JP6519344B2 - Heartbeat interval specifying program, heart beat interval specifying device, and heart beat interval specifying method - Google Patents

Description

  The present invention relates to a heartbeat interval identification program, a heartbeat interval identification device, and a heartbeat interval identification method.

  There is known a technique for outputting heart rate information using a microwave Doppler sensor (see, for example, Patent Document 1).

JP 2005-237569 A

  A non-contact sensor such as a microwave Doppler sensor transmits a detection wave such as a microwave toward the heart of a subject and outputs a sensor signal based on the reflected wave. The sensor signal includes vibration components that occur in other movements than the heart beat. Thus, the sensor signal is filtered at the bandwidth of the frequency of the heartbeat. As a result, it is possible to extract a feature point related to the heartbeat from the filtered signal.

  However, when the sensor signal is filtered in this way, the obtained signal does not become a complete periodic waveform (sine wave), but the time information of the feature points generated by the heartbeat before filtering is blunted Remain in the state. Therefore, when extracting the time information of the feature point which concerns on a heart rate from the signal obtained by the filter process, it is difficult to calculate each heart beat interval accurately.

  Therefore, in one aspect, the present invention aims to provide a heartbeat interval identification program, an heartbeat interval identification device, and a heartbeat interval identification method capable of accurately calculating individual heartbeat intervals.

According to one aspect, a sensor signal generated based on a reflected wave of a detection wave transmitted toward the subject's heart is acquired,
Identify the frequency of the heart beat based on the acquired sensor signal,
Filtering the sensor signal based on the identified frequency of the heart beat;
When a signal obtained by the filtering process is a filtering signal , at least one of time and amplitude is similar to a feature point related to the heartbeat in the filtering signal among a plurality of feature points in the sensor signal Identifying feature points as feature points pertaining to the heart beat;
The interval of heartbeats is calculated based on the identified feature points related to the heartbeats,
A heart beat interval identification program is provided which causes the computer to execute the process.

  The heartbeat interval identification program, the heartbeat interval identification device, and the heartbeat interval identification method capable of accurately calculating each heartbeat interval can be obtained.

It is a figure which shows an example of a non-contact sensor. It is a block diagram showing an example of the hardware constitutions of a cardiac-beats interval specific device. It is a block diagram which shows an example of an electromagnetic wave transmission / reception part. It is a block diagram showing an example of functional composition of a cardiac-beats interval specific device. It is a figure which shows an example of a sensor signal. It is a figure which shows an example of a frequency analysis result. It is a figure which shows an example of a filtering signal. It is a figure which shows the example of an output of the space | interval of a heartbeat. It is a flow chart which shows an example of processing performed by a cardiac-beats interval specific device. It is a flowchart which shows an example of a heartbeat characteristic point identification process. It is a flow chart which shows an example of filter delay amendment processing. It is a figure which shows an example of filtering signal feature point information. It is a figure which shows an example of sensor signal feature point information. It is a flowchart which shows the heartbeat characteristic point determination process by an example. It is a figure which shows an example of heartbeat characteristic point information. It is explanatory drawing of the heartbeat characteristic point determination processing by an example. It is a flowchart which shows the heartbeat characteristic point determination processing by another example. It is explanatory drawing of the heartbeat characteristic point determination processing by another example. It is a flowchart which shows the heartbeat characteristic point determination processing by another example. It is a figure which shows an example of the extraction result of the sensor signal feature point which satisfy | fills the amplitude determination conditions. It is explanatory drawing of the heartbeat characteristic point determination processing by another example. It is a comparison figure of the waveform of the heartbeat interval of a present Example and a reference method. It is a comparison figure of the waveform of the heartbeat interval of a comparative example and a reference method. It is a comparison figure (the 1) of a frequency analysis result of a cardiac-beats interval. It is a comparison figure (the 2) of a frequency analysis result of a cardiac-beats interval. It is a block diagram showing an example of the hardware constitutions of a cardiac-beats interval specific device. It is a block diagram showing an example of functional composition of a cardiac-beats interval specific device. It is a flow chart which shows an example of processing performed by a cardiac-beats interval specific device. It is a flowchart which shows an example of the motion detection process by a motion detection part. It is a figure which shows an example of movement information. It is a flowchart which shows an example of the heartbeat feature point identification process performed by the heartbeat feature point identification part. It is a flowchart which shows an example of the heartbeat characteristic point determination processing which a heartbeat characteristic point search part performs.

  Hereinafter, each example will be described in detail with reference to the attached drawings.

  In the following embodiment, the heartbeat interval identification device 10 calculates the interval of heartbeats using the information from the non-contact sensor. In the following description, a subject may be described as a subject whose heart rate is to be estimated.

Example 1
FIG. 1 is a diagram showing an example of a non-contact sensor. The non-contact sensor 70 is attached to, for example, clothes of the subject 5. Alternatively, when the movement of the subject 5 is small, the non-contact sensor 70 may be provided at the fixed position in a manner independent of the subject 5. The non-contact sensor 70 includes a radio wave transmission / reception unit 25, and the radio wave transmission / reception unit 25 includes a radio wave transmission unit 1 and a radio wave reception unit 2. The details of the radio wave transmission / reception unit 25 will be described later with reference to FIG.

  The radio wave transmission unit 1 applies radio waves to the human body of the subject 5. The band of radio waves is arbitrary. Ultrahigh frequency (UHF: Ultra High Frequency) and microwave (SHF: Super High Frequency) are mentioned as an example of a radio wave. Still further, the radio wave may be, for example, 2.4 G band. The radio wave receiver 2 receives the reflected wave of the radio wave from the subject 5.

  FIG. 2 is a block diagram showing an example of a hardware configuration of the heartbeat interval identification device.

  As shown in FIG. 2, the heartbeat interval identification device 10 includes a central processing unit (CPU) 11, a random access memory (RAM) 12, a read only memory (ROM) 13, a recording medium interface 14, and a recording medium interface 14 connected by a bus 19. The display control unit 15 is included. Further, the heartbeat interval identification device 10 has an input / output control unit 16 and a communication interface 17. A recording medium such as an SD (Secure Digital) card (or memory card) 21 can be connected to the recording medium interface 14. A display device 22 is connected to the display control unit 15. An input / output device 24 is connected to the input / output control unit 16. The input / output device 24 may be a touch panel, a speaker or the like. The functions of the display device 22 and the input / output device 24 may be realized by a touch panel. The recording medium interface 14 and the SD card 21, the input / output control unit 16 and the input / output device 24, the display device 22 and the display control unit 15, and / or the wireless transmission / reception unit 26 may be omitted as appropriate.

  A radio wave transmission / reception unit 25 and a wireless transmission / reception unit 26 are connected to the communication interface 17. The radio wave transmitting / receiving unit 25 has the radio wave transmitting unit 1 and the radio wave receiving unit 2 shown in FIG. The wireless transmission and reception unit 26 includes a transmission and reception unit capable of wireless communication using a wireless communication network in a mobile phone. The wireless transmission / reception unit 26 may include a near field communication (NFC) unit, a Bluetooth (registered trademark) communication unit, a wireless-fidelity (Wi-Fi) transmission / reception unit, an infrared transmission / reception unit, and the like.

  The CPU 11 has a function of controlling the overall operation of the heartbeat interval identification device 10. The RAM 12 and the ROM 13 form a storage unit for storing programs executed by the CPU 11 and various data. The program includes a program that causes the CPU 11 to execute a heartbeat interval identification process to function as a heartbeat interval identification device. The storage unit may include the SD card 21. The storage unit that stores the program is an example of a computer readable storage medium.

  The display device 22 has a function of displaying the result of the heartbeat interval identification process and the like under the control of the display control unit 15.

  The heartbeat interval identification device 10 may be formed integrally with the non-contact sensor 70 (in one housing). Alternatively, the heartbeat interval identification device 10 may be provided separately from the non-contact sensor 70.

