the detection result by the microwave sensor 13. The detection value information 114 is stored in the database 58, for example.

  FIG. 12 is a diagram illustrating an example of the subject physical information 116 according to the first embodiment. The subject physical information 116 is information in which the subject ID of the subject to be measured and the respiration rate and heart rate of the subject specified by the physical information measuring device 10 according to the present embodiment are recorded for each detection time. The subject physical information 116 is recorded in the database 58, for example. Further, the subject physical information 116 may be output as a measurement result by the physical information measuring device 10.

  Hereinafter, the processing by the physical information measuring apparatus 10 according to the first embodiment will be further described with reference to the flowchart. FIG. 13 is a flowchart showing an example of main processing of the physical information measuring apparatus 10 according to the first embodiment. The main processing in the physical information measuring apparatus 10 is performed, for example, by the processor 52 reading and executing the program 56. In the following example, description will be made assuming that each block illustrated in FIG. 3 is executed.

  As shown in FIG. 13, in the physical information measuring device 10, first, the subject registration unit 64 performs a subject registration process (S121). For example, the subject registration unit 64 of the control device 50 accepts subject information input from a keyboard (not shown) or the like and records it in the subject registration information 110. The position recorded in the subject registration information 110 indicates the position of the subject with respect to the microphone array 15, which side is relative to the specific direction of the microphone array 15, and is perpendicular to the perpendicular line passing through the midpoint between the two microphones of the microphone array. You may make it include the angle which is made. The subject information stored in the subject registration information 110 may include information on a plurality of subjects. Details of the processing of S121 will be described later.

  The moving object detection unit 66 receives, for example, a sensor signal SRF from the microwave sensor 13 according to the reflected wave of the irradiated microwave. At this time, when there is a moving object in the irradiation range, the moving object detection unit 66 receives a signal indicating a frequency change according to the moving speed of the moving object. Further, the respiratory sound analysis unit 68 receives an audio signal including, for example, the detected respiratory signals SA1, SA2, SB1, and SB2 from the microphone array 15 (S122).

  The respiratory sound analysis unit 68 analyzes the received audio signal (S123). That is, the respiratory signal (for example, respiratory signal SA or respiratory signal SB) including the breathing sound of the subject set in S121 is identified from the received audio signal, and the first respiratory rate is identified based on the respiratory interval. . The respiratory sound analysis unit 68 records the specified first respiratory rate together with the time, for example, in the first respiratory rate information 112 every predetermined time. Details of the processing of S123 will be described later.

  As described above, the sensor signal analysis unit 70 identifies the second respiration rate and heart rate of the same subject from the sensor signal SRF received from the microwave sensor 13, and stores them in the detection value information 114 (S124). ). Details of the processing of S124 will be described later.

  The heart rate respiration component classification unit 72 compares the recorded first respiration rate information 112 and the detection value information 114, and has a second respiration rate approximate to the first respiration rate, the set of detection values. Is determined as the measurement value of the subject set in the subject registration information 110 (S125). The heartbeat / respiration component classification unit 72 records the specified detection value information 114 as measurement information of the specified subject in the subject physical information 116 and outputs it (S126).

  FIG. 14 is a flowchart illustrating an example of details of the subject registration process in S121 of FIG. As illustrated in FIG. 14, the subject registration unit 64 determines whether or not the subject registration information 110 has been registered (S131). If it has been registered (S131: YES), the subject registration information 110 is read from the database 58 (S132). At this time, the read subject registration information 110 may be, for example, information including a position used at the time of the previous measurement, or may be information in which only the subject ID is registered. If not registered (S131: NO), the subject information is input as subject registration information 110 (S133).

  The subject registration unit 64 inputs, as the position of the subject registration information 110, whether the subject to be measured is on the left or right side (sleeping) toward the microphone array 15, for example (S134). The subject registration unit 64 returns to FIG. 13 and advances the process to S122.

  FIG. 15 is a flowchart showing an example of details of the microphone array signal analysis processing in S123 of FIG. As shown in FIG. 15, the respiratory sound analysis unit 68 classifies at least the respiratory signal of the subject to be measured from the audio signal acquired by the microphone array 15. For example, the respiratory signal SA is extracted from the acquired respiratory sound and the position of the subject A. Similarly, the respiration signal SB of the subject B is extracted (S141). The classification is performed using, for example, the method described with reference to FIG.

