Biological information detection system, program, and biological information detection method

Abstract

To provide a biological information detection system capable of securely detecting accuracy of biological information while detecting the biological information in a non-contact manner.SOLUTION: A biological information detection system 1 comprises: a signal information acquisition unit, a filter setting unit, and a biological information acquisition unit. The signal information acquisition unit acquires a plurality of pieces of signal information. The respective pieces of signal information correspond to reflection waves R from a living body M that differ from each other. Each of the plurality of pieces of signal information has a plurality of peaks in a time region. The filter setting unit sets a frequency filter on the basis of positions in a time axial direction of the plurality of peaks of the plurality of pieces of signal information. The biological information acquisition unit acquires biological information on the living body M by performing filtering processing on information based on at least one of the plurality of pieces of signal information using the frequency filter.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a biological information detection system, a program, and a biological information detection method. [Background technology]

[0002] There is known a technology for detecting biometric information relating to a living body such as a human or animal in a non-contact manner (for example, Patent Document 1). Patent Document 1 describes acquiring signal information relating to the movement of the living body and processing the acquired signal information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-127398 Summary of the Invention [Problem to be solved by the invention]

[0004] When biometric information about a living organism is detected using a contact sensor, it is necessary to expose the skin of the living organism and attach a sensor to the exposed area. "Living organism" includes, for example, humans and animals. When biometric information is detected using an image captured by an imaging device, the biometric information can be detected without contact. However, detection accuracy deteriorates in dark places, and capturing an image poses a problem in terms of protecting privacy.

[0005] To solve the above problems, a technology has been developed that detects biometric information by irradiating a living body with electromagnetic waves as transmitted waves, acquiring signal information based on reflected waves from the living body, and processing the acquired signal information. For example, it has been considered to detect the living body's heartbeat or breathing as biometric information by acquiring Doppler signals from the living body using a Doppler sensor. However, such non-contact biometric information detection is more susceptible to noise than contact-based biometric information detection, making it difficult to ensure biometric information detection accuracy. For example, noise is generated by the propagation of transmitted and reflected waves between the sensor and the living body. This noise or the living body's posture may reduce the accuracy of extracting biometric information from the signal information.

[0006] One aspect of the present invention aims to provide a biometric information detection system that can ensure the accuracy of biometric information detection while detecting biometric information contactlessly. Another aspect of the present invention aims to provide a program that can ensure the accuracy of biometric information detection while detecting biometric information contactlessly. Yet another aspect of the present invention aims to provide a biometric information detection method that can ensure the accuracy of biometric information detection while detecting biometric information contactlessly. [Means for solving the problem]

[0007] A biological information detection system according to one aspect of the present invention includes a signal information acquisition unit, a filter setting unit, and a biological information acquisition unit. The signal information acquisition unit acquires a plurality of pieces of signal information. Each piece of signal information corresponds to a different reflected wave from a living organism. Each piece of signal information has a plurality of peaks in the time domain. The filter setting unit sets a frequency filter based on the positions of the peaks of the plurality of pieces of signal information in the time domain. The biological information acquisition unit acquires biological information of the living organism by performing a filtering process using the frequency filter on information based on at least one piece of signal information.

[0008] In one of the above aspects, multiple pieces of signal information corresponding to different reflected waves are used. Therefore, the diversity effect can improve the accuracy of extracting biometric information that is difficult to capture due to noise generated by the propagation of the reflected waves or the posture of the living body. Furthermore, a frequency filter is set based on the peaks of multiple pieces of signal information corresponding to the multiple reflected waves, so that the frequency band corresponding to the biometric information can be accurately extracted by the frequency filter. As a result, the accuracy of detecting biometric information can be ensured while detecting the biometric information in a non-contact manner.

[0009] In one of the above aspects, the biological information detection system may further include a peak group calculation unit. The peak group calculation unit may set a plurality of peak groups based on positions in the time direction of a plurality of peaks of the plurality of pieces of signal information. Each of the plurality of peak groups may include at least one of the plurality of peaks. The peak group calculation unit may calculate the position in the time direction of each peak group. The filter setting unit may set a frequency filter based on the position in the time direction of each peak group calculated by the peak group calculation unit. In this case, the frequency filter is set based on the position in the time direction of a peak group including at least one of the plurality of peaks. Therefore, the frequency filter can more accurately extract a frequency band corresponding to the biological information.