  FIG. 3 is a block diagram showing an example of the radio wave transmission / reception unit 25. As shown in FIG.

  The radio wave transmission / reception unit 25 includes a control unit 251, an oscillation circuit 252, antennas 253T and 253R, a detection circuit 254, a power supply circuit 255, and operational amplifiers (Operational Amplifiers) 256 and 258. The transmission wave (radio wave) generated by the oscillation circuit 252 is branched to the antenna 253T and the detection circuit 254, and the transmission wave transmitted from the antenna 253T is irradiated to the subject 5. The transmission wave irradiated to the subject 5 is reflected, and the reflection wave of the transmission wave from the subject 5 is received by the antenna 253R. The reflected wave indicated by the alternate long and short dash line received by the antenna 253R interferes with the transmission wave indicated by the solid line at the node N, and the detection circuit 254 outputs a composite wave (DC component) indicated by the alternate long and short dash line. The operational amplifier 256 outputs a sensor output obtained by amplifying the synthetic wave through the communication interface 17. The sensor output from op amp 256 is also referred to as a sensor signal.

  The power supply circuit 255 includes a battery that supplies a power supply voltage to the control unit 251, the oscillation circuit 252, the detection circuit 254, and the operational amplifier 256. The battery is, for example, a rechargeable battery. Needless to say, the power supply circuit 255 may be externally connected to the radio wave transmission / reception unit 25. Also, the antennas 253T and 253R may be integrated as a transmitting and receiving antenna.

  In the example of FIG. 3, the radio wave transmission unit 1 includes at least the oscillation circuit 252 and the antenna 253T, and the radio wave reception unit 2 includes at least the antenna 253R, the detection circuit 254, and the operational amplifier 256.

  FIG. 4 is a block diagram showing an example of a functional configuration of the heartbeat interval identification device 10. As shown in FIG.

  In the example shown in FIG. 4, the heartbeat interval identification device 10 includes a sensor signal acquisition unit 100, a heartbeat component detection unit 101, a heartbeat feature point identification unit 102, a heartbeat interval calculation unit 103, and an output unit 104. The respective units 100 to 104 of the heartbeat interval identification device 10 can be realized by the CPU 11 shown in FIG. 2 executing one or more programs stored in the ROM 13.

  The sensor signal acquisition unit 100 acquires (receives) a sensor signal (see FIG. 3) from the non-contact sensor 70.

  The heartbeat component detection unit 101 includes a frequency analysis unit 31 and a filter processing unit 32.

  The frequency analysis unit 31 specifies the frequency of the heartbeat based on the sensor signal. Specifically, the frequency analysis unit 31 acquires the power spectrum by performing frequency analysis on the sensor signal obtained from the radio wave reception unit 2. FIG. 5 shows an example of a sensor signal. In FIG. 5, the horizontal axis represents time, and the vertical axis represents amplitude. The frequency analysis is, for example, FFT (Fast Fourier Transform), and the analysis result shown in FIG. 6 is obtained, for example. FIG. 6 is a diagram showing an example of an analysis result obtained by frequency analysis of the sensor signal of FIG. In FIG. 6, the horizontal axis represents frequency, and the vertical axis represents intensity (power).

From the analysis result shown in FIG. 6, the frequency analysis unit 31 specifies a frequency at which a peak having an amplitude value equal to or greater than a predetermined threshold Th1 occurs as a heartbeat frequency. Incidentally, in FIG. 6, the peak P ₁ is illustrated in heart rate (displacement organs including the surface or heart of the body caused by the beating of the heart of the subject 5).

  The filter processing unit 32 filters the sensor signal based on the frequency of the heartbeat identified by the frequency analysis unit 31. That is, the filter processing unit 32 performs filter processing on the sensor signal centering on the frequency of the peak identified as the heartbeat. The filtering process is, for example, a band-pass filter (BPF) process and is performed to extract a heartbeat which beats every beat. Hereinafter, a signal obtained by filtering the sensor signal is referred to as a “filtered signal”. FIG. 7 shows an example of the filtering signal. The filtered signal shown in FIG. 7 is obtained by filtering the sensor signal of FIG. In FIG. 7, the horizontal axis represents time, and the vertical axis represents amplitude.

  The heartbeat feature point identifying unit 102 identifies a feature point associated with a heartbeat from among a plurality of feature points in a sensor signal, based on a feature point associated with a heartbeat in a filtering signal. The heartbeat feature point identifying unit 102 includes a filter delay correction unit 40, a filtering signal feature point detection unit 41, a sensor signal feature point detection unit 42, and a heartbeat feature point search unit 43.

  The filter delay correction unit 40 corrects the delay of the filtering signal with respect to the sensor signal. The delay occurs due to the filtering process by the filtering unit 32. A specific example of this process will be described later with reference to FIG.

  The filtering signal feature point detection unit 41 detects a feature point related to a heartbeat in the filtering signal (hereinafter, also referred to as “filtering signal feature point”). The filtered signal feature points appear as peaks (each peak such as Pa to Pd in FIG. 7) in the filtered signal. The filtering signal feature point detection unit 41 detects each peak in the filtering signal as each feature point related to the heartbeat. The filtering signal feature point detection unit 41 generates information representing the time and amplitude of each peak in the filtering signal as information (filtering signal feature point information) related to each feature point related to the heartbeat.

  The sensor signal feature point detection unit 42 detects feature point candidates related to the heartbeat in the sensor signal. The sensor signal feature point detection unit 42 specifies feature point candidates (hereinafter, referred to as “sensor signal feature points”) related to the corresponding heartbeat for each filtering signal feature point. The sensor signal feature point detection unit 42 generates information representing time and amplitude of the sensor signal feature point as sensor signal feature point information. A specific example of this process will be described later with reference to FIG.

  The heartbeat feature point search unit 43 selects a feature point related to a heartbeat (hereinafter referred to as “heart beat” among a plurality of feature points in the sensor signal based on the detection results of the filtering signal feature point detection unit 41 and the sensor signal feature point detection unit 42 Search for “heart rate feature points”. The heartbeat feature point search unit 43 specifies a heartbeat feature point based on at least one of a time difference and an amplitude difference between the filtering signal feature point and the sensor signal feature point. A specific example of this process will be described later with reference to FIG.

  The heartbeat interval calculation unit 103 calculates heartbeat intervals based on the heartbeat feature points identified by the heartbeat feature point identification unit 102. The interval of heartbeats can be calculated as a time interval of each feature point related to the heartbeat identified by the heartbeat feature point identifying unit 102.

  The output unit 104 outputs the heartbeat interval calculated by the heartbeat interval calculation unit 103 on the display device 22. The output (transmission) destination of the heartbeat interval is not limited to the display device 22, and may be, for example, a remotely located monitoring computer (not shown) or the like. FIG. 8 shows an example of output of heart rate interval. In FIG. 8, the horizontal axis represents time, and the vertical axis represents a heart beat interval [unit: second]. The output unit 104 may output a result of performing a predetermined frequency analysis on a waveform of a heartbeat interval as shown in FIG. The predetermined frequency analysis may be, for example, an FFT, an autoregressive model (AR model), or the like (see FIGS. 24 and 25).

  FIG. 9 is a flowchart showing an example of processing executed by the heartbeat interval identification device 10.

  In step S900, the sensor signal acquisition unit 100 receives a sensor signal from the non-contact sensor 70. For example, the sensor signal acquisition unit 100 receives a sensor signal of a predetermined period ΔT from a predetermined time before to the current time.

  In step S902, the frequency analysis unit 31 of the heartbeat component detection unit 101 performs frequency analysis on the sensor signal. The frequency analysis unit 31 specifies the frequency of the heartbeat as a result of the frequency analysis. The method of frequency analysis is as described above.

