Pulse wave detection system, pulse wave detection method and program

Abstract

To perform highly accurate detection of a pulse wave in a non-contact manner with a simple and inexpensive configuration.SOLUTION: A pulse wave detection system comprises: a first color space conversion unit 101 which acquires image data obtained by imaging a face of an object person and converts the acquired image data into image data of an HSV color space formed of components of a color tone, saturation and brightness; a second color space conversion unit 102 which converts the image data of the HSV color space converted by the first color space conversion unit 101 into a color space of original colors; an independent component analysis unit 103 which independently analyzes a component of each original color from the image data of the color space of the original color converted by the second color space conversion unit 102; a Fourier transformation unit 104 which performs frequency analysis on the result analyzed by the independent component analysis unit; and a signal selection and averaging unit 105 which acquires and averages a component equivalent to a pulse wave from the result obtained with the Fourier transformation by the Fourier transformation unit 104.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a pulse wave detection system, a pulse wave detection method, and a program that can detect the pulse wave of a subject in a non-contact manner and determine the condition of the subject. [Background technology]

[0002] In recent years, with various changes in the environment, such as the COVID-19 pandemic, it has become increasingly important to assess the stress of individuals (employees) while they are at work, etc., and to understand their condition. There are various methods for stress assessment, but one method is to detect pulse waves linked to the heartbeat.

[0003] Pulse waves can be measured, for example, using a sensor in a smartwatch worn on the subject's wrist. A method for evaluating stress using the measured pulse waves is to determine whether the subject is in a stressed or relaxed state from the power spectral density of the pulse wave peak interval, for example. Another method for assessing stress using pulse waves is to use the amplitude of the pulse wave. When the amplitude of the pulse wave increases, it can be evaluated as a state of stress, and when the amplitude of the pulse wave decreases, it can be evaluated as a state of relaxation.

[0004] Patent Document 1 describes a technique for photographing a subject, acquiring biological information such as pulse rate from the photographed image, and determining the subject's condition such as stress level. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2021-132726 Summary of the Invention [Problem to be solved by the invention]

[0006] As described in Patent Document 1, it has been proposed to perform stress assessment from pulse waves in a non-contact manner without wearing a device such as a smartwatch. However, there has been a problem in that stress assessment is not necessarily accurate because pulse waves detected from an image are not highly accurate. Furthermore, various studies have been conducted to improve the accuracy of pulse wave detection, but detecting pulse waves with high accuracy requires dedicated, expensive detection equipment with a high sampling frequency. Therefore, it has been desired to be able to detect pulse waves with high accuracy using general equipment with a lower sampling frequency.

[0007] An object of the present invention is to provide a pulse wave detection system, a pulse wave detection method, and a program that have a simple and inexpensive configuration and are capable of detecting pulse waves in a non-contact manner with high accuracy. [Means for solving the problem]

[0008] The pulse wave detection system of the present invention includes an image input unit that acquires image data of a subject's face, a first color space conversion unit that converts the image data acquired by the image input unit into image data in an HSV color space consisting of components of hue, saturation, and brightness, and limits the hue during conversion to a hue range corresponding to skin color, a second color space conversion unit that converts the image data in the HSV color space converted by the first color space conversion unit into a color space of primary colors, an independent component analysis unit that independently analyzes each primary color component from the image data in the color space of primary colors converted by the second color space conversion unit, and a Fourier transform unit that performs frequency analysis on the results of the analysis by the independent component analysis unit. For each of the multiple sections included in a certain time period, The device is equipped with a signal selection and averaging unit that selects, from the results of the Fourier transform performed by the Fourier transform unit, amplitude values ​​that include a first peak in a frequency band that includes a component corresponding to the pulse wave, and averages the selected amplitude values ​​to obtain a detection result of the amplitude value of the subject's pulse wave.

