JP2014200389A - Heartbeat measurement program, heartbeat measurement method, and heartbeat measurement apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To shorten measuring time of heartbeat.SOLUTION: A heartbeat measurement apparatus 10 acquires an image obtained by imaging an organism, extracts a biological region included in the image, and detects a waveform from a signal of a representative value for each wavelength component of each pixel included in the biological region. The heartbeat measurement apparatus 10 also outputs a pulse corresponding to a peak for each peak obtained from the waveform, and selects a pulse set whose time difference between pulse strings having pulses in a row is within a predetermined range. The heartbeat measurement apparatus 10 counts the first number of times in which the output of the pulse exists at the point of time corresponding to integral multiplication of a cycle of the pulse set or in the vicinity of the point of time, and the second number of times in which the output of the pulse does not exist at the point of time or in the vicinity of the point of time for each pulse set. Furthermore, the heartbeat measurement apparatus 10 extracts the pulse set by comparing the difference between the first number of times and the second number of times between each pulse set, and measures a heartbeat cycle from the extracted cycle of the pulse set.

Description

  The present invention relates to a heart rate measurement program, a heart rate measurement method, and a heart rate measurement device.

  A technique for detecting blood volume fluctuations, so-called pulse waves, is known. For example, as an example of a technique for detecting a heart rate that is one aspect of a pulse wave, a peak of an electrocardiogram waveform measured by attaching an electrode of an electrocardiograph to a living body, such as a P wave or an R wave is used. An electrocardiogram that detects the heart rate. Another example is a photoelectric pulse that irradiates a peripheral blood vessel such as a finger or earlobe with infrared rays and detects a pulse that is approximately equivalent to a heartbeat from an optical change in which the reflected light periodically varies depending on blood flow and light absorption characteristics. The wave method is mentioned.

  When these electrocardiograms and photoelectric pulse wave methods are used, the electrodes are attached to the living body or the photosensitive surface is brought into close contact with the living body. Therefore, detection is difficult unless the measuring instrument is in contact with the living body. There is a problem that it is troublesome to live everyday life with the equipment attached.

  For this reason, in order to measure the heart rate without the measurement instrument coming into contact with the living body, a heart rate measurement method using an image taken by the subject has been proposed. In such a heart rate measuring method, an independent component analysis (ICA) is applied to a signal component of an image obtained by photographing a subject's face by a camera, and then the heart rate is measured by converting the signal into a frequency component. . The aim is to improve the signal-to-noise ratio by applying such independent component analysis.

“Non-contact, automated cardiac pulse measurements using video imaging and blind source separation”, OPTICS EXPRESS Vol.18, No.10, 2010

  However, the above technique has a limit in shortening the measurement time of the heartbeat.

  That is, in the above heart rate measuring method, DFT (Discrete Fourier Transform), so-called discrete Fourier transform, is applied to heart rate measurement. However, in frequency analysis such as DFT, it is difficult to achieve the target accuracy unless a signal measured over a certain time is input. Therefore, in the above heart rate measurement method, DFT cannot be applied until a signal is collected over a certain period of time, and there is a limit in reducing the time required to obtain a heart rate measurement result.

  In one aspect, an object of the present invention is to provide a heart rate measurement program, a heart rate measurement method, and a heart rate measurement device that can shorten a heart rate measurement time.

  The heart rate measurement program according to one aspect acquires, from a computer, an image in which a living body is photographed, extracts a living body region included in the image, and uses a representative value signal for each wavelength component of each pixel included in the living body region. A process for detecting a waveform is executed. Further, the computer outputs a pulse corresponding to the peak for each peak obtained from the waveform, and executes a process of selecting a pulse set whose time difference is within a predetermined range from a pulse train including the pulses. . Further, in the computer, for each pulse set, a first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the time point or the vicinity of the time point The process of counting the second number of times when the pulse output does not exist is executed. Further, the computer extracts a pulse set by comparing the difference between the first number and the second number of times between each pulse set, and measures a heartbeat period from the period of the extracted pulse set. Is executed.

  According to one embodiment, the measurement time of the heartbeat can be shortened.

FIG. 1 is a block diagram illustrating a functional configuration of the heartbeat measuring apparatus according to the first embodiment. FIG. 2 is a diagram illustrating an example of a face image. FIG. 3 is a diagram illustrating an example of the spectrum of each signal of the G signal and the R signal. FIG. 4 is a diagram illustrating an example of a spectrum of each signal of the R component multiplied by the G component and the correction coefficient k. FIG. 5 is a diagram illustrating an example of a spectrum after calculation. FIG. 6 is a block diagram showing a functional configuration of the waveform detection unit shown in FIG. FIG. 7 is a diagram illustrating an example of a pulse set. FIG. 8 is a diagram illustrating an example of a pulse set. FIG. 9 is a diagram illustrating an example of a pulse set. FIG. 10 is a diagram illustrating another example of a pulse set. FIG. 11 is a diagram illustrating another example of a pulse set. FIG. 12 is a flowchart illustrating the procedure of the waveform detection process according to the first embodiment. FIG. 13 is a flowchart illustrating the procedure of the heart rate measurement process according to the first embodiment. FIG. 14 is a flowchart illustrating the procedure of the heart rate measurement process according to the first embodiment. FIG. 15 is a schematic diagram illustrating an example of a computer that executes a heart rate measurement program according to the first and second embodiments.

  Hereinafter, a heart rate measurement program, a heart rate measurement method, and a heart rate measurement device according to the present application will be described with reference to the accompanying drawings. Note that this embodiment does not limit the disclosed technology. Each embodiment can be appropriately combined within a range in which processing contents are not contradictory.

[Configuration of heart rate measurement device]
First, the functional configuration of the heartbeat measuring device according to the present embodiment will be described. FIG. 1 is a block diagram illustrating a functional configuration of the heartbeat measuring apparatus according to the first embodiment. The heartbeat measuring device 10 shown in FIG. 1 uses the image obtained by photographing the living body of the subject without bringing the measuring instrument into contact with the subject under ordinary environmental light such as sunlight or room light, that is, the heartbeat of the subject. A heart rate measurement process for measuring a change in blood volume accompanying a heart beat is executed.

  As an aspect, the heart rate measuring apparatus 10 can be implemented by installing a heart rate measuring program in which the above heart rate measuring process is provided as package software or online software on a desired computer. For example, not only mobile communication terminals such as smartphones, mobile phones, and PHS (Personal Handyphone System) but also mobile terminal devices including digital cameras, tablet terminals, and slate terminals that do not have the ability to connect to mobile communication networks. Install a heart rate measurement program. Accordingly, the mobile terminal device can function as the heartbeat measuring device 10. Here, the portable terminal device is illustrated as an example of implementation of the heart rate measuring device 10, but the heart rate measuring program can be installed in a stationary terminal device such as a personal computer.

  As shown in FIG. 1, the heartbeat measuring device 10 includes a camera 11, a touch panel 12, an acquisition unit 13, a region extraction unit 14, a waveform detection unit 15, a pulse output unit 16a, a pulse train extraction unit 16b, A pair selection unit 16c. Furthermore, the heartbeat measuring device 10 includes a first counting unit 17a, a second counting unit 17b, a set extracting unit 18, and a heartbeat measuring unit 19.

  The heartbeat measuring device 10 may have various functional units included in a known computer in addition to the functional units shown in FIG. For example, when the heartbeat measuring device 10 is mounted as a stationary terminal, it may further include input / output devices such as a keyboard, a mouse, and a display. Moreover, when the heart rate measuring apparatus 10 is mounted as a tablet terminal or a slate terminal, it may further include an acceleration sensor or an angular velocity sensor. Further, when the heart rate measuring device 10 is mounted as a mobile communication terminal, it further includes functional units such as an antenna, a wireless communication unit connected to the mobile communication network, and a GPS (Global Positioning System) receiver. It doesn't matter.

  The camera 11 is an imaging device equipped with an imaging element such as a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). For example, the camera 11 can be equipped with three or more light receiving elements such as R (red), G (green), and B (blue). As an example of mounting the camera 11, a digital camera or a Web camera may be connected via an external terminal. As another implementation example, when a camera is mounted from the time of shipment, such as an in camera or an out camera, the camera can be used. In addition, although the case where the heartbeat measuring apparatus 10 has the camera 11 is illustrated here, the heartbeat measuring apparatus 10 does not necessarily have the camera 11 when an image can be acquired via a network or a storage device. .

  The touch panel 12 is a displayable and inputable device. As one aspect, the touch panel 12 displays an image output by an OS (Operating System) or an application program, including a heart rate measurement program executed on the heart rate measurement device 10. As another aspect, the touch panel 12 receives a touch operation such as a tap, a flick, a sweep, a pinch-in, and a pinch-out performed on the screen of the screen. Here, the touch panel 12 is illustrated as an input device for inputting instructions to the heartbeat measuring device 10, but the present invention is not limited thereto, and further includes a physical key for realizing complementary input with the touch panel 12. May be.

  Here, when the above heart rate measurement program is activated, an image of a subject who can easily detect a pulse wave is captured by the camera 11 through image display on the touch panel 12 or sound output from a speaker (not shown). An image shooting operation can be guided. For example, when the heart rate measurement program is activated via the touch panel 12, the camera 11 is activated. In response to this, the camera 11 starts photographing the subject accommodated in the photographing range of the camera 11. At this time, when shooting an image showing the face of the subject, the heart rate measurement program displays the image taken by the camera 11 on a display device (not shown) and displays the target position showing the subject's nose as an aim. You can also. As a result, among the facial parts such as the subject's eyes, ears, nose and mouth, an image in which the subject's nose is within the center of the imaging range can be taken. Then, the heartbeat measurement program stores an image in which the subject's face is captured by the camera 11 in the acquisition unit 13. The above guidance may not necessarily be executed, and the subject's face is photographed while the subject is viewing a screen displayed on the screen, for example, an image or a video output by the operating system or application program. You can also Thus, the subject can be photographed in the background without being aware of the photographing. In the following, an image showing a face may be referred to as a “face image”.