  In step S904, the filter processing unit 32 of the heartbeat component detection unit 101 filters the sensor signal based on the frequency of the heartbeat identified in step S902. As a result, a filtered signal is obtained.

  In step S906, the heartbeat feature point identifying unit 102 executes heartbeat feature point identification processing based on the sensor signal obtained in step S900 and the filtering signal obtained in step S904. A specific example of the heartbeat characteristic point specifying process will be described later.

  In step S908, the heartbeat interval calculation unit 103 calculates an interval of heartbeat based on each feature point related to the heartbeat identified in step S906.

  In step S910, the output unit 104 outputs the heartbeat interval calculated in step S908.

  FIG. 10 is a flowchart showing an example of a heartbeat feature point identification process executed by the heartbeat feature point identification unit 102. The process shown in FIG. 10 is executed as the process of step S906 in FIG. FIG. 11 is a flowchart illustrating an example of the filter delay correction process.

  In step S1002, the filter delay correction unit 40 performs filter delay correction processing. Specifically, as shown in FIG. 11, the filter delay correction unit 40 first calculates the filter delay time (step S1100). Here, the phase delay and group delay of the linear phase FIR (Finite Impulse Response) filter are equally constant over frequency. When an n-order linear phase FIR filter is used in the filter processing unit 32, the group delay is n / 2, and the output signal (filtered signal) by the filtering process is delayed n / 2 samples with respect to the input signal (sensor signal). In this case, the filter delay correction unit 40 calculates a time corresponding to n / 2 samples as the filter delay time. Next, the filter delay correction unit 40 performs delay correction of the filtered signal based on the calculated filter delay time (step S1102).

In step S1004, the filtering signal feature point detection unit 41 detects filtering signal feature points. Then, as described above, the filtering signal feature point detection unit 41 generates filtering signal feature point information representing time and amplitude of each filtering signal feature point. FIG. 12 is a diagram showing an example of the filtering signal feature point information. The filtering signal feature point information, as shown in FIG. 12, represents a feature point number assigned to each filtering signal feature point, a time (occurrence time) for each feature point number, and an amplitude value for each feature point number. . In the example shown in FIG. 12, the case where n filtering signal feature points are detected is shown. In the following, the time of filtering the signal feature points, expressed as _"P filter_time", the amplitude value of the filtered signal characteristic points representing the _"P filter_amp". Also, P _{filter_time} (i) represents the time of the filtering signal feature point with the feature point number “i”, and P _{filter_amp} (i) represents the amplitude value of the filtering signal feature point with the feature point number “i” Represents

In step S1006, the sensor signal feature point detection unit 42 calculates a search window for searching for a sensor signal feature point. The search window (search range) is determined as follows, for example, so as not to erroneously detect heartbeat feature points before and after the search target. That is, the search window is set based on the frequency of the heartbeat and the sampling rate according to the following equation so as not to erroneously detect heartbeat characteristic points that are not to be searched as sensor signal feature points.
Search range before and after (number of samples) サ ン プ リ ン グ sampling rate ÷ heartbeat period component ÷ 2
Here, the front and rear search range indicates a front and rear search range centering on the time of the filtering signal feature point. The sampling rate is the sampling rate of the sensor signal. For example, when the sampling rate is 100 Hz and the frequency of the heartbeat is 1 Hz, a range of 50 samples (= 100 ÷ 1 ÷ 2) before and after the feature point of the filtering signal is calculated as a search window (search range). In addition, when the sampling rate is 60 Hz and the heartbeat frequency is 2 Hz, a range of 15 samples (= 60 ÷ 2 ÷ 2) before and after the feature point of the filtering signal is calculated as a search window (search range).

  In step S1007, the sensor signal feature point detection unit 42 selects one filtering signal feature point in order from among all the filtering signal feature points detected in step S1006. The selection order is arbitrary.

In step S1008, the sensor signal feature point detection unit 42 detects sensor signal feature points for the filtering signal feature points selected in step S1007 based on the search window calculated in step S1006. Specifically, the sensor signal feature point detection unit 42 aligns the center of the search window with the time of the filtering signal feature point. Then, the sensor signal feature point detection unit 42 detects a peak within the search range (a peak having an amplitude value equal to or greater than a predetermined threshold value Th2) around the time of the filtering signal feature point in the sensor signal as a sensor signal feature point. Then, as described above, the sensor signal feature point detection unit 42 generates sensor signal feature point information that represents the time and the amplitude of the sensor signal feature point. FIG. 13 is a diagram showing an example of sensor signal feature point information obtained corresponding to one certain filtering signal feature point. The sensor signal feature point information, as shown in FIG. 13, represents a feature point number assigned to each sensor signal feature point, a time (occurrence time) for each feature point number, and an amplitude value for each feature point number. . In the example shown in FIG. 13, the case where m sensor signal feature points are detected is shown. Hereinafter, the time point sensor signal characteristics, expressed as _"P raw_time", expressed as the amplitude value of the sensor signal feature point _"P raw_amp". Also, P _{raw_time} (i) represents the time of the sensor signal feature point with the feature point number “i”, and P _{raw_amp} (i) represents the amplitude value of the sensor signal feature point with the feature point number “i”. Represents

  In step S1010, the heartbeat feature point search unit 43 determines whether the number of sensor signal feature points detected in step S1008 is more than one. If the number of sensor signal feature points detected in step S1008 is more than one, the process proceeds to step S1014. If the number of sensor signal feature points detected in step S1008 is one, the process proceeds to step S1012.

  In step S1012, the heartbeat feature point search unit 43 specifies (determines) one sensor signal feature point detected in step S1008 as a heartbeat feature point.

  In step S1014, the heartbeat feature point search unit 43 executes heartbeat feature point determination processing for identifying a heartbeat feature point among the plurality of sensor signal feature points detected in step S1008. The heartbeat feature point searching unit 43 determines the heartbeat based on at least one of the time difference and the amplitude difference between each of the plurality of sensor signal feature points detected in step S1008 and the filtering signal feature point. Identify feature points. A specific example of the heartbeat characteristic point determination process will be described later with reference to FIG.

  In step S1016, the heartbeat feature point search unit 43 determines whether or not heartbeat feature points have been identified for all filtered signal feature points. If the heartbeat feature point is specified for all the filtering signal feature points, the process ends. Otherwise, the process returns to step S1007. In this way, heartbeat feature points are identified for each filtering signal feature point for all filtering signal feature points.

  By the way, the filtering signal is obtained by the filter processing by the filter processing unit 32 as described above. Therefore, the filtering signal remains with blunted time information of feature points (heart rate feature points) generated by the heartbeat before filtering. That is, the time of the feature point related to the heartbeat obtained from the filtering signal has an error with respect to the time of the feature point generated by the actual heartbeat. Hereinafter, an error caused by such filtering is referred to as “time error”. Such a time error is not substantially a problem when calculating an average heartbeat interval within a relatively long period, but becomes a problem when accurately computing each heartbeat interval.

  In this respect, according to the present embodiment, as described above, the heartbeat feature point is specified from among the plurality of sensor signal feature points based on the feature point related to the heartbeat obtained from the filtering signal. The sensor signal differs from the filtered signal in that there is no error in the time information due to the filtering as described above. Therefore, according to this embodiment, it is possible to calculate each heartbeat interval with high accuracy.

  Further, as described above, since the sensor signal includes a vibration component generated by other movement other than the heartbeat, it is difficult to accurately identify the heartbeat characteristic point only from the sensor signal. In this respect, according to the present embodiment, as described above, the heartbeat feature point is specified from among the plurality of sensor signal feature points based on the feature point related to the heartbeat obtained from the filtering signal. This identification utilizes that a heartbeat feature point among a plurality of sensor signal feature points is similar in time and / or amplitude to a heartbeat-related feature point obtained from a filtered signal. This is because even when the sensor signal is subjected to the filtering process by the filtering unit 32, the deviation (time error) of the time information due to the filtering process is small. In addition, since the filtering process by the filter processing unit 32 is executed centering on the frequency of the heartbeat, the amplitude value information of the passband component remains. Therefore, according to the present embodiment, the heartbeat characteristic points can be specified with high accuracy, and as a result, individual heartbeat intervals can be calculated with high accuracy.