[0009] Furthermore, the pulse wave detection method of the present invention is a pulse wave detection method in which an information processing device performs arithmetic processing based on image data of an image of the face of a subject, thereby detecting the pulse wave of the subject. The arithmetic processing performed by the information processing device includes an image acquisition process for acquiring image data of the image of the face of the subject, a first color space conversion process for converting the image data obtained by the image acquisition process into image data in an HSV color space consisting of components of hue, saturation, and luminance, and limiting the hue during conversion to a hue range corresponding to skin color, a second color space conversion process for converting the image data in the HSV color space converted by the first color space conversion process into a color space of primary colors, an independent component analysis process for independently analyzing each primary color component from the image data in the color space of primary colors converted by the second color space conversion process, and a Fourier transform process for frequency analyzing the results of the analysis by the independent component analysis process. For each of the multiple sections included in a certain time period, The method includes a signal selection and averaging process for selecting, from the results of the Fourier transform performed by the Fourier transform process, amplitude values ​​resulting in a first peak in a frequency band containing a component corresponding to the pulse wave, and averaging the selected amplitude values ​​to obtain a detection result of the amplitude value of the subject's pulse wave. The program of the present invention causes a computer to execute the steps of the pulse wave detection method described above. [Effects of the Invention]

[0010] According to the present invention, it is possible to accurately detect pulse waves from images of a face. Therefore, for example, highly accurate stress evaluation is possible based on the detected pulse waves. In particular, it is possible to accurately detect pulse waves from images taken using an inexpensive camera such as a general web camera as the camera for acquiring image data, making it possible to perform highly accurate stress evaluation with an inexpensive configuration. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a configuration diagram showing an example of a pulse wave detection system according to an embodiment of the present invention; [Figure 2] 10A and 10B are diagrams illustrating an example of a detection state according to an embodiment of the present invention. [Figure 3]1 is a block diagram showing an example of a hardware configuration when a pulse wave detection device according to an embodiment of the present invention is configured using a computer. [Figure 4] 4 is a flowchart showing the flow of pulse wave detection processing according to an embodiment of the present invention. [Figure 5] 10A and 10B are diagrams illustrating how the peaks of a waveform for detecting a pulse wave are crushed depending on the sampling rate. [Figure 6] FIG. 10 is a diagram showing an example of information contained in each hue band. [Figure 7] 10A and 10B are diagrams showing examples of pulse wave detection processing waveforms according to an embodiment of the present invention. [Figure 8] 10A and 10B are diagrams showing pulse wave detection processing results for each hue detection range according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] An embodiment of the present invention (hereinafter referred to as "this example") will be described below with reference to the accompanying drawings.

[0013] [System Configuration] FIG. 1 is a functional block diagram showing the configuration of the processing performed by pulse wave detection system 100 of this example. The pulse wave detection system 100 of this example captures image data captured by a camera 1. The image data is an image of the face of a subject whose pulse wave is to be detected. The image data captured by the camera 1 is video data at a constant frame rate, and the image data for each frame is composed of pixel data for the primary colors red (R), green (G), and blue (B). The frame rate of the image data is, for example, 30 frames per second, and the pixels in each frame indicate the brightness value of red (R), green (G), or blue (B) in a predetermined gradation.

[0014] Image data captured by pulse wave detection system 100 is supplied to first color space conversion unit 101. First color space conversion unit 101 converts the primary color image data into image data in the HSV color space, which consists of three components: hue, saturation (chroma), and value (brightness). Value in the HSV color space is also called luminance. Here, first color space conversion unit 101 performs processing to extract a specific hue band by applying restrictions to the hue. A specific example of processing to extract a specific hue band will be described later.

[0015] The image data in the HSV color space converted by the first color space conversion unit 101 is supplied to the second color space conversion unit 102. The second color space conversion unit 102 reconverts the image data in the HSV color space into image data in the primary colors of red (R), green (G), and blue (B). The image data converted by the second color space conversion unit 102 is supplied to the independent component analysis unit 103 for each component. The independent component analysis unit 103 performs an analysis process to obtain the amplitude of the change in each color component for each frame. The independent component analysis unit 103 performs a process to obtain the amplitude value of the minute vibration state corresponding to the heartbeat for areas such as skin included in the facial image.

[0016] That is, the independent component analysis unit 103 performs an analysis process to extract amplitude components containing pulse waves from the changes in each color component for each frame. The amplitude data obtained by analyzing each color component by the independent component analysis unit 103 is supplied to a Fourier transform unit 104 . The Fourier transform unit 104 performs frequency analysis on the amplitude data. The result of the frequency analysis by the Fourier transform unit 104 is supplied to the signal selection and averaging unit 105.

[0017] Based on the results of the frequency analysis, signal selection and averaging unit 105 selects signals for detecting pulse waves and averages the selected signals. The signals selected and averaged by signal selection and averaging unit 105 are output from output unit 106 as pulse wave detection values.