  The acquisition unit 13 is a processing unit that acquires an image. As one aspect, the acquisition unit 13 acquires a face image captured by the camera 11. As another aspect, the acquisition unit 13 can also acquire an image from an auxiliary storage device such as a hard disk or an optical disk that accumulates face images, or a removable medium such as a memory card or a USB (Universal Serial Bus) memory. As a further aspect, the acquisition unit 13 can also acquire a face image by receiving from an external device via a network. In addition, although the acquisition part 13 illustrated the case where a process is performed using image data, such as two-dimensional bitmap data obtained from the output by image pick-up elements, such as CCD and CMOS, and vector data, it outputs from one detector. It is also possible to acquire the processed signal as it is and execute the subsequent processing.

  The region extraction unit 14 is a processing unit that extracts a biological region from the image acquired by the acquisition unit 13. As one aspect, a living body region based on a predetermined facial part is extracted from the face image. For example, the region extraction unit 14 performs image processing such as template matching on the face image, thereby performing a facial organ such as the subject's eyes, ears, nose, and mouth, that is, a specific facial part, that is, the subject's face. Detect nose. In addition, the region extraction unit 14 extracts a biological region included in a predetermined range from the center with the subject's nose as the center. As a result, an image of a living body region including the subject's nose and a part of the face center of the cheek located around the nose is extracted as an image used for pulse wave detection. Thereafter, the region extraction unit 14 outputs an image of the biological region extracted from the original image to the waveform detection unit 15.

  FIG. 2 is a diagram illustrating an example of a face image. FIG. 2 shows a block in which a region including part or all of the subject's eyes, nose and mouth shown in the face image is divided into nine. Among the blocks shown in FIG. 2, the subject's eyes are shown in the upper left and right blocks. When the images of these blocks are used for detection, blinking of the eyes may cause noise to cause a decrease in heart rate detection accuracy. Also, the subject's mouth is shown in the lower three blocks of the blocks shown in FIG. When the images of these blocks are used for detection, the movement of the mouth may become noise, leading to a decrease in heart rate detection accuracy. On the other hand, the middle block shown in FIG. 2, that is, the block with hatched lines is separated from the block showing the eyes and mouth, and may contain noise components compared to other blocks. Therefore, good detection results can be expected. Accordingly, the region extraction unit 14 extracts an image of the middle block shown in FIG. 2 from the original image as an image of the biological region. The blocks to be extracted are not limited to the middle block in the middle stage, and may be three blocks including the right and left of the middle stage.

  After extracting the image of the living body region, the region extracting unit 14 performs predetermined statistical processing on the pixel value of each pixel included in the living body region. For example, the region extraction unit 14 averages the pixel value of each pixel included in the biological region for each wavelength component. In addition to the average value, the median value and the mode value may be calculated. In addition to the average value, an arbitrary averaging process such as a weighted average or a moving average may be executed. Thereby, the average value of the pixel values of each pixel included in the living body region is calculated for each wavelength component as a representative value representing the living body region.

  The waveform detection unit 15 is a processing unit that detects a waveform of a pulse wave signal from a representative value signal for each wavelength component of each pixel included in a living body region that is a pulse wave detection target. As an aspect, the waveform detection unit 15 can take a pulse wave between each wavelength component from a representative value signal for each wavelength component of each pixel included in the living body region by executing the following waveform detection processing. A waveform of a pulse wave signal in which components in a specific frequency band other than the pulse wave frequency band are canceled is detected. For example, the waveform detection unit 15 is a time series of representative values of two wavelength components of the three wavelength components included in the image, that is, the R component, the G component, and the B component, which have different blood absorption specifications. The waveform is detected using the data.

  Specifically, there are capillaries on the face, and when the blood flow flowing through the blood vessels changes due to the heartbeat, the amount of light absorbed by the blood flow also changes according to the heartbeat. The brightness obtained by reflection also changes with the heartbeat. Although the amount of change in luminance is small, when the average luminance of the entire face region is obtained, the time-series data of luminance includes a pulse wave component. However, the luminance also changes due to body movement in addition to the pulse wave, and this becomes a noise component of pulse wave detection, so-called body movement artifact. Therefore, a pulse wave is detected at two or more wavelengths having different light absorption characteristics of blood, for example, a G component having a high light absorption characteristic (about 525 nm) and an R component having a low light absorption characteristic (about 700 nm). Since the heart rate is in the range of 30 bpm to 240 bpm when converted to 0.5 Hz to 4 Hz per minute, other components can be regarded as noise components. Assuming that noise does not have wavelength characteristics or is minimal even if it is present, components other than 0.5 Hz to 4 Hz should be equal between the G signal and the R signal. Is different. Therefore, by correcting the sensitivity difference between components other than 0.5 Hz to 4 Hz and subtracting the R component from the G component, the noise component can be removed and only the pulse wave component can be extracted.

  For example, the G component and the R component can be represented by the following formula (1) and the following formula (2). “Gs” in the following equation (1) indicates the pulse wave component of the G signal, “Gn” indicates the noise component of the G signal, and “Rs” in the following equation (2) indicates the R signal. “Rn” indicates the noise component of the R signal. Further, since the noise component has a sensitivity difference between the G component and the R component, the correction coefficient k for the sensitivity difference is expressed by the following equation (3).

Ga = Gs + Gn (1)
Ra = Rs + Rn (2)
k = Gn / Rn (3)

  When the sensitivity difference is corrected and the R component is subtracted from the G component, the pulse wave component S is expressed by the following equation (4). When this is transformed into the formula represented by Gs, Gn, Rs and Rn using the above formula (1) and the above formula (2), the following formula (5) is obtained, and further, the above formula (3 ) To eliminate k and arrange the equations, the following equation (6) is derived.

S = Ga-kRa (4)
S = Gs + Gn−k (Rs + Rn) (5)
S = Gs− (Gn / Rn) Rs (6)

  Here, the G signal and the R signal have different light absorption characteristics, and Gs> (Gn / Rn) Rs. Therefore, the pulse wave component S from which noise is removed can be calculated by the above equation (6).

  FIG. 3 is a diagram illustrating an example of the spectrum of each signal of the G signal and the R signal. The vertical axis of the graph shown in FIG. 3 indicates the signal intensity, and the horizontal axis indicates the frequency (bpm). As shown in FIG. 3, since the sensitivity of the image sensor differs between the G component and the R component, the signal intensities of the two differ. On the other hand, in both the R component and the G component, there is no change in that noise appears outside the range of 30 bpm to 240 bpm, particularly in a specific frequency band of 3 bpm or more and less than 20 bpm. For this reason, as shown in FIG. 3, the signal intensity corresponding to the designated frequency Fn included in the specific frequency band of 3 bpm or more and less than 20 bpm can be extracted as Gn and Rn. The sensitivity difference correction coefficient k can be derived from these Gn and Rn.

  FIG. 4 is a diagram illustrating an example of a spectrum of each signal of the R component multiplied by the G component and the correction coefficient k. In the example of FIG. 4, the result of multiplying the absolute value of the correction coefficient is shown for convenience of explanation. Also in the graph shown in FIG. 4, the vertical axis indicates the signal strength, and the horizontal axis indicates the frequency (bpm). As shown in FIG. 4, when the spectrum of each signal of the G component and the R component is multiplied by the correction coefficient k, the sensitivity is uniform between the components of the G component and the R component. In particular, the spectrum signal intensity in a specific frequency band is almost the same in most spectrum signals. On the other hand, in the peripheral region 400 of the frequency where the pulse wave is actually included, the signal intensity of the spectrum is not uniform between the G component and the R component.

  FIG. 5 is a diagram illustrating an example of a spectrum after calculation. In FIG. 5, the scale of the signal intensity, which is the vertical axis, is enlarged from the viewpoint of improving the visibility of the frequency band in which the pulse wave appears. As shown in FIG. 5, when the spectrum of the R signal after the multiplication of the correction coefficient k is subtracted from the spectrum of the G signal, a signal in which a pulse wave appears due to the difference in the light absorption characteristics between the G component and the R component. It can be seen that the noise component is reduced with the strength of the component maintained as much as possible. In this way, the waveform of the pulse wave signal from which only the noise component is removed can be detected.

  Next, the functional configuration of the waveform detector 15 will be described more specifically. FIG. 6 is a block diagram showing a functional configuration of the waveform detection unit 15 shown in FIG. As shown in FIG. 6, the waveform detection unit 15 includes BPF (Band-Pass Filter) 152R and 152G, extraction units 153R and 153G, LPF (Low-Pass Filter) 154R and 154G, a calculation unit 155, and a BPF 156R. 156G, a multiplication unit 157, and a calculation unit 158. In the example of FIGS. 3 to 5, the example in which the pulse wave is detected in the frequency space has been described. However, in FIG. 6, the noise in the time series space is reduced from the viewpoint of reducing the time required for the conversion to the frequency component. The functional structure in the case of detecting a pulse wave by canceling components is shown.

  For example, time-series data of R signals whose signal values are representative values of R component pixel values of pixels included in the living body region are input from the region extracting unit 14 to the waveform detecting unit 15. The time series data of the G signal having the representative value of the G component pixel value of each pixel included in the signal as the signal value is input. Among these, the R signal of the living body region is input to the BPF 152R and the BPF 156R in the waveform detecting unit 15, and the G signal of the living body region is input to the BPF 152G and the BPF 156G in the waveform detecting unit 15.

  Each of BPF 152R, BPF 152G, BPF 156R, and BPF 156G is a band-pass filter that passes only signal components in a predetermined frequency band and removes signal components in other frequency bands. These BPF 152R, BPF 152G, BPF 156R, and BPF 156G may be implemented by hardware, or may be implemented by software.