  The respiratory sound analysis unit 68 detects a respiratory sound feature point from at least the respiratory signal of the subject to be measured, for example, as shown in the respiratory signal example 80 of FIG. 5 (S142). The respiratory sound analysis unit 68 calculates a respiratory sound interval for each breath from the detected respiratory sound feature points (S143). By the process of S143, for example, the breathing interval BRIA of the subject A and the breathing interval BRIB of the subject B shown in the breathing sound interval time change 84 of FIG. 5 are obtained.

  The respiratory sound analysis unit 68 calculates the first respiratory rate of the subject to be measured from the calculated respiratory sound interval (S144). In the process of S144, for example, the respiratory rate BRA for a predetermined time is calculated from the breathing sound interval of the subject A calculated in S143. Similarly, the respiratory rate BRB of the subject B is obtained. The respiratory sound analysis unit 68 records the obtained respiratory rate RBA in the first respiratory rate information 112 corresponding to the subject A together with the detection time.

  The respiratory sound analysis unit 68 calculates the respiratory sound interval frequency as the reciprocal of the calculated respiratory sound interval (S145). In the process of S145, for example, the breathing sound interval frequency BRFA of the subject A shown in the breathing sound interval frequency change 86 of FIG. 5 is obtained. Similarly, the breathing sound interval frequency BRFB of the subject B is obtained. The respiratory sound analysis unit 68 may record the respiratory rate RBB obtained for the subject B in the first respiratory rate information 112 corresponding to the subject B together with the detection time. The respiratory sound analysis unit 68 returns the process to the process of FIG.

  FIG. 16 is a flowchart showing an example of details of the microwave sensor signal analysis processing in S124 of FIG. As shown in FIG. 16, the sensor signal analysis unit 70 performs frequency analysis on the sensor signal SRF (S151). The sensor signal analysis unit 70 calculates a frequency spectrum by frequency analysis (S152). For example, the sensor signal analysis unit 70 performs frequency analysis on the sensor signal SRF of the microwave sensor signal example 90 in FIG. 6 to obtain a frequency spectrum of the sensor signal frequency spectrum example 92, for example. The sensor signal analysis unit 70 calculates heart rate frequencies HRF1 and HRF2 that appear as peaks in the obtained frequency spectrum (S153). The heart rate frequencies HRF1 and HRF2 obtained here are values calculated from the movements of the subjects A and B in this example, but at this point it is unknown which heart rate frequency belongs to which subject. is there.

  The sensor signal analysis unit 70 extracts a heartbeat component signal from the sensor signal SRF by bandpass filtering using the heartbeat frequency obtained by frequency analysis as the center frequency (S154). For example, the sensor signal analysis unit 70 extracts the heartbeat component signal SHRF1 by bandpass filter processing with the heartbeat frequency HRF1 as the center frequency. Similarly, the sensor signal analysis unit 70 extracts the heartbeat component signal SHRF2 by bandpass filter processing with the heartbeat frequency HRF2 as the center frequency.

  The sensor signal analysis unit 70 extracts a heartbeat feature point in each of the obtained heartbeat component signals SHRF1 and SHRF2, and calculates a heartbeat interval (S155). For example, the heartbeat interval time change 102 in FIG. 8 shows the time change of the heartbeat interval RRI1 obtained based on the bandpass filter processing centered on the heartbeat frequency HRF1. A similar analysis is performed on the heart rate component signal SHRF2, and the time change of the heart rate interval RRI2 is calculated.

  The sensor signal analysis unit 70 performs frequency analysis on the temporal changes of the heartbeat intervals RRI1 and RRI2 obtained in S155 (S156). The sensor signal analysis unit 70 calculates a heartbeat fluctuation spectrum by the process of S156 (S157).

  The sensor signal analysis unit 70 detects the heartbeat fluctuation respiration frequency in the obtained heartbeat fluctuation spectrum (S158), and returns the processing to the process of FIG. For example, the heartbeat fluctuation spectrum example 104 shown in FIG. 8 shows a heartbeat fluctuation spectrum SP_HRF1 based on a signal extracted with the heartbeat frequency HRF1 as the center frequency. In this example, a heartbeat fluctuation respiratory frequency BRHRF1 appears. By this analysis, the heart rate frequency HRF1 and the heart rate fluctuation respiration frequency BRHRF1 are specified as a set of detection values that are measurement values of the same subject. The sensor signal analysis unit 70 records a set of detection values specified for the heartbeat frequency HRF1 in the detection value information 114 as a detection value α, for example.

  Similarly, the heart rate frequency HRF2 and the heart rate fluctuation respiration frequency BRHRF2 are specified as a set of detection values that are measurement values of the same subject. The sensor signal analysis unit 70 may record a set of detection values specified for the heartbeat frequency HRF2 in the detection value information 114 as, for example, a detection value β different from the detection value α.