[0018] [Example of pulse wave detection status] FIG. 2 shows an example of a state in which pulse wave detection system 100 of this embodiment detects the pulse wave of a subject. As shown in Fig. 2, a camera 1 attached to a pulse wave detection system 100 configured, for example, by a personal computer, captures an image of the face of a subject operating the personal computer, and detects the pulse wave. The results of the detected pulse wave can be used, for example, to evaluate the stress of the subject. Note that the personal computer serving as pulse wave detection system 100 can be configured such that a program for detecting pulse waves is installed on the computer on which the subject works, allowing the subject to evaluate their own stress while at work.

[0019] [Example of hardware configuration for pulse wave detection system] FIG. 3 shows an example of the hardware configuration of pulse wave detection system 100 when configured using a computer. The computer (pulse wave detection system 100) shown in FIG. 3 includes a CPU (Central Processing Unit) 100a, a main memory unit 100b, a non-volatile storage 100c, a network interface 100d, an image input unit 100e, an output unit 100f, and an operation unit 100g, which are all connected to a bus.

[0020] The CPU 100a is an arithmetic processing unit that reads out and executes program code of software that realizes the functions performed by the pulse wave detection system 100 from the main storage unit 100b or the nonvolatile storage unit 100c. The CPU 100a reads program code from the main memory 100b or the nonvolatile storage 100c and executes arithmetic processing in the work area of ​​the main memory 100b, thereby configuring various processing function units in the main memory 100b. For example, the main memory 100b is configured with a first color space conversion unit 101, a second color space conversion unit 102, an independent component analysis unit 103, a Fourier transform unit 104, and a signal selection and averaging unit 105 shown in FIG. 1.

[0021] The nonvolatile storage 100c may be a large-capacity information storage medium such as a hard disk drive (HDD), a solid state drive (SSD), a memory card, etc. The nonvolatile storage 100c stores software that realizes the functions of the pulse wave detection system 100 and data obtained by executing the program.

[0022] The network interface 100d is, for example, a network interface card (NIC), and transmits and receives data to and from other devices. Image data from the camera 1 is input to the image input unit 100e, and image acquisition processing is performed. A display 2 is connected to the output unit 100f, and data such as images showing the pulse wave detection results and evaluation results such as stress evaluation based on the pulse wave detection are output from the output unit 100f, and these detection results and evaluation results are displayed on the display 2. The detection results and evaluation results may be transmitted to an external device via the network interface 100d. The operation unit 100g accepts operations of devices such as a keyboard and a mouse operated by the operator of this computer.

[0023] [Pulse wave detection process flow] FIG. 4 is a flowchart showing the flow of pulse wave detection processing performed by pulse wave detection system 100 of this embodiment with the configuration shown in FIG. First, image input unit 100e (FIG. 3) of pulse wave detection system 100 captures facial image data of a subject photographed by camera 1 (step S11). Then, the first color space conversion unit 101 performs a first color space conversion process to convert the primary color image data into image data in the HSV color space consisting of three components: hue, saturation (chroma), and value (brightness) (step S12). When converting into image data in the HSV color space, a process is performed in which hue is restricted and a specific hue band is extracted.

[0024] Then, the image data in the HSV color space converted by the first color space conversion unit 101 is reconverted by the second color space conversion unit 102 into image data in the primary colors of red (R), green (G), and blue (B) (second color space conversion process) (step S13). Furthermore, the image data converted by the second color space conversion unit 102 is subjected to a process (independent component analysis process) in which each component is analyzed individually for each color component by the independent component analysis unit 103, and amplitude data is obtained from the changes for each pixel for each frame (step S14).

[0025] The amplitude data obtained in the independence component analysis process of step S14 is then subjected to frequency analysis by Fourier transform by Fourier transform unit 104 (step S15). From the results of the frequency analysis by Fourier transform unit 104, signal selection and averaging is performed by signal selection and averaging unit 105, a pulse wave is detected from the averaged results, and a detected value of the pulse wave is output (step S16). In step S16, a stress evaluation of the subject is also performed based on the state of the detected pulse wave, and the determined stress evaluation result is output.

[0026] [Explanation of each process for pulse wave detection] Figure 5 compares (a) an example of an original waveform simulating a pulse wave, which is a heartbeat component; (b) a waveform obtained by sampling the original waveform using a photoplethysmograph, a conventionally known pulse wave detection device; and (c) a waveform obtained by sampling the original waveform at the sampling rate of video data. In the case of the photoplethysmograph shown in FIG. 5(b), the sampling frequency is, for example, 1000 Hz, and the peak shape of the original waveform shown in FIG. 5(a) is directly captured as data.