Next, several examples of the heartbeat feature point determination process will be described with reference to FIG.
[First Example of Heartbeat Feature Point Determination Processing]
FIG. 14 is a flowchart showing a heartbeat feature point determination process according to the first example. The process shown in FIG. 14 is executed as the process of step S1014 shown in FIG. Here, as an example, as shown in FIG. 13, it is assumed that m sensor signal feature points are detected. The heartbeat feature point determination process is performed for each filtering signal feature point as described above. Here, the process regarding the filtering signal feature point of the feature point number k will be described.

In step S1400, the heartbeat feature point search unit 43 calculates, for each sensor signal feature point, a time difference from the filtering signal feature point. The time difference ΔTk (i) can be calculated as follows.
ΔTk (i) = abs (P _{filter_time} (k) −P _{raw_time} (i))
abs () represents the absolute value of (P _{filter — time} (k) −P _{raw — time} (i)). P _{filter — time} (k) represents the time of the filtering signal feature point with the feature point number “k” as described above. P _{raw — time} (i) represents the time of the sensor signal feature point having the feature point number “i” as described above. The heartbeat feature point searching unit 43 calculates ΔTk (i) (i = 1 to m) for each sensor signal feature point.

In step S1400, the heartbeat feature point search unit 43 specifies a sensor signal feature point with the smallest time difference ΔTk among the m sensor signal feature points as a heartbeat feature point. For example, when ΔTk (x) is a minimum value, the heartbeat feature point searching unit 43 specifies a sensor signal feature point of the feature point number x as a heartbeat feature point. The pulse feature point search section 43, P _{Raw_time} a (x), specified as the time P _{HR_time} the pulse feature point (k). That is, the heartbeat feature point search unit 43 determines the time P _{HR —} time (k) of the heartbeat feature point corresponding to the filtering signal feature point of the feature point number k as follows.
P _{HR_time} (k) = P _{raw_time} (x)

The heartbeat feature point searching unit 43 generates information (hereinafter, “heartbeat feature point information”) representing the time P _{HR —} time (i) of the heartbeat feature point specified for each filtering signal feature point in this manner. FIG. 15 is a diagram showing an example of heartbeat feature point information. The heartbeat feature point information represents a time (occurrence time) P _{HR —} time for each feature point number of the filtering signal feature point.

FIG. 16 is an explanatory diagram of a heartbeat feature point determination process according to the first example. FIG. 16 shows an example of each of the waveform C _F of the filtering signal and the waveform C _r of the sensor signal in the portion related to the filtering signal feature point P _FK of the feature point number k. In FIG. 16, the range of the search window (see step S1006 in FIG. 10) is indicated by the arrow z centering on the time of the filtering signal feature point P _FK . In the example shown in FIG. 16, the waveform C _r of the sensor signal has three sensor signal feature points P _{r1 to} P _r3 in the search window.

According to the first example, as described above, the heart rate feature point search unit 43 detects, from among the plurality of sensor signal feature points, a sensor signal having the closest time to the feature point related to the heart rate obtained from the filtering signal. The feature points are identified as heart beat feature points. Therefore, in the example shown in FIG. 16, among the sensor signal feature points P _{r1 to} P _r3 , the sensor signal feature point P _r2 closest to the filtering signal feature point P _FK on the time axis is identified as the heartbeat feature point. This is based on the finding that, as described above, even when the sensor signal is subjected to the filtering process by the filtering unit 32, the time error due to the filtering process is small. Therefore, according to the first example, it is possible to specify a heartbeat feature point accurately from among a plurality of sensor signal feature points, and as a result, it is possible to calculate each heartbeat interval with high accuracy.
[Second example of heartbeat characteristic point determination processing]
FIG. 17 is a flowchart showing a heartbeat feature point determination process according to the second example. The process shown in FIG. 17 is executed as the process of step S1014 shown in FIG. Here, as an example, as shown in FIG. 13, it is assumed that m sensor signal feature points are detected. The heartbeat feature point determination process is performed for each filtering signal feature point as described above. Here, the process regarding the filtering signal feature point of the feature point number k will be described.

In step S1700, the heartbeat feature point search unit 43 calculates, for each sensor signal feature point, an amplitude value difference with respect to the filtering signal feature point. The amplitude value difference ΔAmpk (i) can be calculated as follows.
ΔAmpk (i) = abs (P _{filter_amp} (k) −P _{raw_amp} (i))
abs () represents the absolute value of (P _{filter_amp} (k) −P _{raw_amp} (i)). P _{filter — amp} (k) represents the amplitude value of the filtering signal feature point having the feature point number “k” as described above. P _{raw — amp} (i) represents the amplitude value of the sensor signal feature point having the feature point number “i” as described above. The heartbeat feature point search unit 43 calculates ΔAmpk (i) (i = 1 to m) for each sensor signal feature point.

In step S1702, the heartbeat feature point search unit 43 identifies a sensor signal feature point with the smallest amplitude value difference ΔAmpk among the m sensor signal feature points as a heartbeat feature point. For example, when the amplitude value difference ΔAmpk (x) is a minimum value, the heartbeat feature point searching unit 43 specifies a sensor signal feature point of the feature point number x as a heartbeat feature point. The pulse feature point search section 43, P _{Raw_time} a (x), specified as the time P _{HR_time} the pulse feature point (k). That is, the heartbeat feature point search unit 43 determines the time P _{HR —} time (k) of the heartbeat feature point corresponding to the filtering signal feature point of the feature point number k as follows.
P _{HR_time} (k) = P _{raw_time} (x)
The heartbeat feature point searching unit 43 generates information (heartbeat feature point information) representing the time P _{HR —} time (i) of the heartbeat feature point specified for each filtering signal feature point in this manner (see FIG. 15).

FIG. 18 is an explanatory diagram of a heartbeat feature point determination process according to a second example. FIG. 18 shows an example of each of the waveform C _F of the filtering signal and the waveform C _r of the sensor signal in the portion related to the filtering signal feature point P _FK of the feature point number k. In FIG. 18, the range of the search window (see step S1006 in FIG. 10) is indicated by the arrow z centering on the time of the filtering signal feature point P _FK . In the example shown in FIG. 18, the waveform C _r of the sensor signal has four sensor signal feature points P _{r1 to} P _r4 in the search window.

According to the second example, as described above, the heartbeat feature point search unit 43 selects a sensor whose amplitude value is closest to the feature point related to the heartbeat obtained from the filtering signal among the plurality of sensor signal feature points. Signal feature points are identified as heart rate feature points. Therefore, in the example shown in FIG. 18, among the sensor signal feature points P _{r1 to} P _r4 , the sensor signal feature point P _r2 whose amplitude value is closest to the filtering signal feature point P _FK is specified as a heartbeat feature point. This is based on the knowledge that the amplitude value information of the passband component remains even after the filter processing by the filter processing unit 32. Therefore, according to the second example, among the plurality of sensor signal feature points, a heartbeat feature point can be identified with high accuracy, and as a result, individual heartbeat intervals can be accurately computed.
[Third Example of Heartbeat Feature Point Determination Processing]
FIG. 19 is a flowchart showing a heartbeat feature point determination process according to the third example. The process shown in FIG. 19 is executed as the process of step S1014 shown in FIG. Here, as an example, as shown in FIG. 13, it is assumed that m sensor signal feature points are detected. The heartbeat feature point determination process is performed for each filtering signal feature point as described above. Here, the process regarding the filtering signal feature point of the feature point number k will be described.