  The difference in the frequency band that the BPF passes will be described. The BPF 152R and the BPF 152G pass a signal component in a specific frequency band in which a noise component appears more noticeably than other frequency bands.

  Such a specific frequency band can be determined by comparing with a frequency band that can be taken by a pulse wave. An example of a frequency band that can be taken by a pulse wave is a frequency band of 0.5 Hz to 4 Hz, and a frequency band of 30 bpm to 240 bpm when converted per minute. From this, as an example of the specific frequency band, a frequency band of less than 0.5 Hz and more than 4 Hz that cannot be measured as a pulse wave can be employed. Further, the specific frequency band may partially overlap with the frequency band that can be taken by the pulse wave. For example, it is allowed to overlap with the frequency band that the pulse wave can take in the section of 0.7 Hz to 1 Hz that is difficult to be measured as a pulse wave, and the frequency band of less than 1 Hz and 4 Hz or more is adopted as the specific frequency band. You can also Further, the specific frequency band can be narrowed down to a frequency band in which noise is more noticeable with the frequency band of less than 1 Hz and 4 Hz or more as the outer edge. For example, noise appears more noticeably in a low frequency band lower than a frequency band that can take a pulse wave, rather than a high frequency band that is higher than a frequency band that the pulse wave can take. For this reason, a specific frequency band can also be narrowed down to a frequency band of less than 1 Hz. Further, since there are many differences in the sensitivity of the image sensor of each component in the vicinity of the direct current component where the spatial frequency is zero, the specific frequency band can be narrowed down to a frequency band of 0.05 Hz to less than 1 Hz. Furthermore, the specific frequency band can be narrowed down to a frequency band of 0.05 Hz or more and 0.3 Hz or less where noise such as flickering of ambient light other than human body movement, for example, blinking or shaking of the body, is likely to appear.

  Here, as an example, the following description will be given assuming that the BPF 152R and the BPF 152G pass a signal component in a frequency band of 0.05 Hz to 0.3 Hz as a specific frequency band. Here, the case where a bandpass filter is used to extract a signal component in a specific frequency band is illustrated, but a low-pass filter is used when a signal component in a frequency band below a certain frequency is extracted. You can also.

  On the other hand, the BPF 156R and the BPF 156G pass signal components in a frequency band that can be taken by a pulse wave, for example, a frequency band of 0.5 Hz to 4 Hz. Hereinafter, a frequency band that can be taken by a pulse wave may be referred to as a “pulse wave frequency band”.

  The extraction unit 153R extracts the absolute intensity value of the signal component in the specific frequency band of the R signal. For example, the extraction unit 153R extracts the absolute intensity value of the signal component in the specific frequency band by performing an absolute value calculation process on the signal component in the specific frequency band of the R component. Further, the extraction unit 153G extracts the absolute intensity value of the signal component in the specific frequency band of the G signal. For example, the extracting unit 153G extracts the absolute intensity value of the signal component in the specific frequency band by executing an absolute value calculation process on the signal component in the specific frequency band of the G component.

  The LPF 154R and the LPF 154G are low-pass filters that perform a smoothing process that responds to time changes on time-series data of absolute intensity values in a specific frequency band. For example, a signal component in a frequency band of 0.1 Hz or less is passed. The LPF 154R and the LPF 154G are the same except that the signal input to the LPF 154R is an R signal and the signal input to the LPF 154G is a G signal. By such smoothing processing, absolute value intensities R′n and G′n in a specific frequency band are obtained.

  The calculation unit 155 divides the absolute value strength G′n of the specific frequency band of the G signal output by the LPF 154G by the absolute value strength R′n of the specific frequency band of the R signal output by the LPF 154R. n / R'n "is executed. Thereby, a correction coefficient k for the sensitivity difference is calculated.

  The multiplier 157 multiplies the signal component in the pulse wave frequency band of the R signal output from the BPF 156R by the correction coefficient k calculated by the calculator 155.

  The calculating unit 158 subtracts the signal component of the pulse wave frequency band of the R signal multiplied by the correction coefficient k by the multiplier 157 from the signal component of the pulse wave frequency band of the G signal output by the BPF 156G “Gs−k”. * Rs "is executed. The time-series data of the signal thus obtained corresponds to the waveform of the facial pulse wave signal, and the sampling frequency thereof corresponds to the frame frequency at which the image is captured. Hereinafter, the waveform of the pulse wave signal may be referred to as “pulse wave waveform”.

  The pulse output unit 16a is a processing unit that outputs a pulse corresponding to the peak for each peak obtained from the pulse wave waveform. As an aspect, every time the waveform detector 15 outputs an amplitude value of a pulse waveform, the pulse output unit 16a has a pulse wave at a sampling time t-1 one sampling before the sampling time t of the amplitude value. It is determined whether or not the amplitude value of the waveform is a maximum value. At this time, the pulse output unit 16a outputs a pulse to the pulse train extraction unit 16b when the amplitude value of the pulse wave waveform at the sampling time t-1 is the maximum point, that is, the peak of the pulse wave waveform.

  For example, the amplitude value of the latest sampling time t is v (t), the amplitude value of the sampling time t−1 output immediately before v (t) is v (t−1), and v (t) The amplitude value of the sampling time t-2 output two times before is assumed to be v (t-2). Under such a premise, when the amplitude value v (t) is output at the latest sampling time t, the pulse output unit 16a determines that the amplitude value v (t−1) at the sampling time t−1 is the following conditional expression ( 7) Whether or not the condition is satisfied, that is, whether or not the amplitude value v (t−1) is a maximum point is determined. At this time, when the amplitude value v (t−1) at the sampling time t−1 satisfies the following conditional expression (7), the amplitude value has started to decrease after increasing before and after the sampling time t−1. Becomes clear. Therefore, the amplitude value v (t−1) can be regarded as the maximum value, and the sampling time t−1 can be regarded as the maximum point. In this case, the pulse output unit 16a outputs a pulse to the pulse train extraction unit 16b.

  v (t−2) ≦ v (t−1) and v (t−1)> v (t) (7)

  The pulse train extraction unit 16b is a processing unit that extracts a pulse train used for heartbeat measurement from a pulse train in which pulses output from the pulse output unit 16a are continuous. As one aspect, the pulse train extraction unit 16b extracts a pulse train used for heartbeat measurement by excluding pulses that were output during a period when an operation on the touch panel 12 or a physical key (not shown) was performed. .

  More specifically, the pulse train extraction unit 16b executes an operation period monitoring for monitoring the start and end of the operation of the touch panel 12 and the physical keys in the background of the pulse train extraction described above. For example, when the operation of the touch panel 12 or the physical key is started, the pulse train extraction unit 16b stores the time when the operation is started in the internal memory as the operation start time. Thereafter, the pulse train extraction unit 16b determines that the operation has been completed when the operation has not been performed for a predetermined period, for example, 5 seconds after the operation is started. In this case, the pulse train extraction unit 16b stores the time when the operation is ended as an operation end time in the internal memory. Hereinafter, a period determined by the operation start time and the operation end time may be referred to as an “operation period”.

  Further, in addition to the above monitoring of the operation period, the pulse train extraction unit 16b also performs processing for storing the sampling time during which the pulse is output in an internal memory (not shown) every time the pulse is output by the pulse output unit 16a. Run.

  In this manner, the pulse train extraction unit 16b is output during the operation period each time the amplitude value of the pulse wave waveform is output by the waveform detection unit 15 while the operation period is monitored and the pulse is stored in the background. The pulse train used for measuring the heartbeat is extracted by excluding the pulse. For example, the pulse train extraction unit 16b excludes the sampling time included in the operation period stored in the internal memory from the sampling time stored in the internal memory, and extracts the remaining sampling time.

  As a result, the pulses output in the operation period are removed from the pulse train in which the pulses output by the pulse output unit 16a are connected. As a result, extraction is performed by narrowing down to the pulse train that was not output during the operation period. become. When extraction is performed by narrowing down to such a pulse train, for example, a pulse derived from a face image captured in a state where the heartbeat measuring device 10 is swung by operating the touch panel 12 or a physical key is measured for heartbeat. It is possible to suppress the situation used for

  Further, the pulse train extraction unit 16b is configured to provide a pulse for two beats when it is assumed that the time zone is a predetermined section length, for example, the heart rate of a standard person is 60 bpm or more even in a time zone not included in the operation period. If it is less than 2 seconds that can be collected, the pulse of the sampling time included in the time zone is not extracted. This is because, for heart rate measurement, a plausible set of pulses is extracted from the pulse train using the periodicity of the heart beats at approximately the same time interval, but the length of time used for the measurement is insufficient. This is because a set of pulses forming one beat is not included in some cases, which causes a reduction in accuracy. The section length is not limited to the above value, and any value can be adopted. For example, in the above example, the case is described in which the section length is set under the assumption that the heart rate is 60 bpm. However, the heart rate of a person whose heart rate cycle is longer than a standard person, for example, a person with a sports heart The section length may be set based on the number.

  Thereafter, the pulse train extraction unit 16b performs a period from the first sampling time to the last sampling time stored in the internal memory, that is, a period excluded from the previous extraction from the pulse sampling period, for example, a time that is less than the section length. Subtract the sum of the belt and operation period. In this way, a pulse train collection period that can be used for heart rate measurement is obtained. In addition, the pulse train extraction unit 16b can acquire a pulse of 4 beats or more when the pulse train collection period after the subtraction is performed is a predetermined measurement period, for example, when the heart rate of a standard person is 60 bpm or more. It is determined whether or not. Then, when the pulse train collection period is equal to or longer than the predetermined measurement period, the pulse train extractor 16b outputs a set of sampling times, that is, a pulse train to the group selector 16c described later.

  This eliminates pulses derived from face images that contain motion components other than pulse waves, but collects only pulses derived from face images that can be estimated to have fewer motion components other than pulse waves to measure heart rate. Can be used. Therefore, the heart rate can be measured without making the subject aware of whether the heart rate is being measured by the heart rate measuring device 10 or not.