[0027] On the other hand, when the movement of a subject is detected from the video data shown in Fig. 5(c), the sampling frequency is 30 Hz, which corresponds to the frame frequency, and the peaks of the original waveform shown in Fig. 5(a) are flattened. That is, the video data handled by pulse wave detection system 100 of this example is video data acquired by a camera such as a web camera normally used in personal computer devices, and is image data with a relatively low frequency such as 30 Hz, and the sampling frequency is also correspondingly low. The movement components shown in Figure 5(c) correspond to the pulse wave components captured by pulse wave detection system 100 of this example, and it is difficult to correctly detect the pulse wave, which is a heartbeat component, without any modification. For this reason, pulse wave detection system 100 of this example is designed to perform proper pulse wave detection by using the configuration shown in Figure 1 and the processing flow shown in Figure 4. Each process in pulse wave detection system 100 of this example will be described in order below.

[0028] The upper part of Fig. 6 shows the hue in the HSV color space converted by first color space conversion unit 101 in the range of 0° to 360°. Note that the graph in the upper part of Fig. 6 is actually a graph showing the change in color from 0° to 360°, but since the drawing is expressed in black and white, the color is not shown. Therefore, in Fig. 6, the positions of the hues corresponding to the three primary colors R, G, and B are indicated by arrows in the drawing. It is known that human skin color falls within the range of 0° to 15° within the hue range from 0° to 360°. However, the skin color here is assumed to be, for example, Caucasian or Mongoloid, and the range of skin color may differ slightly depending on the race.

[0029] Therefore, the first color space conversion unit 101 in this example performs processing to extract human skin color components from the image data converted to the HSV color space as the hue, such as from 0° to 15°. Note that the band for extracting skin color components from the hue is merely an example, and is not limited to this band. In this way, first color space conversion unit 101 converts image data from the camera into HSV color space, extracts components corresponding to human skin color, and supplies the extracted data to second color space conversion unit 102. Second color space conversion unit 102 reconverts the input HSV color space signal into RGB color space, thereby obtaining reconverted RGB color space image data that makes it easier to capture changes in blood flow.

[0030] As shown in the bottom of Figure 6, skin information is included in the hue in the HSV color space. On the other hand, AC light source components are included in the luminance, which can adversely affect pulse wave detection. Therefore, the band corresponding to the AC light source component included in the luminance can be removed. As shown in the lower part of FIG. 6, the red (R) component in the RGB color space contains arterial information, and the green (G) component contains capillary information.

[0031] The left side of Fig. 7 shows the change over time of the waveform obtained by the independent component analysis unit 103. The vertical axis of the graph shown on the left side of Fig. 7 is amplitude, and the horizontal axis is time. The waveform shown on the left side of Fig. 7 represents, in amplitude, minute movements within the image obtained by the independent component analysis unit 103. As already explained, the image data handled by the independent component analysis unit 103 here is extracted components corresponding to the color of human skin, and corresponds to minute movements of the skin part of the face represented by amplitude.

[0032] The amplitude waveform shown on the left side of Fig. 7 is Fourier transformed by the Fourier transform unit 104, and frequency analysis is performed. Here, in a normal Fourier transform, the data is divided into sections d1, d2, d3, ... at regular time intervals as data sections for performing Fourier transform processing (so-called FFT processing), as shown on the left side of Fig. 7, and data for performing FFT processing is obtained in order. In contrast, in the Fourier transform unit 104 of this example, FFT processing is performed by setting partially overlapping intervals D11, D12, D13, ... as shown on the left side of Fig. 7, and frequency analysis result data for each interval is obtained in sequence. Here, the FFT processing intervals D11, D12, D13 are set so that they overlap by 50%, but 50% is just an example.

[0033] Then, the signal selection and averaging unit 105 selects signals from which the heartbeat component has been properly acquired from the results of frequency analysis for each section D11, D12, D13, ..., and performs processing to average the selected frequency analysis for each fixed section. When the signal selection and averaging unit 105 selects a signal from which the heartbeat component has been properly acquired, the frequency analysis result in which the first peak has been detected in the frequency band from 1 Hz to 2 Hz, which is the frequency band that contains the heartbeat component, is selected, and the amplitude value of the first peak of the selected frequency analysis result is obtained.