[0010] In one of the above aspects, the biological information detection system may further include a peak group selection unit and a peak interval calculation unit. The peak group selection unit may select at least two peak groups from the plurality of peak groups as representative peak groups based on the positions of each peak group in the time direction. The peak interval calculation unit may calculate a representative peak group interval indicating the interval between the plurality of representative peak groups in the time direction. The filter setting unit may set a frequency filter based on the representative peak group interval calculated by the peak interval calculation unit. In this case, the frequency filter can more accurately extract a frequency band corresponding to the biological information.

[0011] In one aspect, the biological information may be a periodically repeated movement of the living body. The peak group selection unit may select, from the plurality of peak groups, combinations of peak groups located at intervals in the time direction that are likely to be spaced apart relative to the movement period as the plurality of representative peak groups. In this case, a combination of peak groups located at intervals closer to the movement period of the living body is selected, so that the frequency band corresponding to the biological information can be extracted more accurately by the frequency filter.

[0012] In the above-described one aspect, the filter setting unit may set the frequency filter to have a center frequency calculated based on the positions in the time direction of the plurality of peak groups of the plurality of signal information, in which case the frequency band corresponding to the biological information can be extracted more accurately.

[0013] In the above aspect, the peak interval calculation unit may calculate a plurality of representative peak group intervals. The filter setting unit may set a frequency filter so that a center frequency is the reciprocal of an average of the plurality of representative peak group intervals. In this case, the frequency band corresponding to the biological information can be extracted more accurately.

[0014] In one of the above aspects, the peak group calculation unit may identify, from among the peaks included in the plurality of pieces of signal information, at least one peak that is within a predetermined time range from each of the peaks included in the reference signal information that serves as a reference among the plurality of pieces of signal information.The peak group calculation unit may calculate the position of each peak group in the time direction based on the identified peaks.In this case, information related to the peaks of the plurality of pieces of signal information is consolidated into one, and it is considered that the interval between the positions of each peak group in the time direction approaches the period of the movement of the living body more closely.As a result, a frequency filter that can more accurately extract the frequency band corresponding to the living body information can be set.

[0015] In one of the above aspects, the peak group calculation unit may calculate, for each peak group, the average of the positions in the time direction of at least one peak included in the peak group as the position of the peak group in the time direction. In this case, information about the peaks of multiple signal information is consolidated into one, and it is considered that the interval between the positions of each peak group in the time direction approaches the period of the movement of the living body more closely. As a result, a frequency filter that can more accurately extract the frequency band corresponding to the living body information can be set.

[0016] In one aspect, each piece of signal information may be information obtained by performing a short-time Fourier transform on information corresponding to the reflected wave and then spectrally integrating the information for each time segment. In this case, noise can be removed from the signal information.

[0017] In the above-mentioned one aspect, the information based on at least one of the plurality of pieces of signal information that is subjected to filtering processing in the biological information acquisition unit may be information that combines at least two of the plurality of pieces of signal information, in which case noise in the signal information can be further reduced.

[0018] In one of the above aspects, the information on at least one of the plurality of pieces of signal information filtered by the biological information acquisition unit may be based on signal information corresponding to a reflected wave reflected at a position where the skin fluctuation corresponding to the biological information is greatest among the positions where the plurality of reflected waves are reflected. In this case, the detection accuracy of the biological information can be further improved. For example, when detecting a heartbeat, the detection accuracy of the heartbeat can be further improved by using signal information corresponding to a reflected wave reflected at a position close to the heart. For example, when detecting respiration, the detection accuracy of respiration can be further improved by using signal information corresponding to a reflected wave reflected at a position close to the lungs.

[0019] According to another aspect of the present invention, a program causes a computer to acquire a plurality of pieces of signal information, set a frequency filter, and acquire biometric information of a living organism. Each piece of signal information corresponds to a different reflected wave from the living organism. Each piece of signal information has a plurality of peaks in the time domain. The frequency filter is set based on the positions of the peaks in the time domain in the plurality of pieces of signal information. The biometric information of the living organism is acquired by filtering information based on at least one piece of the plurality of pieces of signal information using the frequency filter.