In step S1900, the heartbeat feature point search unit 43 sets a feature point determination threshold. The feature point determination threshold value is set based on, for example, the amplitude value of the filtering signal feature point of the feature point number “k”. In this example, the feature point determination threshold includes an upper limit P _{threshold_high} and a lower limit P _{threshold_} low. The upper limit value P _{threshold —} high and the lower limit value P _{threshold —} low are set, for example, as follows.
P _{threshold_} low = P _{filter_amp} (k) × 0.7
P _{threshold_} high = P _{filter_amp} (k) × 1.3

In step S1902, the heartbeat feature point search unit 43 extracts, from the m sensor signal feature points, sensor signal feature points that satisfy the predetermined amplitude determination condition. The amplitude determination condition is set based on the feature point determination threshold, and is satisfied, for example, when both of the following (1) and (2) are satisfied.
(1) P _{threshold_} low ≦ P _{raw_amp} (i)
(2) P _{threshold_} high P P _{raw_amp} (i)
That is, the heartbeat feature point search unit 43 extracts, from the m sensor signal feature points, sensor signal feature points whose amplitude value is equal to or less than the upper limit P _{threshold —} high and equal to or more than the lower limit P _{threshold —} low. FIG. 20 shows an example of extraction results of sensor signal feature points satisfying the amplitude determination condition from m sensor signal feature points. FIG. 20 shows the result of extracting y sensor signal feature points from m sensor signal feature points. Hereinafter, the time of the sensor signal feature point satisfying the amplitude determination condition is represented as “P _{raw —} time ₂ ”, and the amplitude value of the sensor signal feature point satisfying the amplitude determination condition is represented as “P _{raw — amp 2} ”. P _{raw_time 2} (i) represents the time of a sensor signal feature point (a sensor signal feature point satisfying the amplitude determination condition) whose feature point number is “i”, and P _{raw_amp 2} (i) ”represents the feature point number It represents the amplitude value of the sensor signal feature point of "i" (the sensor signal feature point satisfying the amplitude determination condition). In the following, as an example, as shown in FIG. 20, it is assumed that y sensor signal feature points satisfying the amplitude determination condition are extracted.

In step S1904, the heartbeat feature point search unit 43 calculates a time difference with respect to the filtering signal feature point for each sensor signal feature point that satisfies the amplitude determination condition. The time difference ΔTk (i) can be calculated as follows.
ΔT k (i) = abs (P _{filter_time} (k)-P _{raw_time 2} (i))
abs () represents the absolute value of (P _{filter — time} (k) −P _{raw — time 2} (i)). P _{filter — time} (k) represents the time of the filtering signal feature point with the feature point number “k” as described above. P _{raw — time 2} (i) represents the time of the sensor signal feature point having the feature point number “i” as described above. The heartbeat feature point searching unit 43 calculates ΔTk (i) (i = 1 to y) for each sensor signal feature point that satisfies the amplitude determination condition.

In step S1906, the heartbeat feature point search unit 43 specifies, as a heartbeat feature point, a sensor signal feature point having the smallest time difference ΔTk among y sensor signal feature points satisfying the amplitude determination condition. For example, when ΔTk (x) is a minimum value, the heartbeat feature point searching unit 43 specifies a sensor signal feature point of the feature point number x as a heartbeat feature point. Then, the heartbeat feature point searching unit 43 specifies P _{raw —} time ₂ (x) as a time P _{HR —} time (k) of the heartbeat feature point. That is, the heartbeat feature point search unit 43 determines the time P _{HR —} time (k) of the heartbeat feature point corresponding to the filtering signal feature point of the feature point number k as follows.
P _{HR_time} (k) = P _{raw_time2} (x)

FIG. 21 is an explanatory diagram of a heartbeat feature point determination process according to a third example. FIG. 21 shows an example of each of the waveform C _F of the filtering signal and the waveform C _r of the sensor signal in the portion related to the filtering signal feature point P _FK of the feature point number k. In FIG. 21, the range of the search window (see step S1006 in FIG. 10) is indicated by an arrow z centering on the time of the filtering signal feature point P _FK . Further, FIG. 21 shows feature point determination thresholds (upper limit value P _{threshold —} high and lower limit value P _{threshold —} low) according to the amplitude determination condition. In the example shown in FIG. 21, the waveform C _r of the sensor signal has four sensor signal feature points P _{r1 to} P _r4 in the search window.

According to the third example, as described above, the heart rate feature point searching unit 43 first extracts sensor signal feature points that satisfy the amplitude determination condition from among the plurality of sensor signal feature points. Thus, in the example shown in FIG. 21, of the sensor signal feature point P _r1 to P _r4, the sensor signal characteristic point amplitude value is the upper limit value _P threshold_ high or less and the lower limit value _P threshold_ low or P _r1, P _{r2 and,} P _r4 is extracted. That is, the sensor signal feature point P _r3 is removed from the candidates because the amplitude value is less than the lower limit value P _{threshold —} low. This is based on the knowledge that the amplitude value information of the passband component remains even after the filter processing by the filter processing unit 32. Then, among the sensor signal feature points that satisfy the amplitude determination condition, the heartbeat feature point search unit 43 uses, as a heartbeat feature point, a sensor signal feature point whose time is closest to the feature point related to the heartbeat obtained from the filtering signal. Identify. Therefore, in the example shown in FIG. 21, among the sensor signal feature points P _r1 , P _r2 and P _r4 , the sensor signal feature point P _r2 closest to the filtering signal feature point P _FK on the time axis is specified as the heartbeat feature point Be done. This is based on the finding that, as described above, even when the sensor signal is subjected to the filtering process by the filtering unit 32, the time error due to the filtering process is small.

  Thus, according to the third example, to identify the heartbeat feature point, the heartbeat feature point among the plurality of sensor signal feature points is compared with the time and the feature point related to the heartbeat obtained from the filtering signal. Use similarities in both amplitudes. As a result, it is possible to specify a heartbeat feature point with high accuracy from among a plurality of sensor signal feature points, and as a result, it becomes possible to calculate each heartbeat interval with high accuracy. Further, according to the third example, by utilizing the similarity of both time and amplitude, it is possible to calculate each heartbeat interval with high accuracy even if there is a movement of the subject 5. This effect will be described later in connection with the second embodiment.

  In the third example described above, after the heartbeat feature point search unit 43 imposes the amplitude determination condition, the time difference with the time of the filtering signal feature point out of the sensor signal feature points that satisfy the amplitude determination condition is The smallest sensor signal feature point is identified as a heartbeat feature point. However, other methods may be used. For example, the heartbeat feature point searching unit 43 first extracts only the sensor signal feature points whose time difference from the time of the filtering signal feature point is equal to or less than a predetermined threshold value Th3. Then, the heartbeat feature point search unit 43 may specify, from among the extracted sensor signal feature points, a sensor signal feature point whose amplitude value difference with the amplitude value of the filtering signal feature point is minimum as a heartbeat feature point. .

  Next, the effect of this embodiment will be described with reference to FIGS. 22 to 25. FIG.

FIG. 22 is a diagram showing respective waveforms of the heartbeat interval calculated by the present embodiment and the heartbeat interval measured by the reference method. In FIG. 22, the waveform of the heartbeat interval calculated according to this embodiment is indicated by C _em , and the waveform of the heartbeat interval measured by the reference method is indicated by C _ref . FIG. 23 is a diagram showing respective waveforms of the heartbeat interval calculated by the comparative example and the heartbeat interval measured by the reference method. In FIG. 23, the waveform of the heartbeat interval calculated by the comparative example is indicated by _Ccom .

Here, the reference method is a method using a contact sensor (for example, an electrocardiograph). Therefore, the waveform C _ref of the heart beat interval measured by the reference method is the waveform with the highest accuracy, and is hereinafter referred to as a reference waveform C _ref . Further, in the comparative example, a method of calculating a heartbeat interval based on the time of the filtering signal feature point is used.