[0034] That is, as shown in the upper right part of Fig. 7, the signal selection and averaging unit 105 selects, as the frequency analysis result for a certain section, a signal in which a first peak P1 exists within a frequency band Da from 1 Hz to 2 Hz, and obtains the amplitude value (vertical axis) of this first peak P1. In contrast, as shown in the lower right part of Fig. 7, the signal selection and averaging unit 105 does not select, as the frequency analysis result for a certain section, a signal in which a first peak P2 exists at a frequency position outside a frequency band Db from 1 Hz to 2 Hz. Note that in the pulse wave detection process of this example, the amplitude value of the first peak P1 is used, and information on the frequency position of the first peak P2 is not used in detecting the pulse wave.

[0035] The signal selection and averaging unit 105 then collects the amplitude values ​​of the frequency analysis results selected in this manner and performs averaging. For example, as shown on the left side of Fig. 7, the signal selection and averaging unit 105 collects the frequency analysis results selected every certain time A1 and performs averaging. The certain time A1 is set to be sufficiently longer than the time of each of the sections D11, D12, D13, ... In this way, the average of the amplitude values ​​obtained by signal selection and averaging unit 105 is output from output unit 106 and displayed on a display 2 or the like connected to pulse wave detection system 100. The average of the displayed amplitude values ​​is a value that correlates with the pulse wave, and the detected pulse wave is displayed on display 2.

[0036] [Example of correlation between hue restriction band and pulse wave (heart rate)] As already explained, the image data in the HSV color space converted by the first color space conversion unit 101 is subjected to processing to extract the color components of human skin as the hue. In the above explanation, the process for extracting the color components of human skin was performed by extracting the hue range from 0° to 15°. Figure 8 shows an example with a narrower band. The images in the top rows of Figure 8(a), (b), and (c) show the area where the skin color components were extracted from the face, and the table in the bottom row shows the similarity between the detected pulse wave and the actual heart rate. In the top images of Figure 8(a), (b), and (c), the relevant areas to be extracted are those where the face is visible, i.e., the "non-black parts" in the image; black areas are outside the band and are not extracted. A similarity value close to 1, as shown in the bottom table, indicates a high similarity to the heart rate.

[0037] Figure 8(a) shows an example of detecting heartbeats, which correspond to pulse waves, by extracting the 4° to 14° hue band. In this example, as can be seen from the upper image in Figure 8(a), a large area of ​​the face is used for detection. When the 4° to 14° hue band is used, the similarity between all six subjects is high, and it can be seen that pulse waves can be detected well.

[0038] Figure 8(b) shows an example in which the 5° to 9° hue band was extracted to detect the heartbeat, which corresponds to the pulse wave. In this example, as can be seen by comparing the upper image in Figure 8(a) with the upper image in Figure 8(b), the area used is slightly narrower, and the earlobe area is extracted with emphasis. When the 5° to 9° hue band was used, the similarity was slightly reduced for only one subject (subject 4), but the similarity was high for the other five subjects, indicating that the pulse wave could be detected even in this band.

[0039] Figure 8(c) shows an example of detecting heartbeats, which correspond to pulse waves, by extracting a hue band from 2° to 5°. In this example, as can be seen from the upper image in Figure 8(c), detection is performed using a relatively narrow area centered on the cheeks of the face. When a hue band from 5° to 9° is used, the similarity is high for all six subjects, and it can be seen that pulse waves can be detected well. For reference, when pulse waves are detected from an image using the conventional method, the similarity indicating the correlation between the detected pulse wave and the actually measured heart rate is about 0.70, and the average similarity value of 0.87 to 0.88 shown in Figure 8 indicates a much higher correlation than the conventional method.

[0040] In this way, pulse wave detection system 100 of this example can capture and process a facial image of the subject, thereby enabling highly accurate pulse wave detection. In particular, a web camera with a general frame rate that can be connected to a personal computer or the like can be used as the camera for capturing the facial image of the subject, enabling highly accurate pulse wave detection with a simple and inexpensive configuration. Then, it becomes possible to appropriately evaluate the stress of the subject based on the change in the detected pulse wave. For example, when the pulse wave remains high, it can be evaluated as a state of stress. Alternatively, when a state of the pulse wave decreasing is detected, it can be evaluated as a state of less stress. As a specific example of evaluating stress based on the detected pulse wave, various known methods can be applied.