  In addition, said measurement period is not limited to said value, Arbitrary values can be employ | adopted. For example, in the above example, the case is described in which the section length is set under the assumption that the heart rate is 60 bpm. However, the heart rate of a person whose heart rate cycle is longer than a standard person, for example, a person with a sports heart The section length may be set based on the number. In addition, since the measurement accuracy of the heartbeat can be expected to be improved as the measurement period is longer, when measuring a heartbeat with high accuracy, it can be set longer than the above value.

  The group selection unit 16c is a processing unit that selects a pulse group whose time difference is within a predetermined range from the pulse train. As an aspect, the group selection unit 16c selects the following pulse group until there is no pulse group that can be combined with each other within the above time difference after the pulse train extraction unit 16b extracts the pulse train. Run repeatedly. For example, the set selection unit 16c refers to the sampling time of the pulses included in the pulse train extracted by the pulse train extraction unit 16b, and selects a pair of two pulses whose difference in sampling time is within a predetermined range. In addition, as an example of a range, the range of 0.25 second-2 second corresponding to 0.5 Hz-4 Hz is employable.

  For each pulse set, the first counting unit 17a has a first number of times that a pulse output exists in the vicinity of a time point corresponding to an integer multiple of the period of the pulse set, and a pulse output exists in the time point or in the vicinity of the time point. It is a processing unit that counts the second number of times of not. As an aspect, the first counting unit 17a repeatedly executes the following counting every time a pulse set is selected by the set selecting unit 16c. In the following, for convenience of explanation, the pulse having the smaller sampling time value in the pulse set is denoted as pulse P1, the pulse having the larger sampling time is denoted as pulse P2, and these sampling times are denoted as pulse P2. Sometimes expressed as tp1 and tp2 (tp1 <tp2). For example, the first counting unit 17a calculates the time interval of the pulse set, that is, the period T (= tp2-tp1) by subtracting the sampling time tp1 of the pulse P1 from the sampling time tp2 of the pulse P2.

  Subsequently, the first counting unit 17a calculates a time point corresponding to an integral multiple of the period of the pulse set by the following equation (8). “N” in the following formula (8) is a positive or negative integer. In addition, “T” in the following equation (8) is the period of the previously obtained pulse group. In addition, “α” in the following equation (8) is an allowable error, and can be derived, for example, by the following equation (9). The permissible error α means that even if there is no pulse output at the time point calculated by the following equation (8), it is sufficient that the pulse output exists within a range of ± α before and after the time point.

tp1 + nT ± α (8)
α = 0.1 × (tp1 + nT) (9)

  Here, the time point at which the period of the pulse set arrives for every integer n can be predicted by the above equation (8). From such a meaning, a time point corresponding to an integral multiple of the period of the pulse set may be described as a “period arrival point”.

  After calculating the periodic arrival point, the first counting unit 17a determines whether or not a pulse output exists within the allowable error α before and after the periodic arrival point for each periodic arrival point. That is, it is determined whether or not there is a pulse whose sampling time is within the period arrival point ± α.

  At this time, the first counting unit 17a increments the first number by one when there is a pulse whose sampling time is included within the period arrival point ± α. Thus, when a pulse output exists within the range of the allowable error α before and after the arrival point of the cycle, it means that the pulse is output at a timing that matches the cycle of the pulse set. From this point of view, the first number of times may be described as “number of times of coincidence” and “Na” may be used as a subscript.

  On the other hand, the first counting unit 17a increments the second number by one when there is no pulse whose sampling time is within the period arrival point ± α. Thus, when there is no pulse output within the range of the allowable error α before and after the period arrival point, it means that the pulse is missing in a time zone that matches the period of the pulse set. From this point of view, the second number of times may be described as “missing number of times” and “No” may be used as a subscript.

  After that, the first counting unit 17a repeatedly executes the counting of the number of coincidences Na or the number of missing times No until the counting of the number of coincidences Na or the number of missing times No is finished for all the arrival points of the period. To do.

  The second counting unit 17b is a processing unit that counts, for each pulse set, the third number of times that a pulse output exists outside the vicinity of the time corresponding to an integer multiple of the period of the pulse set. As an aspect, the second counting unit 17b has a pulse output in a time zone outside the range of the allowable error α before and after the sampling time calculated by the first counting unit 17a. Count the number of times. Thus, if there is a pulse output outside the range of the allowable error α before and after the arrival point of the cycle, it means that the pulse is output in excess at a timing that does not match the cycle of the pulse set. . From this point of view, the third number of times is sometimes referred to as “remainder number” and “Nr” is sometimes used as a subscript.

  The set extraction unit 18 is a processing unit that extracts a pulse set having the highest periodicity from the pulse sets selected by the set selection unit 16c. As one aspect, the set extraction unit 18 calculates the cycle score N of the pulse set using the following equation (10) every time the number of coincidences, the number of missing times, and the number of surpluses of the pulse sets are tabulated.

  N = Na-No-Nr (10)

  Here, as shown in the above equation (10), the calculation formula of the cycle score N is calculated by subtracting the pulse group missing number No and the pulse group remainder number Nr from the pulse group coincidence number Na. For this reason, the higher the number of coincidences Na of the pulse set, the higher the cycle score N, the higher the number of missing pulse sets No, the lower the cycle score N, and the higher the pulse set remainder number Nr, the lower the cycle score N is calculated. It will be. Therefore, the higher the value of the cycle score N, the higher the frequency at which pulses are measured at a timing that matches the cycle of the pulse set, and the lower the frequency at which noise is measured at timings other than the cycle of the pulse set. A certain chance increases.

  After calculating the above-described cycle score N, the set extraction unit 18 determines whether or not the cycle score N of the pulse set is higher than the cycle score Nmax stored in the internal memory. When the cycle score N is higher than the cycle score Nmax, the set extraction unit 18 sets the cycle score N of the pulse set as the cycle score Nmax, sets the cycle of the pulse set as the cycle Top, and overwrites and updates the internal memory. To do. In this way, every time the cycle score N of the pulse set is calculated, it is compared with the cycle score Nmax stored in the internal memory, and the cycle score and cycle having the higher value are left in the internal memory. As a result, at the stage where the calculation of the cycle score N of all the pulse sets is completed, the highest cycle score N remains in the internal memory. Thereafter, the set extraction unit 18 outputs the period score Nmax and the period Top of the pulse set stored in the internal memory to the heart rate measurement unit 19.

  The heartbeat measuring unit 19 is a processing unit that measures the heartbeat period from the period of the pulse set. As one aspect, the heart rate measurement unit 19 calculates the heart rate of the subject using the period Top of the pulse set stored in the internal memory after the calculation of the period scores N of all the pulse sets is completed. For example, the heart rate measuring unit 19 can calculate the heart rate per second and the heart rate per minute by converting the period Top of the pulse set into an arbitrary frequency unit, for example, Hz or bpm.

  The heart rate obtained in this manner can be output to an arbitrary output destination including a display device (not shown) included in the heart rate measuring apparatus 10. For example, when a diagnostic program for diagnosing the presence or absence of a heart disease from the heart rate is installed in the heart rate measuring device 10, the diagnostic program can be the output destination. For example, in a diagnostic program, when a person with high blood pressure has tachycardia, for example, 100 bpm or more, it is diagnosed that angina or myocardial infarction is suspected, or arrhythmia or mental illness such as tension or stress is detected using the heart rate. Can be diagnosed. In addition, a server device that provides a diagnostic program as a Web service can be used as an output destination. Furthermore, a terminal device used by a person concerned of the user who uses the heart rate measuring device 10, for example, a caregiver, a nurse, a doctor, or the like, can be used as the output destination. This also enables monitoring services outside the hospital, for example, at home or at home. Needless to say, the measurement result and the diagnosis result of the diagnostic program can be displayed on the terminal devices of the persons concerned including the heart rate measuring device 10.

  The acquisition unit 13, the region extraction unit 14, the waveform detection unit 15, the pulse output unit 16a, the pulse train extraction unit 16b, the set selection unit 16c, the first counting unit 17a, the second counting unit 17b, and the set extraction unit. 18 and the heart rate measuring unit 19 can be realized by causing a central processing unit (CPU), a micro processing unit (MPU), or the like to execute a heart rate measurement program. Each functional unit described above can also be realized by a hard wired logic such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  For example, a semiconductor memory element can be adopted for the internal memory. For example, examples of the semiconductor memory device include a video random access memory (VRAM), a random access memory (RAM), a read only memory (ROM), and a flash memory. Further, a storage device such as a hard disk or an optical disk may be employed instead of the internal memory.

[Specific Example 1]
Subsequently, a specific example of the pulse set extraction method according to the present embodiment will be described with reference to FIGS. 7 to 9 are diagrams illustrating an example of a pulse set. 7 to 9, the pulse graph is shown in the upper graph, the pulse train is shown in the middle graph, and the pulse set is shown in the lower graph. In these FIGS. 7-9, the case where the pulse groups 1-3 are selected from the pulse train of the same pulse wave signal is assumed. In the pulse group graphs shown in FIGS. 7 to 9, pulses belonging to the pulse group are shown by bold lines, coincidence pulses are shown by thin lines, missing pulses are shown by broken lines, and surplus pulses are shown by dotted lines. Is shown. In addition, a circle is shown when a coincidence pulse is counted at each arrival point, a cross is shown when a missing pulse is counted, and a triangle is shown when a remainder pulse is counted. Yes.