  FIG. 17 is a flowchart showing an example of the heartbeat / respiration component classification process in S125 of FIG. As shown in FIG. 17, the heartbeat / respiration component classification unit 72 includes differences X1, X2, Y1, and Y2 between the heartbeat fluctuation respiratory frequencies BRHRF1 and BRHRF2 and the breathing sound interval frequencies BRFA and BRFB based on the detection signals from the microphone array 15. Is calculated (S161). Here, the differences X1, X2, Y1, and Y2 are calculated as shown in the following formulas 1 to 4, for each value of the same detection time in the first respiratory rate information 112 and the detection value information 114, for example. .

X1 = | BRFA−BRHRF1 | (Formula 1)
X2 = | BRFA−BRHRF2 | (Expression 2)
Y1 = | BRFB−BRHRF1 | (Formula 3)
Y2 = | BRFB−BRHRF2 | (Formula 4)

  The heartbeat / respiration component classification unit 72 determines whether or not the difference X1 <the difference X2 and the difference Y1> the difference Y2 (S162). When the determination result in S162 is YES, the heartbeat fluctuation respiratory frequency BRHRF1 approximates the breathing sound interval frequency BRFA of the subject A, and the heartbeat fluctuation respiratory frequency BRHRF2 approximates the breathing sound interval frequency BRRFB of the subject B. It is determined. Therefore, it is determined that the heartbeat interval RRI1 corresponding to the heartbeat fluctuation respiratory frequency BRHRF1 is the heartbeat interval frequency RRIA of the subject A, and the heartbeat interval RRI2 corresponding to the heartbeat fluctuation breathing frequency BRHRF2 is the heartbeat interval frequency RRIB of the subject B. (S163).

  When the determination result in S162 is NO, the heartbeat fluctuation respiratory frequency BRHRF2 approximates the breathing sound interval frequency BRFA of the subject A, and the heartbeat fluctuation respiratory frequency BRHRF1 approximates the breathing sound interval frequency BRRFB of the subject B. It is determined. Therefore, it is determined that the heartbeat interval RRI2 corresponding to the heartbeat fluctuation respiratory frequency BRHRF2 is the heartbeat interval frequency RRIA of the subject A, and the heartbeat interval RRI1 corresponding to the heartbeat fluctuation breathing frequency BRHRF1 is the heartbeat interval frequency RRIB of the subject B. (S164).

  The heartbeat / respiration component classification unit 72 calculates a heart rate HRA corresponding to the specified heartbeat interval frequency RRIA of the subject A and a heart rate HRB corresponding to the heartbeat interval frequency RRIB of the subject B (S165). Return processing.

  As described above, according to the body information measuring apparatus 10 according to the first embodiment, the following processing is performed. That is, the first breathing sound identified as the breathing sound of the subject to be measured from the breathing sounds of the plurality of subjects acquired at different measurement locations and the position of the subject to be measured among the plurality of subjects with respect to the measurement location. The respiratory rate is identified. In addition, at least one set of detection values for the heart rate and the second respiration rate for each subject is identified from the movements of the plurality of subjects acquired from the reflected waves of the electromagnetic waves irradiated to the range including the plurality of subjects. Furthermore, a set of detection values that are specified from the specified set of detection values and include a second respiration rate that approximates the first respiration rate are specified as the heart rate and respiration rate of the subject to be measured. The specified heart rate and respiratory rate are recorded for each subject, for example.

  As described above, according to the physical information measuring apparatus 10 according to the first embodiment, the heart rate of the same subject and the second value are obtained from the sensor signal SRF from the microwave sensor 13 including the physical information of a plurality of subjects. A set of respiratory rates is identified. Further, the first respiration rate of the subject to be measured is specified by analyzing the detection signals including the breathing sounds of the plurality of subjects from the microphone array 15 and referring to the positions of the subjects input in advance. By specifying the second respiration rate that approximates the first respiration rate, the respiration rate and heart rate of the subject to be measured that could not be specified only by the signal of the microwave sensor 13 including the body information of a plurality of subjects Can be specified. Since the measurement uses the microwave sensor 13 and the microphone array 15 that can be performed without contact, body information in a natural state can be acquired without bothering the subject. Further, by using the microwave sensor 13, measurement in a dark place is possible, and body information at the time of sleeping of the subject can be easily acquired.

  At this time, the time lag of the measurement results at different measurement locations of the breathing sounds of the plurality of subjects is analyzed, the breathing sound corresponding to the position of the subject to be measured is specified, and the first respiratory rate is calculated. . Further, the second respiration rate is calculated from the fluctuation frequency of the heart rate obtained by the electromagnetic wave analysis. Therefore, it is possible to reliably specify the respiration rate and heart rate of the subject to be measured.