[0020] A biological information detection method according to yet another aspect of the present invention includes acquiring a plurality of pieces of signal information, setting a frequency filter, and acquiring biological information of a living organism. Each of the plurality of pieces of signal information corresponds to a different reflected wave from the living organism. Each of the plurality of pieces of signal information has a plurality of peaks in the time domain. The frequency filter is set based on the positions of the plurality of peaks in the time domain in the plurality of pieces of signal information. The biological information of the living organism is acquired by filtering information based on at least one of the plurality of pieces of signal information using the frequency filter. [Effects of the Invention]

[0021] One aspect of the present invention provides a biometric information detection system that can ensure the accuracy of biometric information detection while detecting biometric information in a non-contact manner. Another aspect of the present invention provides a program that can ensure the accuracy of biometric information detection while detecting biometric information in a non-contact manner. Yet another aspect of the present invention provides a biometric information detection method that can ensure the accuracy of biometric information detection while detecting biometric information in a non-contact manner. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a block diagram of a biological information detection system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a schematic diagram of a biological information detection system. [Figure 3]FIG. 1 is a schematic diagram of a biological information detection system. [Figure 4] FIG. 10 is a diagram illustrating an example of a spectrogram output from a Fourier transform unit. [Figure 5] FIG. 10 is a diagram illustrating an example of information output from an integrating unit. [Figure 6] 10 is a diagram for explaining an example of calculation processing in a peak group calculation unit. FIG. [Figure 7] 10A and 10B are diagrams for explaining an example of calculation processing in a peak group selection unit. [Figure 8] 10A and 10B are diagrams illustrating an example of a result of a filtering process performed by a frequency filter set in a filter setting unit. [Figure 9] FIG. 2 is a diagram illustrating an example of a hardware configuration of a signal processing unit of the biological information detection system. [Figure 10] 10 is a flowchart illustrating an example of a biological information detection method. [Figure 11] 10A and 10B are diagrams illustrating biometric information acquired by a biometric information acquisition unit when different signal information is input. [Figure 12] FIG. 10(a) is a diagram showing the experimental results of a comparative example, and FIG. 10(b) is a diagram showing the experimental results of the biological information detection system according to this embodiment. [Figure 13] FIG. 10 is a diagram showing the cumulative probability distribution CDF of the error between the estimated RRI and the true value. [Figure 14] FIG. 10 is a diagram showing the detection accuracy of biological information for each subject using RMSE. [Figure 15] FIG. 10 is a diagram showing the cumulative probability distribution CDF of the error between the estimated RRI and the true value. [Figure 16] FIG. 10 is a diagram showing the cumulative probability distribution CDF of the error between the estimated RRI and the true value. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, an embodiment of the biological information detection system of the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same or corresponding parts will be given the same reference numerals, and duplicated explanations will be omitted. First, with reference to Figs. 1 to 3, a schematic configuration of the biological information detection system of the embodiment of the present invention will be described. Fig. 1 is a block diagram of the biological information detection system of this embodiment. Figs. 2 and 3 are schematic diagrams of the biological information detection system.

[0024] The biological information detection system 1 detects biological information related to a living organism in a non-contact manner. The "biological information" refers to information about periodically repeated movements of the living organism, including, for example, heartbeat and breathing information. The biological information detection system 1 irradiates the living organism with electromagnetic waves or sound waves and acquires information about multiple reflected waves from the living organism. The electromagnetic waves may include visible light. The "information about reflected waves" refers to information indicating temporal changes in the amplitude of the reflected waves. The biological information detection system 1 detects the biological information based on the acquired information about the multiple reflected waves, utilizing the diversity effect of the multiple reflected waves. The biological information detection system 1 detects the biological information by estimating multiple peaks indicating the period of time change in the biological information based on the information about the multiple reflected waves. The "period of time change in the biological information" refers to, for example, the period of the biological organism's movements. For example, the period of the heartbeat or the period of breathing, or other biological movements, is detected as the biological information. The biological information detection system 1 includes a signal control unit 2 and a signal processing unit 3.