  For example, in the case of the pulse set 1, as shown in FIG. 7, the pulse (A) is set as the pulse P1 and the pulse (B) is set as the pulse P2, and the period arrival point is obtained. That is, when n is 0, the sampling time tp1 of the pulse P1 becomes the period arrival point. In this case, the pulse P1 itself of the pulse set whose sampling time is tp1 is included in the range of the periodic arrival point tp1 ± allowable error α. Therefore, at the period arrival point tp1, the pulse P1 of the pulse set, that is, the pulse (A) becomes a coincidence pulse, so that the coincidence number Na is incremented by 1 and becomes 1.

  Subsequently, when n is 1, the sampling time tp2 of the pulse P2 becomes the period arrival point. In this case, the pulse P2 itself of the pulse set whose sampling time is tp2 is included in the range of the period arrival point tp2 ± allowable error α. Therefore, at the period arrival point tp2, the pulse P2 of the pulse set, that is, the pulse (b), becomes a coincidence pulse, so that the coincidence number Na is further incremented by 1 and becomes 2 times.

  Further, when n is 2, the time tp1 + 2T is the period arrival point. In this case, a pulse (c) whose sampling time is tp1 + 2T is included in the range of the period arrival point tp1 + 2T ± allowable error α. Therefore, since the pulse (c) becomes a coincidence pulse at the period arrival point tp1 + 2T, the coincidence number Na is further incremented by 1 to 3 times.

  Next, when n is 3, time tp1 + 3T is the period arrival point. In this case, there is no pulse output within the range of the periodic arrival point tp1 + 3T ± allowable error α. Therefore, since the period arrival point tp1 + 3T is a missing pulse, the number of missing times No is incremented by 1 and becomes 1.

  Thus, as a result of counting the number of coincidences Na and the number of missing times No at each period arrival point until n becomes 10, the number of coincidence times Na is counted as 6 circles and 6 times, and the number of missing times No is X is counted 5 times with 5 x. On the other hand, since there is no pulse having a sampling time outside the range of the allowable error α at each period arrival point, the remainder number Nr is counted as 0 with 0 triangles. Therefore, the period score N of the pulse set is calculated as “1” by calculating 6-5-0 by the above equation (10).

  In the case of the pulse set 2, as shown in FIG. 8, the pulse (A) is set as the pulse P1 and the pulse (C) is set as the pulse P2, and the period arrival point is obtained. That is, when n is 0, the sampling time tp1 of the pulse P1 becomes the period arrival point. In this case, the pulse P1 itself of the pulse set whose sampling time is tp1 is included in the range of the periodic arrival point tp1 ± allowable error α. Therefore, at the period arrival point tp1, the pulse P1 of the pulse set, that is, the pulse (A) becomes a coincidence pulse, so that the coincidence number Na is incremented by 1 and becomes 1. Similarly, when n is 1, since the pulse P2 of the pulse set, that is, the pulse (c) becomes a coincidence pulse at the period arrival point tp2, the coincidence number Na is further incremented by 1 and becomes 2 times.

  Further, when n is 2, the time tp1 + 2T is the period arrival point. In this case, the pulse (d) whose sampling time is tp1 + 2T is included in the range of the period arrival point tp1 + 2T ± allowable error α. Therefore, since the pulse (d) becomes a coincidence pulse at the period arrival point tp1 + 2T, the coincidence number Na is further incremented by 1 to 3 times. Similarly, when n is 3, since the pulse (e) becomes a coincidence pulse at the period arrival point tp1 + 3T, the coincidence number Na is further incremented by 1 to 4 times.

  Next, when n is 4, time tp1 + 4T is the period arrival point. In this case, the pulse output does not exist within the range of the period arrival point tp1 + 4T ± allowable error α. Therefore, since the period arrival point tp1 + 4T becomes a missing pulse, the number of missing times No is incremented by 1 and becomes 1.

  Thus, as a result of counting the number of coincidences Na and the number of missing times No at each period arrival point until n reaches 5, the number of coincidence times Na is counted as 5 circles and 5 times, and the number of missing times No is One cross is counted once. On the other hand, since one pulse (B) having a sampling time exists outside the range of the allowable error α at each period arrival point, the remaining number Nr is counted as 1 with one triangle. Therefore, the period score N of the pulse set is calculated as “3” by calculating 5-1-1 using the above equation (10).

  In the case of the pulse group 3, as shown in FIG. 9, the pulse (b) and the pulse (d) whose time difference is not less than 0.25 seconds and not more than 2 seconds are set as the pulses P1 and P2, and the period arrival point is Desired. That is, when n is 0, the sampling time tp1 of the pulse P1 becomes the period arrival point. In this case, the pulse P1 itself of the pulse set whose sampling time is tp1 is included in the range of the periodic arrival point tp1 ± allowable error α. Therefore, at the period arrival point tp1, the pulse P1, that is, the pulse (b), of the pulse set becomes a coincidence pulse, so that the coincidence number Na is incremented by 1 and becomes 1. Similarly, when n is 1, since the pulse P2 of the pulse set, that is, the pulse (d) becomes a coincidence pulse at the period arrival point tp2, the coincidence number Na is further incremented by 1 and becomes 2 times.

  Subsequently, when n is 2, the time tp1 + 2T is the period arrival point. In this case, there is no pulse output within the range of the period arrival point tp1 + 2T ± allowable error α. Therefore, since the period arrival point tp1 + 2T is a missing pulse, the number of missing times No is incremented by 1 and becomes 1.

  Furthermore, when n is 3, the time tp1 + 3T is the period arrival point. In this case, a pulse (f) whose sampling time is tp1 + 3T is included in the range of the periodic arrival point tp1 + 3T ± allowable error α. Therefore, since the pulse (f) becomes a coincidence pulse at the period arrival point tp1 + 3T, the coincidence number Na is further incremented by 1 to 3 times.

  In this way, as a result of counting the number of coincidences Na and the number of missing times No at each period arrival point until n becomes 3, the number of coincidence times Na is counted as 3 with 3 circles, and the number of missing times No is One cross is counted once. On the other hand, since there are three pulses (A), (C), and (E) having a sampling time outside the range of the allowable error α at each period arrival point, the remainder number Nr is 3 with 3 triangles. Is counted. Therefore, the period score N of the pulse set is calculated as “−1” by calculating 3-1-3 by the above equation (10).

  As described above, in the examples of FIGS. 7 to 9, the cycle score N of the pulse set 2 among the pulse sets 1 to 3 is the highest. As described above, even when a face image including noise having the same frequency component as the frequency component that can be taken by the pulse wave is captured, a cycle in which a pulse derived from the face image, for example, a pulse (B) is selected as a pulse set The score N is lowered. This is because the noise has no periodicity, so the number of coincidences Na is small and the number of missing times No is counted, or the remainder number Nr is counted high by a pulse derived from a heartbeat component with high periodicity. Therefore, by extracting a pulse set having the highest cycle score N, it is possible to extract a pulse set having a periodicity similar to that of a heartbeat. As a result, it is possible to extract a pulse set that is likely to be a heartbeat.

  Here, the above-described counting is performed for the remaining pulse sets by the set selection unit 16c, but the pulse (B) and pulse (C) sets have the same period T as the pulse set 1, so the pulse set 1 The same counting result is obtained. Furthermore, the set of pulse (c) and pulse (d) and the set of pulse (d) and pulse (e) have the same period T as pulse set 2, so Become. Thus, when the period T of the pulse set is substantially the same as the pulse set that has been counted in the past, it is highly probable that the counting result overlaps with the past pulse set, and therefore the counting can be omitted. .

[Specific Example 2]
Subsequently, another specific example of the pulse set extraction method according to the present embodiment will be described with reference to FIGS. 10 and 11. 10 and 11 are diagrams showing another example of the pulse set. 10 and 11, a pulse wave signal is shown in the upper graph, a pulse train is shown in the middle graph, and a pulse set is shown in the lower graph. In these FIG.10 and FIG.11, it is assumed that the pulse group 1 and the pulse group 2 are selected from the pulse train of the same pulse wave signal. In the pulse set graphs shown in FIGS. 10 and 11, pulses belonging to the pulse set are shown by bold lines, coincidence pulses are shown by thin lines, missing pulses are shown by broken lines, and surplus pulses are shown by dotted lines. Is shown. In addition, a circle is shown when a coincidence pulse is counted at each arrival point, a cross is shown when a missing pulse is counted, and a triangle is shown when a remainder pulse is counted. Yes.

  Here, as shown in FIG. 10 and FIG. 11, not all the pulse trains obtained from the pulse waveform are necessarily used for measuring the heartbeat. That is, the pulse whose sampling time is included in the operation period is removed by the pulse train extraction unit 16b. For example, as shown in FIG. 10 and FIG. 11, the pulse (d) to pulse whose sampling period is included in the operation period among the ten pulses (i) to (nu) output by the pulse output unit 16 a. The four (g) are excluded from the measurement of the heart rate.

  Under such circumstances, when calculating the period score N of the pulse set 1, as shown in FIG. 10, the pulse (A) and the pulse (B) are set as the pulse P1 and the pulse P2, and the period arrival point is obtained. It is done. That is, when n is 0, the sampling time tp1 of the pulse P1 becomes the period arrival point. In this case, the pulse P1 itself of the pulse set whose sampling time is tp1 is included in the range of the periodic arrival point tp1 ± allowable error α. Therefore, at the period arrival point tp1, the pulse P1 of the pulse set, that is, the pulse (A) becomes a coincidence pulse, so that the coincidence number Na is incremented by 1 and becomes 1. Similarly, when n is 1, since the pulse P2 of the pulse set, that is, the pulse (B), becomes a coincidence pulse at the period arrival point tp2, the coincidence number Na is further incremented by 1 and becomes 2 times.

  Further, when n is 2, the time tp1 + 2T is the period arrival point. In this case, a pulse (c) whose sampling time is tp1 + 2T is included in the range of the period arrival point tp1 + 2T ± allowable error α. Therefore, since the pulse (c) becomes a coincidence pulse at the period arrival point tp1 + 2T, the coincidence number Na is further incremented by 1 to 3 times.