  When classifying the set of detection values of the subject to be measured, the set of detection values including the second respiration rate closest to the first respiration rate specified by the microphone array 15 is used as the measurement value of the subject to be measured. You may make it classify | categorize as there, and can acquire the measured value of the test subject reliably.

(Second Embodiment)
Hereinafter, the physical information measuring apparatus 200 according to the second embodiment will be described. In 2nd Embodiment, about the structure and operation | movement similar to the physical information measuring device 10 by 1st Embodiment, the same code | symbol is attached | subjected and duplication description is abbreviate | omitted.

  FIG. 18 is a diagram illustrating an example of a usage state of the physical information measuring apparatus 200 according to the second embodiment. As shown in FIG. 18, the physical information measuring apparatus 200 measures physical information of at least one predetermined subject of a plurality of subjects (for example, subjects A and B) sleeping in the same room 11 or the like, for example. It is a body information measuring device used when doing.

  As shown in FIG. 18, the physical information measuring device 200 is different from the physical information measuring device 10 according to the first embodiment in that a microphone 16 is provided instead of the microphone array 15. That is, the hardware configuration of the body information measuring device 200 is a configuration including the microwave sensor 13, the microphone 16, and the control device 50, for example. In the control device 50, a program for performing processing as the physical information measuring device 200 according to the second embodiment is stored in the memory 54, and various processes are performed by being read and executed by the processor 52. May be.

  The microphone 16 is installed in the vicinity of the subject to be measured. For example, the breathing sound of the subject to be measured can be acquired in a distinguishable manner compared to the breathing sounds of other subjects existing in the irradiation range of the microwave sensor 13. It is preferable to install as described above.

  Hereinafter, processing by the physical information measuring apparatus 200 according to the second embodiment will be described with reference to a flowchart. The main processing of the physical information measuring device 200 according to the second embodiment is the same as the processing of the physical information measuring device 10 in FIG. Since the functional configuration of the physical information measuring device 200 is equivalent to the functional configuration of the physical information measuring device 10, in the following description, the functional configuration of the physical information measuring device 200 is shown in FIG. A configuration similar to that of the physical information measuring apparatus 10 is used.

  FIG. 19 is a flowchart illustrating an example of processing of the physical information measuring apparatus 200 according to the second embodiment. As shown in FIG. 19, in the physical information measuring apparatus 200, first, the subject registration unit 64 performs subject registration processing (S221). For example, the subject registration unit 64 of the control device 50 accepts subject information input from a keyboard (not shown) or the like and records it in the subject registration information 110. In the second embodiment, the position recorded in the subject registration information 110 may be the identification number of the microphone 16 or the like.

  The moving object detection unit 66 receives, for example, a sensor signal SRF corresponding to the reflected wave of the irradiated microwave from the microwave sensor 13. At this time, when there is a moving object in the irradiation range, the moving object detection unit 66 receives a signal indicating a frequency change according to the moving speed of the moving object. The respiratory sound analysis unit 68 receives, for example, an audio signal including the respiratory signal SA1 of the subject to be measured from the microphone 16 (S222).

  The respiratory sound analysis unit 68 analyzes the received signal (S223). That is, the first respiratory rate is specified from the received audio signal. The respiratory sound analysis unit 68 records the specified first respiratory rate together with the time, for example, in the first respiratory rate information 112 every predetermined time. Details of the process of S223 will be described later.

  As described above, the sensor signal analysis unit 70 identifies the second respiration rate and heart rate of the same subject from the sensor signal SRF received from the microwave sensor 13 and stores them in the detection value information 114 (S224). ). The details of the processing of S224 are the same as the details of the processing of S124 according to the first embodiment.

  The heart rate respiration component classification unit 72 compares the recorded first respiration rate information 112 and the detection value information 114, and if it has a second respiration rate that approximates the first respiration rate, The measured value of the subject set in the subject registration information 110 is specified (S225). The heartbeat / respiration component classification unit 72 records the specified detection value information 114 as measurement information of the specified subject in the subject physical information 116 and outputs it (S226).