In the comparative example, as shown in FIG. 22, the error with respect to the reference waveform C _ref is large. This is due to the time error as described above. On the other hand, according to the present embodiment, as can be seen from the comparison with FIGS. 22 and 23, it can be seen that the error with respect to the reference waveform C _ref is smaller than in the comparative example.

  FIG. 24 is a comparison diagram of the frequency analysis result of the heartbeat interval, and the frequency analysis is FFT. FIG. 25 is a comparison diagram of the frequency analysis results of the heartbeat interval, and the frequency analysis is an AR model. Note that these analysis results are analysis results representing a heart rate fluctuation (fluctuation) component.

In FIG. 24 and FIG. 25, as in FIG. 22 and FIG. 23, the frequency analysis result based on the waveform of the heartbeat interval calculated by the present embodiment is indicated by C _em and the waveform of the heartbeat interval measured by the reference method The frequency analysis result based on is indicated by C _ref . Further, a frequency analysis result based on the waveform of the heartbeat interval calculated by the comparative example is indicated by _Ccom . Hereinafter, the frequency analysis result based on the waveform of the heart beat interval measured by the reference method will be referred to as “reference frequency analysis result”.

  In the comparative example, as shown by site X in FIG. 24, the RSA (Respiratory Sinus Arrhythmia) peak is different from the frequency of the same RSA peak as the reference frequency analysis result. Further, in the comparative example, as indicated by a portion Y in FIG. 25, different frequency components appear largely even in the vicinity of the RSA peak. On the other hand, according to the present embodiment, as shown by a part X in FIG. 24, the RSA peak accurately matches the frequency of the same RSA peak as the reference frequency analysis result. Further, according to the present embodiment, as shown in FIG. 24, unlike the comparative example, different frequency components do not appear largely around the RSA peak. The RSA peak is an index value indicating how the heart rate is changed by respiration, and is an index value representing parasympathetic nerve activity.

  Further, in the comparative example, as shown in FIG. 25, the waveform deviates from the reference frequency analysis result, and the area ratio (LF / HF) based on the waveform obtained in the comparative example is the area ratio based on the reference frequency analysis result We diverge greatly. Here, LF is an abbreviation of Low Frequency, and is represented by an integral value of a waveform within a predetermined range on the low frequency side. Further, HF is an abbreviation of High Frequency and is represented by an integral value of a waveform within a predetermined range on the high frequency side. The area ratio (LF / HF) is an index value representing parasympathetic nerve activity as in the case of the RSA peak.

  As described above, according to the present embodiment, since it is possible to calculate each heartbeat interval with high accuracy, it is possible to accurately obtain an index value representing parasympathetic nerve activity. Therefore, the heartbeat interval identification device 10 according to the present embodiment can be effectively used for estimation of an autonomic nervous condition (stress, sleepiness, etc.).

Example 2
FIG. 26 is a block diagram showing an example of a hardware configuration of the heartbeat interval identification device according to the second embodiment.

  The hardware configuration of the heartbeat interval identification device 10A differs from the hardware configuration of the heartbeat interval identification device 10 according to the first embodiment described above in that a motion sensor 90 is added. The other configuration is the same as that of the first embodiment described above, and the same reference numerals as those in FIG.

  The motion sensor 90 may detect a motion of the body of the subject 5 either singly or as a set of a plurality of sensors. Also, the motion sensor 90 may be attached to a person so as not to be movable relative to the subject 5 by, for example, a band, may be carried by the subject 5, or may be arranged at a distance from the subject 5. Good. As a kind of sensor attached to the subject 5, for example, a gyro sensor, an acceleration sensor, etc. are given. Examples of such sensors disposed at a position distant from the subject 5 include an image sensor (camera) and a distance image sensor. In the following, as an example, the motion sensor 90 is assumed to be an acceleration sensor unless otherwise stated. The motion sensor 90 may be integrally formed with the non-contact sensor 70.

  FIG. 27 is a block diagram showing an example of a functional configuration of the heartbeat interval identification device 10A.

  The heart beat interval identifying apparatus 10A has a motion detection unit 105 added to the heart beat interval identifying apparatus 10 according to the first embodiment described above, and the heart beat feature point identifying unit 102 is replaced with a heart beat feature point identifying unit 102A. Is different. The other configuration is the same as that of the heartbeat interval identification device 10 according to the first embodiment described above, and the same reference numerals as those in FIG. The motion detection unit 105 can be realized by the CPU 11 executing one or more programs stored in the ROM 13.

  The motion detection unit 105 detects a predetermined motion of the subject 5 based on a detection signal from the motion sensor 90. The predetermined motion is a motion that significantly affects the amplitude value of the sensor signal feature point (and the filtering signal feature point) described above, and is, for example, a walking motion, a running motion, a standing motion, or the like. The motion detection unit 105 may detect a state in which the body of the subject 5 has a large amount of movement (for example, a state in which the number of times of detection of movement per unit time is a predetermined threshold Th4 or more) based on the detection signal from the movement sensor 90 .

  The heartbeat feature identification unit 102A differs from the heartbeat feature identification unit 102 of the heartbeat interval identification device 10 according to the first embodiment described above in that the heartbeat feature search unit 43 is replaced by a heartbeat feature search unit 43A. The other configurations are the same.

  The heartbeat feature point searching unit 43A changes the heartbeat feature point searching method based on the detection result of the motion detection unit 105. An operation example of the heartbeat feature point search unit 43A will be described below.

  FIG. 28 is a flowchart showing an example of processing executed by the heartbeat interval identifying device 10A. The process shown in FIG. 28 is different from the process shown in FIG. 9 according to the first embodiment described above in that step S2800 is added and step S906 is replaced in step S2802. Since the other processes are the same, the same step numbers are given and the description is omitted.

  In step S2800, the motion detection unit 105 generates information (hereinafter referred to as "motion information") representing the motion of the body of the subject 5 based on the detection signal from the motion sensor 90. A specific example of this process will be described later with reference to FIG.

  In step S2802, the heartbeat feature point identifying unit 102A performs heartbeat feature point identification processing based on the sensor signal obtained in step S900, the filtering signal obtained in step S904, and the motion information obtained in step S2800. Run. A specific example of the heartbeat feature point identification process will be described later with reference to FIG.

  FIG. 29 is a flowchart showing an example of motion detection processing by the motion detection unit 105. The process shown in FIG. 29 is executed as the process of step S2800 shown in FIG.

  In step S2900, the motion detection unit 105 acquires a detection signal (time-series data) of a predetermined period ΔT from the motion sensor 90. The predetermined period ΔT related to the detection signal of the motion sensor 90 is the same as the predetermined period ΔT (see step S900) related to the sensor signal of the non-contact sensor 70. That is, the predetermined period ΔT related to the detection signal of the motion sensor 90 and the predetermined period ΔT related to the sensor signal of the non-contact sensor 70 have the same start time and length.

  In step S2902, the motion detection unit 105 specifies a period in which the subject 5 is performing a predetermined motion based on the detection signal from the motion sensor 90. The predetermined movement is as described above. The method of detecting the predetermined movement is arbitrary, and the predetermined movement can be identified, for example, from the feature of the waveform of the acceleration signal (the detection signal from the movement sensor 90).

  In step S2904, the motion detection unit 105 generates information (motion information) indicating the detection result of the motion detection unit 105. The motion information represents a period in which a predetermined motion is detected. FIG. 30 is a diagram showing an example of motion information. The motion information illustrated in FIG. 30 indicates that the predetermined motion is detected in the period of t0 to t1 and the period of t2 to t3 based on the detection signal of the motion sensor 90 in the period of t0 to t3. Hereinafter, a period in which a predetermined movement is detected by the movement detection unit 105 is referred to as a “predetermined movement detection period”.