[0025] As shown in FIG. 2, the signal control unit 2 performs beamforming. The signal control unit 2 irradiates a transmission wave T, such as an electromagnetic wave or a sound wave, toward a living body and acquires information about a reflected wave R from the living body. The signal control unit 2 acquires information about a plurality of different reflected waves R. The signal control unit 2 is, for example, a Doppler radar. In this case, the signal control unit 2 detects biological information of the living body by observing a frequency shift caused by the Doppler effect. The biological information detection system 1 may also be a radar that utilizes a method other than the Doppler effect. The following mainly describes, as an example, a case where the signal control unit 2 is a Doppler radar. The signal control unit 2 includes a signal transmitting unit 11 and a signal receiving unit 12. In this embodiment, the signal control unit 2 includes a plurality of signal receiving units 12.

[0026] The signal transmitting unit 11 oscillates a transmission wave T. The transmission wave T is, for example, an unmodulated continuous wave (CW). The signal control unit 2 of the biological information detecting system 1 is placed, for example, in front of a seated living body M. The distance d0 between the biological information detecting system 1 and the living body M is, for example, 60 cm to 70 cm. The distance d0 between the biological information detecting system 1 and the living body M may be, for example, 3 m or more.

[0027] 3, the signal transmitting unit 11 of the signal control unit 2 transmits, for example, transmission waves T0, T1, T2, T3, and T4 toward the living body M. The transmission waves T0, T1, T2, T3, and T4 are, for example, one signal wave transmitted from the signal transmitting unit 11. Here, for convenience, components of one signal wave directed at different angles will be described as transmission waves T0, T1, T2, T3, and T4. As a modification of this embodiment, the signal transmitting unit 11 may transmit the transmission waves T0, T1, T2, T3, and T4 independently from each other at different locations or at different times.

[0028] The transmission waves T0, T1, T2, T3, and T4 are transmitted from the biological information detection system 1 at, for example, different angles. The transmission wave T0 is transmitted, for example, toward the position where the skin variation corresponding to the biological information of the detection target is greatest among the positions toward which the transmission waves T0 to T4 are directed. The transmission wave T0 is transmitted, for example, toward the position nearest to the heart or lungs of the living body M among the transmission waves T0 to T4.

[0029] For example, the transmission wave T1 is transmitted at an angle θ1 from the transmission wave T0. The transmission wave T2 is transmitted at an angle θ2 from the transmission wave T0. The transmission wave T3 is transmitted at an angle θ3 from the transmission wave T0. The transmission wave T4 is transmitted at an angle θ4 from the transmission wave T0. For example, the angles θ1, θ2, θ3, and θ4 are +15°, +30°, -15°, and -30°, respectively. For example, when the angle is positive, the wave is inclined upward from the transmission wave T0, and when the angle is negative, the wave is inclined downward from the transmission wave T0. Both the upward and downward directions are parallel to the vertical direction.

[0030] As shown in FIG. 2 , the signal receiving unit 12 receives multiple reflected waves R from the living body M. The reflected waves R are modulated according to the biological information of the living body M. The signal receiving unit 12 receives multiple reflected waves R, for example, corresponding to the above-mentioned transmission waves T0, T1, T2, T3, and T4. The multiple reflected waves R reach the signal receiving unit 12 from the living body M via, for example, different paths. Each reflected wave R is received by, for example, a different signal receiving unit 12. For example, when the signal transmitting unit 11 emits multiple transmission waves T having different frequencies, when the signal transmitting unit 11 emits multiple transmission waves T at different times, or when the signal transmitting unit 11 emits multiple transmission waves T to different parts of the living body M, the multiple reflected waves R may be received by a single signal receiving unit 12. For example, the reflected waves R are modulated according to chest wall fluctuations x(t) caused by the heartbeat or breathing of the living body M. Hereinafter, as an example, detection of the heartbeat of the living body M using a Doppler radar will be mainly described.

[0031] The signal transmitting unit 11 includes a source 21, a power amplifier 22, and a transmitting antenna 23. The source 21 generates a signal of a transmission wave T to be transmitted from the transmitting antenna 23. The source 21 is a source that generates the signal of the transmission wave T. The power amplifier 22 amplifies the signal generated in the source 21. The transmitting antenna 23 converts the signal from the power amplifier 22 into a transmission wave T and transmits it toward the living body M.