  FIG. 20 is a flowchart illustrating an example of details of the microphone signal analysis processing in S223 of FIG. As illustrated in FIG. 20, the respiratory sound analysis unit 68 detects a respiratory sound feature point from the audio signal acquired by the microphone 16 as illustrated in the respiratory signal example 80 of FIG. 5 (S <b> 231). The respiratory sound analysis unit 68 calculates the respiratory sound interval for each breath from the detected respiratory sound feature points (S232). The breathing sound interval obtained by the process of S232 is distinguished from the breathing sound interval obtained from the signal from the microphone array 15 and is referred to as a breathing interval BRIm. The respiratory sound analysis unit 68 calculates the respiratory sound interval frequency BRFm from the calculated respiratory interval BRIm (S233). At this time, the respiratory sound analysis unit 68 obtains the respiratory rate BRm from the respiratory sound interval BRIm, and records the respiratory rate BRm as the first respiratory rate at each detection time in the first respiratory rate information 112. Good. The respiratory sound analysis unit 68 returns the process to the process of FIG.

  FIG. 21 is a flowchart showing an example of the heartbeat / respiration component classification process in S225 of FIG. As shown in FIG. 21, the heartbeat / respiration component classification unit 72 calculates differences P1 and P2 between the microwave respiration frequencies BRF1 and BRF2 and the respiration sound interval frequency BRFm based on the detection signal from the microphone 16 (S251). The microwave respiration frequencies BRF1 and BRF2 are respiration frequencies based on the signal from the microwave sensor 13 described in the first embodiment with reference to FIG. Here, the differences P1 and P2 are calculated as shown in the following formulas 5 and 6 for each detection time value of the respiratory sound interval frequency BRFm, for example.

P1 = | BRFm−BRF1 | (Formula 5)
P2 = | BRFm−BRF2 | (Formula 6)

  The heartbeat / respiration component classification unit 72 determines whether or not the difference P1 <the difference P2 (S252). In the following description, it is assumed that the breathing sound specified by the microphone 16 is the breathing sound of the subject A. When the determination result in S252 is YES, the heartbeat / respiration component classification unit 72 uses the microwave respiration frequency BRF1 as a measurement value by the microwave sensor 13 corresponding to the respiration sound interval frequency BRFm of the respiration sound specified by the microphone 16. The breathing sound interval frequency BRFA of the subject A is assumed. Further, the heartbeat / respiration component classification unit 72 sets the microwave respiration frequency BRF2 as the respiration sound interval frequency BRFB of the subject B not specified by the microphone 16 (S253).

  When the determination result in S252 is NO, the heartbeat / respiration component classification unit 72 uses, for example, the breathing sound interval frequency BRFA of the subject A as the measurement value corresponding to the breathing sound interval frequency BRFm of the breathing sound specified by the microphone 16. The microwave respiration frequency is BRF2. The heartbeat respiratory component classification unit 72 sets the breathing sound interval frequency BRFB of the subject B not specified by the microphone 16 as the microwave breathing frequency BRF1 (S254).

  The heartbeat respiratory component classification unit 72 calculates the respiratory rates BRA and BRB of the subject A and the subject B based on the obtained respiratory sound interval frequencies BRFA and BRFB, respectively (S255). At this time, the heart rate respiratory component classification unit 72 may record the respiratory rates BRA and BRB as the first respiratory rate in the first respiratory rate information 112.

  Subsequently, the heartbeat / respiration component classification unit 72 calculates differences Q1 and Q2 between the heartbeat fluctuation respiratory frequencies BRHRF1 and BRHRF2 and the breathing sound interval frequency BRFm based on the detection signal from the microphone 16 (S256). Here, the differences Q1 and Q2 are calculated as shown in the following formulas 7 and 8 for each detection time value of the first respiratory rate information 112, for example.

Q1 = | BRFm−BRHRF1 | (Expression 7)
Q2 = | BRFm−BRHRF2 | (Expression 8)

  The heartbeat / respiration component classification unit 72 determines whether or not the difference Q1 <the difference Q2 (S257). When the determination result in S257 is YES, the heartbeat fluctuation respiratory frequency BRHRF1 approximates the breathing sound interval frequency BRFm of the subject A, and the heartbeat fluctuation respiratory frequency BRHRF2 does not approximate the breathing sound interval frequency BRFm of the subject A. It is determined. Therefore, it is determined that the heartbeat interval RRI1 corresponding to the heartbeat fluctuation respiratory frequency BRHRF1 is the heartbeat interval frequency RRIA of the subject A, and the heartbeat interval RRI2 corresponding to the heartbeat fluctuation breathing frequency BRHRF2 is the heartbeat interval frequency RRIB of the subject B. (S258).