  Thereafter, the period arrival points where n is from 3 to 6 are included in the operation period, and thus measurement is not performed. Then, when n becomes 7 and the time tp1 + 7T is calculated as the period arrival point, the operation period is canceled. In this case, there is no pulse output within the range of the period arrival point tp1 + 7T ± allowable error α. Therefore, since the period arrival point tp1 + 7T is a missing pulse, the number of missing times No is incremented by 1 and becomes 1.

  Subsequently, when n is 8, time tp1 + 8T is the period arrival point. In this case, a pulse (h) whose sampling time is tp1 + 8T is included in the range of the periodic arrival point tp1 + 8T ± allowable error α. Therefore, since the pulse (h) becomes a coincidence pulse at the period arrival point tp1 + 8T, the coincidence number Na is further incremented by 1 to 4 times.

  Next, when n is 9, time tp1 + 9T is the period arrival point. In this case, there is no pulse output within the range of the periodic arrival point tp1 + 9T ± allowable error α. Therefore, since the period arrival point tp1 + 9T becomes a missing pulse, the number of missing times No is incremented by 1 and becomes 2 times.

  In this way, as a result of counting the number of coincidences Na and the number of missing times No at each period arrival point excluding the period arrival points where n is 3 to 6 among the period arrival points until n becomes 0 to 12, the coincidence results The number of times Na is counted as 6 times with 6 circles, and the number of missing times No is counted as 3 times with 3 crosses. On the other hand, since there is no pulse having a sampling time outside the range of the allowable error α at each period arrival point, the remainder number Nr is counted as 0 with 0 triangles. Therefore, the period score N of the pulse set is calculated as “3” by calculating 6-3-0 using the above equation (10).

  When calculating the period score N of the pulse set 2, as shown in FIG. 11, the period (A) and the pulse (C) are set as the pulse P1 and the pulse P2, and the period arrival point is obtained. That is, when n is 0, the sampling time tp1 of the pulse P1 becomes the period arrival point. In this case, the pulse P1 itself of the pulse set whose sampling time is tp1 is included in the range of the periodic arrival point tp1 ± allowable error α. Therefore, at the period arrival point tp1, the pulse P1 of the pulse set, that is, the pulse (A) becomes a coincidence pulse, so that the coincidence number Na is incremented by 1 and becomes 1. Similarly, when n is 1, since the pulse P2 of the pulse set, that is, the pulse (c) becomes a coincidence pulse at the period arrival point tp2, the coincidence number Na is further incremented by 1 and becomes 2 times.

  Thereafter, the period arrival points where n is from 2 to 3 are included in the operation period, and thus measurement is not performed. Then, when n becomes 4 and the time tp1 + 4T is calculated as the period arrival point, the operation period is canceled. In this case, a pulse (h) whose sampling time is tp1 + 4T is included in the range of the periodic arrival point tp1 + 4T ± allowable error α. Therefore, at the period arrival point tp1 + 4T, the pulse (h) becomes a coincidence pulse, so that the coincidence number Na is further incremented by 1 to 3 times. Similarly, when n is 5, since the pulse (re) becomes a coincidence pulse at the period arrival point tp1 + 5T, the coincidence number Na is further incremented by 1 to 4 times. Similarly, when n is 6, the pulse (nu) becomes a coincidence pulse at the period arrival point tp1 + 6T, and therefore the coincidence number Na is further incremented by 1 to 5 times.

  In this way, as a result of counting the number of coincidences Na and the number of omissions No at each period arrival point excluding the period arrival points where n is 2 to 3 among the period arrival points until n becomes 0 to 6, the coincidence results The number of times Na is counted as 5 for 5 circles, and the number of missing times No is counted as 0 for 0. On the other hand, since one pulse (B) having a sampling time exists outside the range of the allowable error α at each period arrival point, the remaining number Nr is counted as 1 with one triangle. Therefore, the period score N of the pulse set is calculated as “4” by calculating 5-0-1 by the above equation (10).

  As described above, even if the interval for counting the number of coincidences, the number of omissions, and the number of remainders of pulse sets becomes discontinuous by extracting the pulse train excluding the operation period, the pulses derived from the heartbeat are periodically Since it is detected, the cycle score N can be calculated as in the case of the specific example 1 described above. In the example of FIGS. 10 and 11, the cycle score N of the pulse set 2 among the pulse set 1 and the pulse set 2 is the highest.

[Process flow]
Next, a processing flow of the heartbeat measuring apparatus according to the present embodiment will be described. Here, after describing (1) waveform detection processing executed by the heartbeat measuring device 10, (2) heartbeat measurement processing will be described.

(1) Waveform Detection Processing FIG. 12 is a flowchart illustrating a procedure of waveform detection processing according to the first embodiment. This waveform detection process is a process that is started every time an image is acquired from the camera 11 and is repeatedly executed until no image is acquired. Note that when an interruption operation is received via an input device (not shown) or the like, the waveform detection process can be stopped.

  As shown in FIG. 12, when an image showing a subject is acquired (step S101), the region extraction unit 14 uses a predetermined facial part, for example, a living body based on the subject's nose from the image acquired in step S101. An image of the area is extracted (step S102).

  After that, the region extracting unit 14 outputs the time series data of the R signal to the BPF 152R and the BPF 156R, and outputs the time series data of the G signal to the BPF 152G and the BPF 156G (Step S103).

  Subsequently, the BPF 152R extracts a signal component of a specific frequency band of the R signal, for example, a frequency band of 0.05 Hz to 0.3 Hz, and the BPF 152G extracts a signal component of the specific frequency band of the G signal (step) S104A).

  Then, the extraction unit 153R extracts the absolute intensity value of the signal component in the specific frequency band of the R signal, and the extraction unit 153G extracts the absolute intensity value of the signal component in the specific frequency band of the G signal (Step S105). .

  After that, the LPF 154R executes a smoothing process for responding to the time change on the time series data of the absolute intensity value of the specific frequency band of the R signal, and the LPF 154G is the time of the absolute intensity value of the specific frequency band of the G signal. A smoothing process for responding to the time change is executed on the series data (step S106).

Subsequently, the calculation unit 155, the absolute value intensity _G'noise of a particular frequency band of the G signal outputted by LPF154G, dividing the absolute value intensity _R'noise of a particular frequency band of the R signal output by LPF154R division It calculates a correction coefficient a by performing the _"G'noise / _R'noise" (step S107).

  In parallel with the processing of step S104A, the BPF 156R extracts a pulse wave frequency band of the R signal, for example, a signal component in a frequency band of 0.5 Hz to 4 Hz, and the BPF 156G is a pulse wave frequency band of the G signal. Are extracted (step S104B).

Thereafter, the multiplier 157 multiplies the signal component in the pulse wave frequency band of the R signal extracted in step S104B by the correction coefficient a calculated in step S107 (step S108). The calculation unit 158 then subtracts the signal component in the pulse wave frequency band of the R signal multiplied by the correction coefficient a in step S108 from the signal component in the pulse wave frequency band of the G signal extracted in step S104B. “G _signal −a * R _signal ” is executed (step S109).

  And the waveform detection part 15 outputs the time series data of the signal after a calculation to the pulse output part 16a as a pulse wave waveform (step S110), and complete | finishes a process.

(2) Heartbeat Measurement Processing FIGS. 13 and 14 are flowcharts illustrating the procedure of the heartbeat measurement processing according to the first embodiment. This heartbeat measurement process is a process that can be repeatedly executed as long as a pulse wave waveform is input from the waveform detector 15.

  As shown in FIG. 13, when the amplitude value of the pulse waveform is output by the waveform detector 15 (step S301), the pulse output unit 16a has a sampling time t one sampling before the sampling time t of the amplitude value. It is determined whether the amplitude value of −1 is a maximum value, that is, a peak (step S302). When the amplitude value at the sampling time t−1 is not a peak (No at Step S302), the process proceeds to Step S305.

  At this time, when the amplitude value of the sampling time t−1 is a peak (Yes in step S302), the pulse output unit 16a outputs a pulse to the pulse train extraction unit 16b (step S303). Subsequently, the pulse train extraction unit 16b stores the sampling time during which the pulse is output in step S303 in an internal memory (not shown) (step S304).

  Then, the pulse train extraction unit 16b excludes the sampling time included in the operation period stored in the internal memory from the sampling time stored in the internal memory, and extracts the remaining sampling time (step S305).

  Subsequently, the pulse train extraction unit 16b determines whether or not the period excluded in step S305, for example, the pulse train collection period obtained by subtracting the sum of the time period and the operation period that is less than the section length is equal to or longer than the predetermined measurement period. (Step S306). If it is not longer than the measurement period (No at Step S306), the process returns to Step S301, and the processes up to Step S305 are repeatedly executed.

  Then, as illustrated in FIG. 14, the set selection unit 16c refers to the sampling time of the pulses included in the pulse train extracted in step S305, and the two pulses P1 whose sampling time difference is within a predetermined range And a set of pulses P2 is selected (step S307).

  Subsequently, the first counting unit 17a calculates the time interval of the pulse set, that is, the period T (= tp2-tp1) by subtracting the sampling time tp1 of the pulse P1 from the sampling time tp2 of the pulse P2 (step S308). ). Then, the first counting unit 17a calculates a period arrival point corresponding to an integer multiple of the period T of the pulse set by the above equation (8) (step S309).

  Thereafter, the first counting unit 17a selects one of the period arrival points (step S310). After that, the first counting unit 17a determines whether or not there is a pulse output within the range of the allowable error α before and after the period arrival point selected in step S310, that is, the sampling time ± It is determined whether or not there is a pulse included within α (step S311).

  At this time, when there is a pulse whose sampling time is included within the cycle arrival point ± α (step S311 Yes), the first counting unit 17a increments the pulse pair coincidence number Na by one (step S312).

  On the other hand, when there is no pulse whose sampling time is within the cycle arrival point ± α (No in step S311), the first counting unit 17a increments the missing number No of pulse sets by one (step S313).