[0032] The signal receiving unit 12 includes a receiving antenna 31, a regulator 32, a mixer 33, a band-pass filter 34, a voltage gain control amplifier 35, and an AD converter 36. The receiving antenna 31 converts the modulated reflected wave R into a signal. The regulator 32 performs processing such as noise removal from the signal converted from the reflected wave R. The mixer 33 mixes the signal generated by the source 21 with the signal output from the regulator 32. As a result, a Doppler signal is generated. The band-pass filter 34 removes noise from the Doppler signal generated by the mixer 33. The voltage gain control amplifier 35 amplifies the signal output from the band-pass filter 34. The AD converter 36 converts the signal output from the voltage gain control amplifier 35 from an analog signal to a digital signal. As a result, a digital signal containing information about the Doppler signal is output from the signal receiving unit 12. The signal receiving unit 12 outputs the converted Doppler signal to the signal processing unit 3.

[0033] The signal processing unit 3 acquires information indicating biological information based on the signal acquired from the signal receiving unit 12. The signal processing unit 3 processes, for example, the Doppler signal acquired by the signal receiving unit 12, and generates information indicating biological information of the living body M. As shown in FIG. 1 , the signal processing unit 3 includes a signal information acquiring unit 41, a tentative peak detecting unit 42, a peak group calculating unit 43, a peak group selecting unit 44, a peak interval calculating unit 45, a filter setting unit 46, an information calculating unit 47, a biological information acquiring unit 48, and a storage unit 49.

[0034] The signal information acquisition unit 41 acquires information output from the signal receiving unit 12. For example, the signal information acquisition unit 41 performs various processes on the information acquired from the signal receiving unit 12 to generate a signal according to a frequency component corresponding to biological information of the detection target, and acquires the generated signal as signal information. For example, the signal information acquisition unit 41 acquires and outputs a plurality of pieces of signal information based on the information output from the signal receiving unit 12. Each of the plurality of pieces of signal information corresponds to a different reflected wave R from the living body M. For example, the signal information acquisition unit 41 extracts a plurality of pieces of signal information corresponding to different parts based on signals acquired from the receiving antennas 31 of the plurality of signal receiving units 12 by signal processing.

[0035] Each of the plurality of pieces of signal information has a plurality of peaks in the time domain. In this specification, the "time domain" refers to a state in which the change in the intensity of the signal information over time is shown with the intensity and time being along axes perpendicular to each other. As a modification of this embodiment, the signal information acquisition unit 41 may directly output the information output from the signal receiving unit 12. In this embodiment, the signal information acquisition unit 41 includes a reception information acquisition unit 51, a first filter unit 52, a Fourier transform unit 53, an integration unit 54, and a second filter unit 55.

[0036] The reception information acquisition unit 51 acquires information output from the signal receiving unit 12. The reception information acquisition unit 51 acquires, for example, a Doppler signal related to the reflected wave R received by the signal receiving unit 12. The acquired Doppler signal represents the frequency shift between the signal that generates the transmission wave T and the signal generated from the reflected wave R. The acquired Doppler signal is composed of, for example, an I / Q signal. The I signal is a signal having an in-phase component that is in phase with the signal that generates the transmission wave T. The Q signal is a signal having a quadrature component that is orthogonal to the phase of the signal that generates the transmission wave T.

[0037] The signal transmitting unit 11 transmits the transmission wave T in a range of, for example, -60° to +60° with respect to the direction in which the transmission wave T0 is transmitted. In other words, the signal transmitting unit 11 transmits the transmission wave T, for example, in a range from the thighs to the head by beamforming. The signal transmitting unit 11 may transmit the transmission wave T in nine directions at intervals of 15°, for example. In this case, the signal receiving unit 12 receives nine reflected waves R corresponding to the transmission waves T in the nine directions. In this case, the reception information acquiring unit 51 acquires nine I / Q signals corresponding to the transmission waves T in the nine directions.