  FIG. 31 is a flowchart showing an example of a heartbeat feature point identification process executed by the heartbeat feature point identification unit 102A. The process shown in FIG. 31 is executed as the process of step S2802 shown in FIG. The process shown in FIG. 31 is different from the process shown in FIG. 10 according to the first embodiment described above in that step S3100 is added and step S1014 is replaced in step S3102. Since the other processes are the same, the same step numbers are given and the description is omitted.

  In step S3100, the heartbeat feature point search unit 43A acquires the motion information (see FIG. 30) obtained in step S2800.

  In step S3102, the heartbeat feature point search unit 43A performs the heartbeat feature point determination process using the motion information. A specific example of the heartbeat characteristic point determination process will be described later with reference to FIG.

  FIG. 32 is a flowchart showing an example of a heartbeat feature point determination process performed by the heartbeat feature point search unit 43A. The heartbeat feature point determination process is performed for each filtering signal feature point as described above. Here, the process regarding the filtering signal feature point of the feature point number k will be described.

In step S3200, based on the motion information (see FIG. 30) obtained in step S2800, the heartbeat feature point search unit 43A detects that the time P _{filter_time} (k) of the filtering signal feature point of the feature point number k is predetermined motion It is determined whether it is within a period. For example, in the case of the example shown in FIG. 30, the heart rate feature point search unit 43A determines whether the time P _{filter_time} (k) of the filtering signal feature point is within the period of t0 to t1 or within the period of t2 to t3. judge. If the determination result is "YES", the process proceeds to step S3202, and otherwise, the process proceeds to step S3204.

In step S3202, the heartbeat feature point searching unit 43A executes the heartbeat feature point determination process (see FIG. 19 and the like) according to the third example described above, to thereby implement the heartbeat feature corresponding to the filtering signal feature point of feature point number k. Determine the point time P _{HR_time} (k).

In step S3204, the heartbeat characteristic point determination process (see FIG. 14, FIG. 17, etc.) according to the first or second example described above is performed to obtain the heartbeat characteristic points corresponding to the filtering signal feature points of the characteristic point number k. Determine the time P _{HR_time} (k).

  According to the second embodiment described above, the following effects can be obtained in addition to the effects of the first embodiment described above.

  According to the second embodiment, as described above, the heartbeat feature point searching unit 43A determines the heartbeat feature point according to the third example depending on whether or not the time of the filtering signal feature point is within the predetermined motion detection period. One of processing and heart beat feature point determination processing according to the first or second example is selected. Specifically, when the time of the filtering signal feature point is within the predetermined motion detection period, the heartbeat feature point searching unit 43A selects the heartbeat feature point determination process according to the third example. If the time of the filtering signal feature point is not within the predetermined motion detection period, the heartbeat feature point searching unit 43A selects the heartbeat feature point determination process according to the first or second example.

  Here, when the subject 5 makes a motion that significantly affects the amplitude value of the above-mentioned sensor signal feature point (and the filtering signal feature point, the same applies hereinafter), the sensor signal feature point has an amplitude according to the motion. Peaks (feature points) appear. Therefore, in the heartbeat feature point determination process (a method for determining a heartbeat feature point based on a time difference) according to the first example, a sensor signal feature point that is not a heartbeat when there is much noise due to the movement of the subject 5 The probability of false detection as a heartbeat feature point increases. In addition, in the heartbeat feature point determination process (a method for determining a heartbeat feature point based on an amplitude value difference) according to the second example, another motion (noise due to body motion or the like) that produces an amplitude value of the same size occurs. In such a case, there is a high possibility that the same false detection will occur.

  In this regard, according to the second embodiment, the possibility of such erroneous detection can be reduced. That is, according to the second embodiment, when the time of the filtering signal feature point is within the predetermined motion detection period, the heartbeat feature point determination processing according to the third example is executed. As described above, in the heartbeat feature point determination processing according to the third example, among the sensor signal feature points that satisfy the amplitude determination condition, the sensor signal feature having the closest time to the feature point related to the heartbeat obtained from the filtering signal The points are identified as heart beat feature points. Therefore, according to the second embodiment, even when there is much noise due to the movement of the subject 5, most of the sensor signal feature points due to the noise can be excluded by imposing the amplitude determination condition. Also, according to the second embodiment, another movement that produces an amplitude value of the same magnitude as the sensor signal feature point caused by the heartbeat occurs apart from the time of the sensor signal feature point caused by the heartbeat. In this case, sensor signal feature points due to such noise can be excluded. Thus, according to the second embodiment, the robustness to the movement of the subject 5 can be enhanced. Further, according to the second embodiment, when the time of the filtering signal feature point is not within the predetermined motion detection period, the heartbeat feature point determination process according to the first or second example is selected, so the processing load is efficiently reduced. it can. This is because the processing characteristic of the heartbeat characteristic point determination processing according to the first or second example is smaller than that of the heartbeat characteristic point determination processing according to the third example, as the parameter used for the determination is less.

  As mentioned above, although each Example was explained in full detail, it is not limited to a specific example, A various deformation | transformation and change are possible within the range described in the claim. In addition, it is also possible to combine all or a plurality of the components of the above-described embodiment.

  For example, in the first and second embodiments described above, the filter delay correction unit 40 corrects the filtering signal in order to correct the delay of the filtering signal with respect to the sensor signal, but may be reversed. That is, the filter delay correction unit 40 may correct the sensor signal in order to correct the delay of the filtering signal with respect to the sensor signal.