  Thereafter, the first counting unit 17a repeats the processing from step S310 to step S313 until counting of the number of coincidences Na or the number of missing times No is completed for all the arrival points (No in step S314). Run.

  Then, when the above-mentioned coincidence number Na or the above-described missing number No is counted for all arrival points (Yes in step S314), the second counting unit 17b executes the following process. That is, the second counting unit 17b counts the remaining number Nr in which the output of the pulse exists in a time zone outside the allowable error α range around the sampling time calculated in step S309. Step S315).

  Subsequently, the set extraction unit 18 calculates the period score N of the pulse set by substituting the coincidence number Na, the missing number No, and the surplus number Nr of the pulse set into the above equation (10) (step S316).

  Then, the set extraction unit 18 determines whether or not the cycle score N of the pulse set calculated in step S316 is higher than the cycle score Nmax stored in the internal memory (step S317).

  At this time, when the cycle score N is higher than the cycle score Nmax (step S317 Yes), the set extraction unit 18 sets the cycle score N of the pulse set calculated in step S316 as the cycle score Nmax, and the cycle of the pulse set. Is overwritten and updated in the internal memory (step S318). If the cycle score N is equal to or less than the cycle score Nmax (No in step S317), the process proceeds to step S319 without executing the above overwrite update.

  Then, the processing from step S307 to step S318 is repeated until the cycle scores N of all pulse sets are calculated (No in step S319). After that, when the cycle scores N of all the pulse sets are calculated (Yes in step S319), the heart rate measuring unit 19 calculates the heart rate of the subject using the pulse set cycle Top stored in the internal memory (step S319). S320), the process ends.

  In the flowchart shown in FIG. 14, the number of coincidence times Na and the number of missing times No of pulse groups is counted, and then the remainder number Nr is counted. However, the counting order is not limited to this. In other words, the coincidence number Na and the missing number No of the pulse set may not be counted by the branch of step S311 and the remainder number Nr may not be counted after the coincidence number Na and the missing number No. The coincidence number Na, missing number No, and remainder number Nr of these pulse sets can also be counted in parallel.

  Moreover, in the flowchart shown in FIG. 14, the case where the period score N of each period arrival point is calculated in order is illustrated, but the period score N is calculated after calculating the period score N of each period arrival point in parallel. The maximum pulse set may be extracted.

[Effect of Example 1]
As described above, the heartbeat measuring device 10 according to the present embodiment matches pulses with a period of an integer multiple for each pulse set whose time difference is within a predetermined range in the pulse train corresponding to the peak of the pulse wave waveform. The number of missing times is counted, and a pulse set is extracted by comparing the difference between the number of matches and the number of missing times.

  For this reason, even if a face image including noise having the same frequency component as the frequency component that can be taken by the pulse wave is captured, the heart rate measurement device 10 according to the present embodiment includes a pulse set including a pulse derived from the face image. Can be difficult to extract. This is because noise has no periodicity, so the number of matches is small, and the number of omissions can be counted a lot. Therefore, by extracting a pulse set having the maximum difference obtained by subtracting the number of missing times from the number of coincidences, it is possible to extract a pulse set having a periodicity similar to that of a heartbeat. As a result, it is possible to extract a pulse set that is likely to be a heartbeat. . Further, the frequency analysis is not applied to the counting of the number of coincidences and the number of missing times.

  Therefore, according to the heartbeat measuring device 10 according to the present embodiment, it is possible to shorten the heartbeat measurement time.

  Furthermore, the heartbeat measuring device 10 according to the present embodiment counts the number of times a pulse is surplus outside the vicinity of an integer multiple period, and extracts a pulse set having the maximum difference obtained by subtracting the number of missing times and the number of surpluses from the number of matches. To do. For this reason, in the heartbeat measuring device 10 according to the present embodiment, it is possible to more easily extract a pulse set including a pulse derived from noise having a frequency component similar to the frequency component that can be taken by the pulse wave. This is because noise has no periodicity, and in the pulse set of pulses derived from noise, the number of remainders is counted high by pulses derived from heartbeat components having high periodicity. Therefore, according to the heartbeat measuring apparatus 10 according to the present embodiment, it is possible to accurately extract a pulse set that seems to be a heartbeat.

  Although the embodiments related to the disclosed apparatus have been described above, the present invention may be implemented in various different forms other than the above-described embodiments. Therefore, another embodiment included in the present invention will be described below.

[Pulse output]
In the first embodiment, the case where the pulse corresponding to the peak of the pulse wave waveform is output has been exemplified. However, the pulse corresponding to the peak of the differential waveform of the pulse wave waveform may be output. For example, the differential waveform of the facial pulse wave may be calculated by time-differentiating the pulse waveform detected by the waveform detection unit 15 and the maximum point indicating the peak of the differential waveform may be detected. That is, every time the amplitude value of the pulse wave waveform is output, the differential waveform is determined by determining the difference from the amplitude value output one time before. Then, it is determined whether or not the amplitude value of the differential waveform at the sampling time t-1 one sampling before the sampling time t of the amplitude value is a maximum value. At this time, the pulse output unit 16a may output a pulse to the pulse train extraction unit 16b when the amplitude value of the differential waveform at the sampling time t-1 is the maximum point, that is, the peak of the differential waveform. This means that a pulse is output at the steepest position of the rise of the pulse wave waveform.

[Pulse train extraction]
In the first embodiment described above, the pulse train is extracted by excluding the pulses output during the operation period. However, the pulse that has an adverse effect on the heartbeat measurement, that is, the face in which the motion component other than the pulse wave appears. Pulses detected from the image can be removed by other methods.

  For example, the heartbeat measuring device 10 changes the amplitude change when the amplitude value of the pulse waveform output by the waveform detector 15 changes by a predetermined value, for example, five times or more than the amplitude value output so far. It is also possible to extract a pulse train by excluding pulses output during a period with a large. As a result, it is possible to suppress a situation in which a pulse derived from a face image including body movement and background change is used for measuring a heartbeat while minimizing software modification without adding extra hardware.

  When the heart rate measuring device 10 is equipped with an acceleration sensor or an angular velocity sensor, the sensor value collected by the acceleration sensor or the angular velocity sensor is monitored. The heartbeat measuring device 10 can also extract a pulse train by excluding pulses output during a period when the sensor value collected by the acceleration sensor or the angular velocity sensor is equal to or greater than a predetermined value. As a result, the vibration of the heartbeat measuring device 10 itself and the vibration of the subject holding the heartbeat measuring device 10 can be accurately detected. As a result, the pulse train is extracted by focusing on the time zone in which the face image is captured in a good measurement environment. it can.

[input signal]
In the first embodiment and the second embodiment, the case where two types of R signal and G signal are used as input signals is illustrated. However, any type of signal and any number of signals having different light wavelength components may be used. A number of signals can be input signals. For example, two signals of any combination among signals having different optical wavelength components such as R, G, B, IR, and NIR can be used, or three or more signals can be used.

[Other implementation examples]
In the first embodiment, the case where the heartbeat measuring device 10 executes the above heartbeat measurement processing in a stand-alone manner is illustrated, but it may be implemented as a client server system. For example, the heart rate measurement device 10 may be implemented as a Web server that provides a heart rate measurement service, or may be implemented as a cloud that provides a heart rate measurement service by outsourcing. Thus, when the heartbeat measuring device 10 operates as a server device, a mobile terminal device such as a smartphone or a mobile phone, or an information processing device such as a personal computer can be accommodated as a client terminal. When an image showing the face of the subject is acquired from these client terminals via the network, waveform detection processing and heartbeat measurement processing are executed, and the heartbeat detection results and the diagnosis results made using the detection results are sent to the client terminals By responding, a heart rate measurement service can be provided.

[Distribution and integration]
In addition, each component of each illustrated apparatus does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. For example, the acquisition unit 13, the region extraction unit 14, the waveform detection unit 15, the pulse output unit 16a, the pulse train extraction unit 16b, the set selection unit 16c, the first counting unit 17a, the second counting unit 17b, the set extraction unit 18 or The heart rate measuring unit 19 may be connected as an external device of the heart rate measuring apparatus 10 via a network. Also, the acquisition unit 13, the region extraction unit 14, the waveform detection unit 15, the pulse output unit 16a, the pulse train extraction unit 16b, the set selection unit 16c, the first counting unit 17a, the second counting unit 17b, the set extraction unit 18 or Another device may have the heart rate measuring unit 19 and may be connected to a network to cooperate to realize the function of the heart rate measuring device 10 described above.

[Heart rate measurement program]
The various processes described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer or a workstation. Therefore, in the following, an example of a computer that executes a heart rate measurement program having the same function as that of the above embodiment will be described with reference to FIG.

  FIG. 15 is a schematic diagram illustrating an example of a computer that executes a heart rate measurement program according to the first and second embodiments. As illustrated in FIG. 15, the computer 100 includes an operation unit 110 a, a speaker 110 b, a camera 110 c, a display 120, and a communication unit 130. Further, the computer 100 includes a CPU 150, a ROM 160, an HDD 170, and a RAM 180. These units 110 to 180 are connected via a bus 140.

  As shown in FIG. 15, the HDD 170 includes an acquisition unit 13, a region extraction unit 14, a waveform detection unit 15, a pulse output unit 16 a, a pulse train extraction unit 16 b, a set selection unit 16 c, a first selection unit, and a first selection unit. A heart rate measurement program 170a that exhibits the same functions as those of the counting unit 17a, the second counting unit 17b, the pair extracting unit 18 and the heart rate measuring unit 19 is stored in advance. For the heart rate measurement program 170a, each acquisition unit 13, region extraction unit 14, waveform detection unit 15, pulse output unit 16a, pulse train extraction unit 16b, set selection unit 16c, and first counting unit 17a shown in FIG. Similarly to the constituent elements of the second counting unit 17b, the pair extracting unit 18 and the heart rate measuring unit 19, they may be appropriately integrated or separated. In other words, all data stored in the HDD 170 need not always be stored in the HDD 170, and only data necessary for processing may be stored in the HDD 170.