[0038] The first filter unit 52 performs a filtering process on the information acquired by the reception information acquisition unit 51. The first filter unit 52 performs a filtering process on, for example, the Doppler signal acquired by the reception information acquisition unit 51. For example, the first filter unit 52 includes a band-pass filter that removes noise components from the Doppler signal. The first filter unit 52 removes noise components from the Doppler signal using the band-pass filter. The pass band of this band-pass filter is determined depending on the biological information to be detected. For example, when the biological information detection system 1 detects a heartbeat, the pass band of the band-pass filter is set to be equal to or greater than 5.0 Hz and equal to or less than 30 Hz.

[0039] The Fourier transform unit 53 performs a short-time Fourier transform (STFT) on the input information. In this specification, the term "short-time Fourier transform" refers to creating information for multiple time segments from the input information and performing a Fourier transform for each time segment. For example, in the short-time Fourier transform, information for multiple time segments is created by multiplying the input information by a window function while shifting it. As a result, the Fourier transform unit 53 converts the input information from the time domain to the frequency domain for each time segment using the short-time Fourier transform. Each time segment has, for example, the same time width. For example, in the short-time Fourier transform, the window size is 512 ms and the step size is 10 ms. The Fourier transform unit 53 performs a short-time Fourier transform on the information input from the signal receiving unit 12 via the received information acquisition unit 51 and the first filter unit 52.

[0040] The Fourier transform unit 53 performs a short-time Fourier transform on the input information to create and output information indicating the intensity of each frequency for each time segment. The Fourier transform unit 53 performs a short-time Fourier transform on the input information to create and output a spectrogram. The spectrogram shows a frequency spectrum for each time segment. For example, the spectrogram created by the Fourier transform unit 53 is displayed as a graph in which the horizontal axis represents time and the vertical axis represents frequency, and the brightness or color of a pixel at each coordinate represents intensity at the time and frequency corresponding to the coordinate. FIG. 4 shows an example of a spectrogram created by the Fourier transform unit 53. In FIG. 4, intensity is indicated by a grayscale. The Fourier transform unit 53 creates a spectrogram for information input from the signal receiving unit 12, for example. The Fourier transform unit 53 creates a spectrogram based on the Doppler signal output from the first filter unit 52.

[0041] The integrator 54 performs spectral integration on the input information within a frequency range set in advance for each time segment, and outputs the result. The integrator 54, for example, creates information for multiple time segments from the input information and performs spectral integration for each time segment. For example, information for multiple time segments is created by multiplying the input information by a window function while shifting it. The integrator 54, for example, divides the input information into multiple time segments and performs spectral integration for each divided time segment. The "spectral integration" is the frequency integration of the frequency spectrum. In other words, the spectral integration is an integration in the frequency direction in the frequency domain.

[0042] The integrator 54 performs spectral integration on the information output from the Fourier transformer 53, for example, in a frequency range set in advance for each time segment. The frequency range for which spectral integration is performed corresponds, for example, to the passband of the filtering process in the first filter unit 52. The frequency range for which spectral integration is performed is, for example, from 5.0 Hz to 30 Hz. The integrator 54 performs spectral integration on the information output from the Fourier transformer 53, for example, in each time segment set in the Fourier transformer 53.

[0043] As a modification of this embodiment, the integrator 54 may use, for example, a window function to set each time interval for performing spectral integration to a time interval different from the time interval set by the Fourier transformer 53. Fig. 5 shows an example of information output from the integrator 54. In Fig. 5, the horizontal axis represents time, and the vertical axis represents the spectral integration value for each time interval. The integrator 4 outputs the spectral integration for each time interval.

[0044] The second filter unit 55 performs a filtering process on the information output from the integrating unit 54. For example, the second filter unit 55 includes a band-pass filter that removes noise components from the information output from the integrating unit 54. The cut-off frequency of this band-pass filter is determined depending on the biological information to be detected. The normal human heart rate is, for example, about 40 bpm (beats per minute) to 120 bpm. In this case, the frequency of the human heart rate is about 0.8 to 2.0 Hz. Therefore, for example, when the biological information detection system 1 detects a heart rate, the pass band of the band-pass filter is set to be equal to or greater than 0.8 Hz and equal to or less than 2.0 Hz.