  If the determination result in S257 is NO, the heartbeat fluctuation respiratory frequency BRHRF2 approximates the breathing sound interval frequency BRFm of the subject A, and the heartbeat fluctuation respiratory frequency BRHRF1 does not approximate the breathing sound interval frequency BRFm of the subject A. It is determined. Therefore, it is determined that the heartbeat interval RRI2 corresponding to the heartbeat fluctuation respiratory frequency BRHRF2 is the heartbeat interval frequency RRIA of the subject A, and the heartbeat interval RRI1 corresponding to the heartbeat fluctuation breathing frequency BRHRF1 is the heartbeat interval frequency RRIB of the subject B. (S259).

  The heartbeat / respiration component classification unit 72 calculates a heart rate HRA corresponding to the specified heartbeat interval frequency RRIA of the subject A and a heart rate HRB corresponding to the heartbeat interval frequency RRIB of the subject B, for example, the heart rate of the subject body information 116 (S260), and the process returns to the process of FIG.

  As described above, according to the physical information measuring apparatus 200 according to the second embodiment, the heart rate of the same subject and the second heart rate are obtained from the sensor signal SRF from the microwave sensor 13 including the physical information of a plurality of subjects. A set of respiratory rates is identified. Further, the detection signal including the breathing sound of the subject from the microphone 16 is analyzed, and the first breathing rate of the subject to be measured is specified by referring to the position of the subject input in advance based on the arrangement of the microphone 16. Is done. At this time, the respiration rate may be obtained from the microwave respiration frequencies BRF1 and BRF2 obtained when the signal obtained by the microwave sensor 13 is frequency-converted. By specifying the second respiration rate that approximates the first respiration rate, the respiration rate and heart rate of the subject to be measured that could not be specified only by the signal of the microwave sensor 13 including the body information of a plurality of subjects It becomes possible to specify. Since the measurement uses the microwave sensor 13 and the microphone 16 that can be performed without contact, body information in a natural state can be acquired without bothering the subject. Further, by using the microwave sensor 13, measurement in a dark place is possible, and body information at the time of sleeping of the subject can be easily acquired.

  At the time of measurement, the time lag of the measurement results at different measurement locations of the breathing sounds of a plurality of subjects is analyzed, the breathing sound corresponding to the position of the subject to be measured is specified, and the first respiratory rate is calculated. . Further, the fluctuation frequency of the heart rate obtained by electromagnetic wave analysis is set as the second respiration rate. Therefore, it is possible to reliably specify the respiration rate and heart rate of the subject to be measured.

  When classifying the set of detection values of the subject to be measured, the set of detection values including the second respiration rate closest to the first respiration rate specified by the microphone 16 is the measurement value of the subject to be measured. The measurement value of the subject to be measured can be reliably acquired.

  In the present embodiment, the heartbeat / respiration component classification unit 72 calculates the respiration rate from the microwave respiration frequencies BRF1 and BRF2, but the present invention is not limited to this. The respiratory sound analysis unit 68 may obtain the respiratory rate using the respiratory sound interval frequency BRFm calculated from the measured value. This processing may be performed instead of the processing from S251 to S255 in the flowchart of FIG.

  As described above, the body information measuring apparatus 200 according to the second embodiment specifies the breathing sound of one subject by the microphone 16 and compares it with the analysis result obtained by the microwave sensor 13. Thereby, it becomes possible to specify the respiration rate and heart rate of the subject to be measured from the measurement values of a plurality of subjects. At this time, as in the case where the microphone array 15 is used, processing for specifying the breathing sound of the subject to be measured from a plurality of breathing sounds becomes unnecessary. In addition, when the microwave sensors 13 are individually installed, the cost is high, but the cost of installing the microphones 16 is smaller than that.

  The microphone 16 may be installed in each of a plurality of subjects. Thereby, the respiration rate of each subject can be individually measured. For example, when three or more subjects exist within the detection range of the microwave sensor 13, it is also possible to acquire physical information for each of three or more subjects by increasing the number of microphones 16 to the number of people. Become. Further, as the microphone 16, a directional microphone may be used.

(Modification)
Hereinafter, modified examples will be described. This modification is an example in a state where, for example, two subjects exist in the same room 11, and at least one of the respiratory rate and the heart rate is the same. Regarding the hardware configuration of the physical information measuring device, the physical information measuring device 10 according to the first embodiment or the physical information measuring device 200 according to the second embodiment can be used.

  First, when the respiratory rate and the heart rate are different from each other, the first or second embodiment is applied.

  When there are two types of respiration rate and one type of heart rate, the first respiration rate specified by the measurement value of the microphone array 15 or the microphone 16 is the respiration rate of each subject. The heart rate is the same for both subjects.

  When the respiratory rate is one type and the heart rate is two types, the subject cannot be specified only by the measurement result at the current time, and therefore the subject is specified from the temporal relationship. For example, processing such as setting the heart rate of the subject to be smaller is the difference between the heart rate in the previous and subsequent times.