In addition, the following additional remarks are disclosed regarding the above-mentioned example.
(Supplementary Note 1)
Acquiring a sensor signal generated based on the reflected wave of the detection wave transmitted to the subject's heart,
Identify the frequency of the heart beat based on the acquired sensor signal,
Filtering the sensor signal based on the identified frequency of the heart beat;
When a signal obtained by the filtering process is a filtering signal, a feature point relating to the heartbeat is specified from among a plurality of feature points in the sensor signal based on the feature points relating to the heartbeat in the filtering signal,
The interval of heartbeats is calculated based on the identified feature points related to the heartbeats,
A heart rate interval identification program that causes a computer to execute processing.
(Supplementary Note 2)
Identifying the feature points relating to the heart beat is a difference on the time axis and a difference in amplitude value between the feature points pertaining to the heart beat in the filtering signal and each of a plurality of feature points in the sensor signal. The heartbeat interval identification program according to appendix 1, comprising calculating at least one of them.
(Supplementary Note 3)
Identifying the feature points relating to the heart beat is a feature point of which the difference on the time axis with the feature point relating to the heart beat in the filtering signal is the smallest among the plurality of feature points in the sensor signal. The heartbeat interval identification program according to appendix 2, comprising identifying as a feature point related to a heartbeat.
(Supplementary Note 4)
Among the plurality of feature points in the sensor signal, identifying a feature point relating to the heartbeat is a feature point in which a difference in amplitude value between the filtering signal and the feature point relating to the heartbeat is minimum. The heartbeat interval identification program according to appendix 2, comprising identifying as a feature point pertaining to
(Supplementary Note 5)
Identifying the feature points related to the heartbeat extracts a feature point in which the difference between the amplitude value of the filtered signal and the feature point related to the heartbeat is equal to or less than a predetermined threshold from the plurality of feature points in the sensor signal Appendix 2 includes, among the extracted feature points, specifying, as the feature point related to the heartbeat, a feature point whose difference on the time axis with the feature point related to the heartbeat in the filtered signal is minimum. Heartbeat interval identification program described in.
(Supplementary Note 6)
And causing a computer to further execute a process of correcting a delay of the filtered signal with respect to the sensor signal based on a delay characteristic of a filter used for the filtering process.
The heartbeat interval identification program according to Appendix 2 or 3, wherein the difference on the time axis is a difference after the delay of the filtered signal with respect to the sensor signal is corrected.
(Appendix 7)
Identifying a plurality of feature points related to the heartbeat in the filtered signal;
Identifying the feature points associated with the heartbeat is performed on each of the feature points associated with the heartbeat in the filtered signal;
The heartbeat interval identification program according to any one of appendices 1 to 6, further causing the computer to execute the process.
(Supplementary Note 8)
The computer-implemented method according to any one of appendices 1 to 6, further causing the computer to execute a process of determining a search range for searching a plurality of feature points in the sensor signal based on the feature points related to the heart beat in the filtered signal. Heart rate interval identification program described.
(Appendix 9)
The heart beat interval specifying program according to claim 8, further causing the computer to execute a process of determining the search range based on a sampling rate of the sensor signal and a frequency of the heart beat.
(Supplementary Note 10)
Detecting a predetermined movement of the subject's body;
Changing a method used to specify feature points related to the heartbeat based on the detection result of the movement of the body;
The heartbeat interval identification program according to any one of appendices 1 to 9, further causing the computer to execute the process.
(Supplementary Note 11)
When the movement of the body is detected, a feature point whose difference in amplitude value from the feature point related to the heartbeat in the filtering signal is equal to or less than a predetermined threshold value is extracted from the plurality of feature points in the sensor signal Among the feature points, the feature point having the smallest difference on the time axis with the feature point related to the heartbeat in the filtered signal is specified as the feature point related to the heartbeat,
Among the plurality of feature points in the sensor signal, when the movement of the body is not detected, the feature point with which the difference on the time axis with the feature point related to the heartbeat in the filtering signal is minimized relates to the heartbeat. The heartbeat interval identification program according to Appendix 10, which is identified as a feature point.
(Supplementary Note 12)
When the movement of the body is detected, a feature point whose difference in amplitude value from the feature point related to the heartbeat in the filtering signal is equal to or less than a predetermined threshold value is extracted from the plurality of feature points in the sensor signal Among the feature points, the feature point having the smallest difference on the time axis with the feature point related to the heartbeat in the filtered signal is specified as the feature point related to the heartbeat,
When a movement of the body is not detected, a feature point having a minimum difference in amplitude value from a feature point related to the heartbeat in the filtering signal among a plurality of feature points in the sensor signal is the feature related to the heartbeat The heartbeat interval identification program according to Appendix 10, which is identified as a point.
(Supplementary Note 13)
A sensor signal acquisition unit that acquires a sensor signal generated based on a reflection wave of a detection wave transmitted toward the heart of a subject;
A frequency analysis unit that specifies a heart beat frequency based on the sensor signal;
A filter processing unit that filters the sensor signal based on the frequency of the heartbeat identified by the frequency analysis unit;
When a signal obtained by the filtering process is used as a filtering signal, the heartbeat is specified to identify the heartbeat related feature point among a plurality of feature points in the sensor signal based on the heartbeat related feature point in the filtered signal A feature point identification unit,
A heartbeat interval identifying device, comprising: a heartbeat interval calculation unit that calculates an interval of heartbeats based on the feature point related to the heartbeat identified by the heartbeat feature point identifying unit.
(Supplementary Note 14)
Acquiring a sensor signal generated based on the reflected wave of the detection wave transmitted to the subject's heart,
Identify the frequency of the heart beat based on the acquired sensor signal,
Filtering the sensor signal based on the identified frequency of the heart beat;
When a signal obtained by the filtering process is a filtering signal, a feature point relating to the heartbeat is specified from among a plurality of feature points in the sensor signal based on the feature points relating to the heartbeat in the filtering signal,
A computer implemented heartbeat interval identifying method comprising calculating an interval of heartbeats based on the identified feature points related to the heartbeats.

10, 10A heart rate interval identification device
31 Frequency analysis unit
32 Filter processing unit
40 filter delay correction unit 41 filtering signal feature point detection unit 42 sensor signal feature point detection unit 43, 43 A heartbeat feature point search unit 70 non-contact sensor
DESCRIPTION OF SYMBOLS 100 Sensor signal acquisition part 101 Heart rate component detection part 102, 102A Heart rate characteristic point identification part 103 Heart rate interval calculation part 104 Output part
105 Motion Detection Unit

Claims (9)

Acquiring a sensor signal generated based on the reflected wave of the detection wave transmitted to the subject's heart,
Identify the frequency of the heart beat based on the acquired sensor signal,
Filtering the sensor signal based on the identified frequency of the heart beat;
When a signal obtained by the filtering process is a filtering signal , at least one of time and amplitude is similar to a feature point related to the heartbeat in the filtering signal among a plurality of feature points in the sensor signal Identifying feature points as feature points pertaining to the heart beat;
The interval of heartbeats is calculated based on the identified feature points related to the heartbeats,
A heart rate interval identification program that causes a computer to execute processing.   Identifying the feature points relating to the heart beat is a difference on the time axis and a difference in amplitude value between the feature points pertaining to the heart beat in the filtering signal and each of a plurality of feature points in the sensor signal. The heartbeat interval identification program according to claim 1, comprising calculating at least one of them.   Identifying the feature points relating to the heart beat is a feature point of which the difference on the time axis with the feature point relating to the heart beat in the filtering signal is the smallest among the plurality of feature points in the sensor signal. The heartbeat interval identification program according to claim 2, comprising identifying as a feature point related to a heartbeat.   Among the plurality of feature points in the sensor signal, identifying a feature point relating to the heartbeat is a feature point in which a difference in amplitude value between the filtering signal and the feature point relating to the heartbeat is minimum. The heartbeat interval identification program according to claim 2, comprising identifying as a feature point pertaining to   Identifying the feature points related to the heartbeat extracts a feature point in which the difference between the amplitude value of the filtered signal and the feature point related to the heartbeat is equal to or less than a predetermined threshold from the plurality of feature points in the sensor signal And identifying, among the extracted feature points, a feature point whose difference on the time axis with the feature point related to the heartbeat in the filtered signal is minimum as the feature point related to the heartbeat. The heart rate interval identification program described in 2. And causing a computer to further execute a process of correcting a delay of the filtered signal with respect to the sensor signal based on a delay characteristic of a filter used for the filtering process.
The heartbeat interval identification program according to claim 2 or 3, wherein the difference on the time axis is a difference after the delay of the filtered signal with respect to the sensor signal is corrected. Detecting a predetermined movement of the subject's body;
Changing a method used to specify feature points related to the heartbeat based on the detection result of the movement of the body;
The heartbeat interval identification program according to any one of claims 1 to 6, further causing the computer to execute the process. A sensor signal acquisition unit that acquires a sensor signal generated based on a reflection wave of a detection wave transmitted toward the heart of a subject;
A frequency analysis unit that specifies a heart beat frequency based on the sensor signal;
A filter processing unit that filters the sensor signal based on the frequency of the heartbeat identified by the frequency analysis unit;
When a signal obtained by the filtering process is a filtering signal , at least one of time and amplitude is similar to a feature point related to the heartbeat in the filtering signal among a plurality of feature points in the sensor signal A heartbeat feature point identifying unit that identifies feature points as feature points related to the heartbeat;
A heartbeat interval identifying device, comprising: a heartbeat interval calculation unit that calculates an interval of heartbeats based on the feature point related to the heartbeat identified by the heartbeat feature point identifying unit. Acquiring a sensor signal generated based on the reflected wave of the detection wave transmitted to the subject's heart,
Identify the frequency of the heart beat based on the acquired sensor signal,
Filtering the sensor signal based on the identified frequency of the heart beat;
When a signal obtained by the filtering process is a filtering signal , at least one of time and amplitude is similar to a feature point related to the heartbeat in the filtering signal among a plurality of feature points in the sensor signal Identifying feature points as feature points pertaining to the heart beat ;
A computer implemented heartbeat interval identifying method comprising calculating an interval of heartbeats based on the identified feature points related to the heartbeats.

Images