  Then, the CPU 150 reads the heart rate measurement program 170 a from the HDD 170 and develops it in the RAM 180. Accordingly, as shown in FIG. 15, the heart rate measurement program 170a functions as a heart rate measurement process 180a. The heart rate measurement process 180a expands various data read from the HDD 170 in an area allocated to itself on the RAM 180 as appropriate, and executes various processes based on the expanded data. The heartbeat measurement process 180a includes the acquisition unit 13, the region extraction unit 14, the waveform detection unit 15, the pulse output unit 16a, the pulse train extraction unit 16b, the set selection unit 16c, the first counting unit 17a, the first count unit illustrated in FIG. 2 includes processing executed by the counting unit 17b, the pair extracting unit 18 and the heart rate measuring unit 19, for example, the processing shown in FIGS. In addition, each processing unit virtually realized on the CPU 150 does not always require that all processing units operate on the CPU 150, and only a processing unit necessary for the processing needs to be virtually realized.

  Note that the above heart rate measurement program 170a is not necessarily stored in the HDD 170 or the ROM 160 from the beginning. For example, each program is stored in a “portable physical medium” such as a flexible disk inserted into the computer 100, so-called FD, CD-ROM, DVD disk, magneto-optical disk, or IC card. Then, the computer 100 may acquire and execute each program from these portable physical media. In addition, each program is stored in another computer or server device connected to the computer 100 via a public line, the Internet, a LAN, a WAN, etc., and the computer 100 acquires and executes each program from these. It may be.

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

(Supplementary note 1)
Obtain an image of the living body,
Extracting a biological region included in the image;
A waveform is detected from a representative value signal for each wavelength component of each pixel included in the biological region,
For each peak obtained from the waveform, output a pulse corresponding to the peak,
Selecting a pulse set whose time difference is within a predetermined range from the pulse train of the series of pulses,
For each pulse set, the first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the output of the pulse at or near the time point. Count the second number that does not exist,
Extracting a pulse set by comparing the difference between the first number of times and the second number of times between each pulse set;
A heart rate measurement program for executing a process of measuring a heart rate cycle from the extracted pulse set cycle.

(Supplementary note 2)
For each pulse set, further executing a process of counting a third number of times that the output of the pulse is present at a time corresponding to an integer multiple of the period of the pulse set or in the vicinity of the time,
As a process of extracting the pulse set,
The heart rate measurement program according to appendix 1, wherein a pulse set having a maximum difference between the first number, the second number, and the third number is extracted.

(Supplementary note 3)
The heart rate measurement program according to appendix 1 or 2, further executing a process of extracting the pulse train by excluding pulses that were output during a period in which an operation to the operation unit of the computer is received.

(Supplementary note 4)
The processing for extracting the pulse train by excluding pulses that were output during a period in which the representative value for each wavelength component of each pixel included in the living body region changes by a predetermined value or more is further executed. The heart rate measurement program according to 1 or 2.

(Supplementary note 5)
3. The heart rate measurement according to appendix 1 or 2, wherein a process of extracting the pulse train by excluding pulses output by a sensor included in the computer during a period in which acceleration or angular velocity is equal to or greater than a predetermined value is further executed. program.

(Appendix 6) As a process of outputting the pulse,
The heart rate measurement program according to any one of appendices 1 to 5, wherein a pulse corresponding to the peak is output for each peak obtained from the differential waveform of the waveform.

(Supplementary note 7) As processing for detecting the waveform,
For each wavelength component, extract a component in a specific frequency band other than the frequency band in which a pulse wave can be taken between the wavelength components,
Correction by which the signal of the representative value is multiplied when the difference of the signal of the representative value is calculated between the wavelength components by comparing the magnitude of the component of the specific frequency band between the wavelength components Calculating a correction coefficient that is a coefficient and is minimized after the component of the specific frequency band is calculated;
Multiplying at least one of the signals of representative values of each wavelength component by the correction coefficient,
Appendices 1-6, wherein a waveform of a signal in which the components of the specific frequency band are offset from each other is detected by calculating a difference in signal of the representative value between the respective wavelength components after multiplication of the correction coefficient. The heart rate measurement program according to any one of the above.

(Appendix 8) The computer
Obtain an image of the living body,
Extracting a biological region included in the image;
A waveform is detected from a representative value signal for each wavelength component of each pixel included in the biological region,
For each peak obtained from the waveform, output a pulse corresponding to the peak,
Selecting a pulse set whose time difference is within a predetermined range from the pulse train of the series of pulses,
For each pulse set, the first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the output of the pulse at or near the time point. Count the second number that does not exist,
Extracting a pulse set by comparing the difference between the first number of times and the second number of times between each pulse set;
A heartbeat measuring method, comprising: executing a process of measuring a heartbeat period from a cycle of an extracted pulse set.

(Additional remark 9) The acquisition part which acquires the image by which the biological body was image | photographed,
A region extraction unit for extracting a biological region included in the image;
A waveform detector that detects a waveform from a signal of a representative value for each wavelength component of each pixel included in the biological region;
A pulse output unit that outputs a pulse corresponding to the peak for each peak obtained from the waveform;
A set selection unit that selects a pulse set having a time difference within a predetermined range from a pulse train in which the pulses are connected; and
For each pulse set, the first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the output of the pulse at or near the time point. A counter for counting a second number that does not exist;
A set extraction unit that extracts a pulse set by comparing a difference between the first number of times and the second number of times between each pulse set;
A heartbeat measuring device comprising: a heartbeat measuring unit that measures a heartbeat period from the extracted period of a pulse set.

DESCRIPTION OF SYMBOLS 10 Heartbeat measuring device 11 Camera 12 Touch panel 13 Acquisition part 14 Area extraction part 15 Waveform detection part 16a Pulse output part 16b Pulse train extraction part 16c Pair selection part 17a First counting part 17b Second counting part 18 Pair extraction part 19 Heart rate measurement Part

Claims (8)

On the computer,
Obtain an image of the living body,
Extracting a biological region included in the image;
A waveform is detected from a representative value signal for each wavelength component of each pixel included in the biological region,
For each peak obtained from the waveform, output a pulse corresponding to the peak,
Selecting a pulse set whose time difference is within a predetermined range from the pulse train of the series of pulses,
For each pulse set, the first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the output of the pulse at or near the time point. Count the second number that does not exist,
Extracting a pulse set by comparing the difference between the first number of times and the second number of times between each pulse set;
A heart rate measurement program for executing a process of measuring a heart rate cycle from the extracted pulse set cycle. In the computer,
For each pulse set, further executing a process of counting a third number of times that the output of the pulse is present at a time corresponding to an integer multiple of the period of the pulse set or in the vicinity of the time,
As a process of extracting the pulse set,
The heart rate measurement program according to claim 1, wherein a pulse set having a maximum difference between the first number, the second number, and the third number is extracted. In the computer,
3. The heart rate measurement program according to claim 1, further comprising executing a process of extracting the pulse train by excluding a pulse that was output during a period in which an operation on the operation unit of the computer is received. In the computer,
The process for extracting the pulse train by excluding pulses that were output during a period in which the representative value for each wavelength component of each pixel included in the living body region changes by a predetermined value or more is further executed. Item 3. A heart rate measurement program according to item 1 or 2. In the computer,
3. The heartbeat according to claim 1, further comprising executing a process of extracting the pulse train by excluding pulses that were output during a period in which acceleration or angular velocity is equal to or greater than a predetermined value by a sensor included in the computer. Measurement program. As a process of detecting the waveform,
For each wavelength component, extract a component in a specific frequency band other than the frequency band in which a pulse wave can be taken between the wavelength components,
Correction by which the signal of the representative value is multiplied when the difference of the signal of the representative value is calculated between the wavelength components by comparing the magnitude of the component of the specific frequency band between the wavelength components Calculating a correction coefficient that is a coefficient and is minimized after the component of the specific frequency band is calculated;
Multiplying at least one of the signals of representative values of each wavelength component by the correction coefficient,
The signal waveform in which the components of the specific frequency band are canceled each other is detected by calculating a difference in the signal of the representative value between the respective wavelength components after multiplication of the correction coefficient. The heart rate measurement program according to any one of 5. Computer
Obtain an image of the living body,
Extracting a biological region included in the image;
A waveform is detected from a representative value signal for each wavelength component of each pixel included in the biological region,
For each peak obtained from the waveform, output a pulse corresponding to the peak,
Selecting a pulse set whose time difference is within a predetermined range from the pulse train of the series of pulses,
For each pulse set, the first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the output of the pulse at or near the time point. Count the second number that does not exist,
Extracting a pulse set by comparing the difference between the first number of times and the second number of times between each pulse set;
A heartbeat measuring method, comprising: executing a process of measuring a heartbeat period from a cycle of an extracted pulse set. An acquisition unit for acquiring an image of a living body photographed;
A region extraction unit for extracting a biological region included in the image;
A waveform detector that detects a waveform from a signal of a representative value for each wavelength component of each pixel included in the biological region;
A pulse output unit that outputs a pulse corresponding to the peak for each peak obtained from the waveform;
A set selection unit that selects a pulse set having a time difference within a predetermined range from a pulse train in which the pulses are connected; and
For each pulse set, the first number of times that the output of the pulse exists at or near the time point corresponding to an integral multiple of the period of the pulse set, and the output of the pulse at or near the time point. A counter for counting a second number that does not exist;
A set extraction unit that extracts a pulse set by comparing a difference between the first number of times and the second number of times between each pulse set;
A heartbeat measuring device comprising: a heartbeat measuring unit that measures a heartbeat period from the extracted period of a pulse set.

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