  When both the respiratory rate and the heart rate are one type, the subject identification process is not required, and both of them have the same respiratory rate and heart rate.

  By performing the processing as described above, in the first embodiment, the second embodiment, or the modified example, at least one of the respiratory rate and the heart rate of the two subjects becomes the same state. Even in special cases, it is possible to specify the respiratory rate and heart rate.

  As described above, according to the first embodiment, the second embodiment, and the modification, a set of heart rate and respiration rate of a plurality of subjects obtained by measuring electromagnetic waves is obtained from any subject. Can be identified. That is, even when a plurality of persons are nearby, physical information such as individual respiration rate and heart rate can be appropriately assigned to each subject by measuring electromagnetic waves and breathing sounds.

  The present invention is not limited to the embodiments described above, and various configurations or embodiments can be adopted without departing from the gist of the present invention. For example, the above formulas (1) to (8) may use other formulas having substantially the same action. For example, the second respiratory rate whose absolute value of the difference from the first respiratory rate specified by the microphone array 15 or the microphone 16 is not more than a predetermined value is determined as a respiratory rate that approximates the first respiratory rate. Deformation is possible.

Regarding the above first and second embodiments and modifications, the following additional notes are disclosed.
(Appendix 1)
First respiratory sound identified as the breathing sound of the subject to be measured from the breathing sounds of the plurality of subjects acquired at different measurement locations and the position of the subject to be measured among the plurality of subjects with respect to the measurement location. The respiratory rate of the subject is determined, and at least one set of the heart rate and the second respiratory rate for each subject is determined from the movements of the subject obtained from the reflected waves of the electromagnetic waves irradiated to the range including the subject. A specifying unit for specifying a set and specifying a set including the second respiration rate approximate to the first respiration rate specified from the set as a heart rate and a respiration rate of the subject to be measured;
A physical information acquisition device characterized by comprising:
(Appendix 2)
The specific part is:
Analyzing the time lag of the measurement results at the different measurement locations of the breathing sounds of the plurality of subjects, specifying the breathing sound corresponding to the position of the subject to be measured, and calculating the first respiratory rate A respiratory sound analysis unit;
A sensor signal analyzer that calculates the second respiration rate from the fluctuation frequency of the heart rate obtained by the analysis of the electromagnetic wave;
The physical information acquisition apparatus according to appendix 1, characterized by comprising:
(Appendix 3)
The specifying unit further measures, from the set of at least one set of the heart rate and the second respiration rate, a set including the second respiration rate closest to the first respiration rate, for the subject to be measured. Heart rate breathing component classifying unit that classifies as a value,
The physical information acquisition device according to supplementary note 1 or supplementary note 2, characterized by comprising:
(Appendix 4)
A computer executing the physical information acquisition method
First respiratory sound identified as the breathing sound of the subject to be measured from the breathing sounds of the plurality of subjects acquired at different measurement locations and the position of the subject to be measured among the plurality of subjects with respect to the measurement location. Identify the respiratory rate of
From the movements of the plurality of subjects acquired from the reflected waves of electromagnetic waves irradiated to the range including the plurality of subjects, identify at least one set of heart rate and second respiratory rate for each subject,
A set including the second respiration rate approximated to the first respiration rate specified from the set of the heart rate and the second respiration rate is specified as the heart rate and respiration rate of the subject to be measured. The physical information acquisition method characterized by this.
(Appendix 5)
further,
Analyzing the time lag of the measurement results at the different measurement locations of the respiratory sounds of the plurality of subjects, identifying the respiratory sound corresponding to the position of the subject to be measured, and calculating the first respiratory rate ,
Calculating the second respiratory rate from the fluctuation frequency of the heart rate obtained by the analysis of the electromagnetic wave;
The physical information acquisition method according to supplementary note 4, characterized in that:
(Appendix 6)
Furthermore, a group including a second respiratory rate closest to the first respiratory rate is classified as a measurement value of the subject to be measured from at least one set of the heart rate and the second respiratory rate. To
The physical information acquisition method according to Supplementary Note 4 or Supplementary Note 5, wherein
(Appendix 7)
First respiratory sound identified as the breathing sound of the subject to be measured from the breathing sounds of the plurality of subjects acquired at different measurement locations and the position of the subject to be measured among the plurality of subjects with respect to the measurement location. Identify the respiratory rate of
From the movements of the plurality of subjects acquired from the reflected waves of electromagnetic waves irradiated to the range including the plurality of subjects, identify at least one set of heart rate and second respiratory rate for each subject,
A set including the second respiration rate approximated to the first respiration rate specified from the set of the heart rate and the second respiration rate is specified as the heart rate and respiration rate of the subject to be measured. A program that causes a computer to execute processing.
(Appendix 8)
further,
Analyzing the time lag of the measurement results at the different measurement locations of the respiratory sounds of the plurality of subjects, identifying the respiratory sound corresponding to the position of the subject to be measured, and calculating the first respiratory rate ,
Calculating the second respiratory rate from the fluctuation frequency of the heart rate obtained by the analysis of the electromagnetic wave;
The program according to appendix 7, which includes a process.
(Appendix 9)
Furthermore, a group including a second respiratory rate closest to the first respiratory rate is classified as a measurement value of the subject to be measured from at least one set of the heart rate and the second respiratory rate. To
The program according to appendix 7 or appendix 8, characterized in that it includes a process.