[0041] Although the pulse wave detection system 100 shown in FIG. 1 is an example configured as a system for detecting pulse waves, the pulse wave detection system 100 of this example can also be configured with a computer as shown in FIG. 3 . Therefore, by incorporating a program for operating the pulse wave detection system 100 of this example into an information processing device such as a computer or smartphone, pulse wave detection processing can be performed in parallel with various processes being executed by the computer or smartphone. As a result, it becomes possible to detect the subject's pulse wave and evaluate the stress level by evaluating the pulse wave detection results at any time. The program for operating the pulse wave detection system 100 of this example can be created by executing the processes described in the flowchart of FIG. 4 as procedures. This program can also be stored on various types of memory, IC cards, SD cards, optical discs, and other recording media. [Explanation of symbols]

[0042] 1...camera, 2...display, 4...subject, 100...pulse wave detection system, 100a...CPU, 100b...main memory unit, 100c...non-volatile storage, 100d...network interface, 100e...image input unit, 100f...output unit, 100g...operation unit, 101...first color space conversion unit, 102...second color space conversion unit, 103...independent component analysis unit, 104...Fourier transform unit, 105...averaging unit, 106...output unit

Claims

1. a first color space conversion unit that acquires image data of a subject's face, converts the acquired image data into image data in an HSV color space consisting of components of hue, saturation, and brightness, and limits the hue during conversion to a hue range corresponding to skin color; a second color space conversion unit that converts the image data in the HSV color space converted by the first color space conversion unit into a primary color space; an independent component analysis unit that analyzes components of each primary color independently from the image data in the color space of the primary colors converted by the second color space conversion unit; a Fourier transform unit that performs frequency analysis on the results of the analysis by the independent component analysis unit; a signal selection and averaging unit that selects, from the results of Fourier transform performed by the Fourier transform unit for each of a plurality of sections included in a certain time period, amplitude values ​​resulting in a first peak being included in a frequency band including a component corresponding to a pulse wave, and averages the selected amplitude values ​​to obtain a detection result of the amplitude value of the pulse wave of the subject. Pulse wave detection system.

2. The signal selection and averaging unit detects an amplitude value of the arterial pulse wave of the subject by selecting red color analyzed by the independent component analysis unit. The pulse wave detection system according to claim 1 .

3. The interval of one unit that is frequency analyzed by the Fourier transform unit is an interval that partially overlaps with the preceding and succeeding intervals. The pulse wave detection system according to claim 1 or 2.

4. A pulse wave detection method in which an information processing device performs arithmetic processing based on image data of a face of a subject, and detects an amplitude value of a pulse wave of the subject, The arithmetic processing performed by the information processing device includes: a first color space conversion process for converting image data of the subject's face into image data in an HSV color space consisting of components of hue, saturation, and luminance, and limiting the hue during conversion to a hue band corresponding to skin color; a second color space conversion process for converting the image data in the HSV color space converted by the first color space conversion process into a primary color space; an independent component analysis process for independently analyzing components of each primary color from the image data in the color space of the primary colors converted by the second color space conversion process; a Fourier transform process for frequency-analyzing the results of the analysis performed by the independent component analysis process; and a signal selection and averaging process for obtaining a detection result of the amplitude value of the pulse wave of the subject by selecting, from the results of Fourier transform performed by the Fourier transform process for each of a plurality of sections included in a certain time period, an amplitude value resulting in a first peak being included in a frequency band including a component corresponding to a pulse wave, and averaging the selected amplitude values. Pulse wave detection method.

5. A program for detecting a pulse wave of a subject based on image data of the subject's face, a first color space conversion step of converting image data of the subject's face into image data in an HSV color space consisting of components of hue, saturation, and luminance, and limiting the hue during conversion to a hue range corresponding to skin color; a second color space conversion step of converting the image data in the HSV color space converted by the first color space conversion step into a primary color space; an independent component analysis step of independently analyzing components of each primary color from the image data in the primary color color space converted by the second color space conversion step; a Fourier transform step for frequency-analyzing the results of the analysis performed in the independent component analysis step; a signal selection and averaging step of selecting, from the results of performing Fourier transform on each of a plurality of sections included in a certain time period using the Fourier transform step, amplitude values ​​resulting in a first peak being included in a frequency band including a component corresponding to a pulse wave, and averaging the selected amplitude values ​​to obtain a detection result of the amplitude value of the pulse wave of the subject; A program that is implemented and executed on a computer.

Images