DESCRIPTION OF SYMBOLS 10 Physical information measuring device 11 Room 13 Microwave sensor 15 Microphone array 17 Microwave irradiation range 50 Control apparatus 52 Processor 54 Memory 56 Program 58 Database 62 Identification part 64 Subject registration part 66 Moving object detection part 68 Respiration sound analysis part 70 Sensor signal Analysis unit 72 Heart rate respiration component classification unit 80 Respiration signal example 82 Respiration sound feature point 84 Respiration sound interval time change 86 Respiration sound interval frequency time change 90 Microwave sensor signal example 92 Sensor signal frequency spectrum example 94 Heart rate signal example 96 Heart rate fluctuation spectrum Example 98 Low frequency region 99 High frequency region 102 Heartbeat interval time change 104 Heart rate fluctuation spectrum example 110 Subject registration information 112 First respiratory rate information 114 Detection value information 116 Subject physical information SA1, SA2, SB1, SB2, SA, SB Respiration signal SRF Ma Microwave signal BRIA Breathing interval BRFA Breathing sound interval frequency SRF Sensor signal BRF1 Microwave breathing frequency BRHRF Heart rate fluctuation breathing frequency

Claims (4)

Among the respiratory sounds of a plurality of subjects acquired at different measurement locations, the time lag or amplitude difference of the respiratory sounds at the different measurement locations and the measurement subject of the plurality of subjects with respect to the measurement location The breathing sound of the subject to be measured is specified based on the position, the breathing sound of the subject to be measured and the first respiratory rate of the identified breathing sound are specified, and the range including the plurality of subjects is irradiated. From the movements of the plurality of subjects acquired from reflected waves of electromagnetic waves, at least one set of heart rate and second respiration rate for each subject is specified, and the first respiration rate is specified from the set A specifying unit for specifying a set including the second respiratory rate closest to the heart rate and the respiratory rate of the subject to be measured;
A physical information acquisition device characterized by comprising: The specific part is :
A sensor signal analyzer for calculating the second respiration rate from the fluctuation frequency of the heart rate obtained by the analysis of the electromagnetic wave ;
The physical information acquisition apparatus according to claim 1, comprising: A computer executing the physical information acquisition method
Among the respiratory sounds of a plurality of subjects acquired at different measurement locations, the time lag or amplitude difference of the respiratory sounds at the different measurement locations and the measurement subject of the plurality of subjects with respect to the measurement location Identifying the breathing sound of the subject to be measured based on the position ,
Specifying the first breathing rate of the breathing sound identified with the breathing sound of the subject to be measured;
From the movements of the plurality of subjects acquired from the reflected waves of electromagnetic waves irradiated to the range including the plurality of subjects, identify at least one set of heart rate and second respiratory rate for each subject,
A set including the second respiration rate closest to the first respiration rate, which is specified from the set of the heart rate and the second respiration rate, is specified as the heart rate and respiration rate of the subject to be measured. The physical information acquisition method characterized by this. Among the respiratory sounds of a plurality of subjects acquired at different measurement locations, the time lag or amplitude difference of the respiratory sounds at the different measurement locations and the measurement subject of the plurality of subjects with respect to the measurement location Identifying the breathing sound of the subject to be measured based on the position ,
Specifying the first breathing rate of the breathing sound identified with the breathing sound of the subject to be measured;
From the movements of the plurality of subjects acquired from the reflected waves of electromagnetic waves irradiated to the range including the plurality of subjects, identify at least one set of heart rate and second respiratory rate for each subject,
A set including the second respiration rate closest to the first respiration rate, which is specified from the set of the heart rate and the second respiration rate, is specified as the heart rate and respiration rate of the subject to be measured. A program that causes a computer to execute processing.