[0045] For example, the signal information acquisition unit 41 outputs the information output from the second filter unit 55 as signal information to the tentative peak detection unit 42 and the information calculation unit 47. The signal information acquisition unit 41 outputs multiple pieces of signal information from the second filter unit 55 to the tentative peak detection unit 42 and the information calculation unit 47. The information output from the second filter unit 55 corresponds to the information output from the integrator unit 54. Therefore, each piece of signal information output from the signal information acquisition unit 41 is information obtained by performing a short-time Fourier transform on information corresponding to the reflected wave R and then performing spectral integration for each time segment. The signal information acquisition unit 41 may not include the second filter unit 55 and may output the information output from the integrator unit 54 as signal information to the tentative peak detection unit 42 and the information calculation unit 47. The signal information output to the tentative peak detection unit 42 and the information calculation unit 47 may be different.

[0046] The tentative peak detection unit 42 detects multiple tentative peaks PE0, PE1, PE2, and PE3 shown in FIG. 6 for each of the multiple pieces of signal information B0, B1, B2, and B3 input from the signal information acquisition unit 41. Signal information B0 is signal information corresponding to the transmitted wave T0 shown in FIG. 3. Signal information B1 is signal information corresponding to the transmitted wave T1 shown in FIG. 3. Signal information B2 is signal information corresponding to the transmitted wave T2 shown in FIG. 3. Signal information B3 is signal information corresponding to the transmitted wave T3 shown in FIG. 3. The tentative peak detection unit 42 does not need to use all of the signal information acquired by the signal information acquisition unit 41.

[0047] The tentative peaks PE0, PE1, PE2, and PE3 detected by the tentative peak detection unit 42 are tentative peaks used to estimate peaks that indicate periods of time change in the biological information. For example, the tentative peak detection unit 42 acquires the positions of the tentative peaks PE0, PE1, PE2, and PE3 in the time direction in each of the signal information B0, B1, B2, and B3. The "time direction" refers to the time axis direction in the time domain. For example, the tentative peak detection unit 42 acquires the positions of the tentative peaks PE0 in the time direction in the signal information B0, and acquires the positions of the tentative peaks PE1 in the time direction in the signal information B1. The "position of the peak in the time direction" refers to the time at which the peak occurred. Hereinafter, the "position in the time direction" may also be simply referred to as "position."

[0048] The peak group calculation unit 43 sets a plurality of peak groups GR based on the positions of each tentative peak PE0, PE1, PE2, and PE3 detected by the tentative peak detection unit 42. Each peak group GR includes at least one of the plurality of tentative peaks PE0, PE1, PE2, and PE3. The peak group calculation unit 43 identifies at least one tentative peak PE0, PE1, PE2, and PE3 constituting each peak group GR from the plurality of tentative peaks PE0, PE1, PE2, and PE3 included in the plurality of signal information B0, B1, B2, and B3, for example.

[0049] The peak group calculation unit 43 determines, for example, reference signal information from among the plurality of pieces of signal information B0, B1, B2, and B3. The reference signal information is, for example, the signal information B0 corresponding to the transmitted wave T0. The peak group calculation unit 43 identifies, for example, at least one tentative peak PE0, PE1, PE2, or PE3 within a predetermined time range W from each of the plurality of tentative peaks PE0 included in the reference signal information as peaks constituting each peak group GR. This time range W is, for example, ±0.05 seconds from the tentative peak PE0.

[0050] The peak group calculation unit 43 calculates positions P1, P2, P3, P4, P5, P6, P7, and P8 of each peak group GR in the time direction based on the positions of multiple tentative peaks PE0, PE1, PE2, and PE3 in the multiple pieces of signal information B0, B1, B2, and B3. The peak group calculation unit 43 calculates positions P1 to P8 of each peak group GR based on, for example, the tentative peaks PE0, PE1, PE2, and PE3 included in each peak group GR. For each peak group GR, the peak group calculation unit 43 calculates the average of the position in the time direction of at least one tentative peak PE0, PE1, PE2, and PE3 included in the peak group GR as the position P1 to P8 of the peak group GR. In this specification, the term "average" is not limited to an arithmetic average and may include, for example, a weighted average.

[0051] The peak group selection unit 44 selects a plurality of representative peak groups from the plurality of peak groups GR based on the position of each peak group GR. The "representative peak group" refers to the plurality of peak groups GR selected by the peak group selection unit 44. The representative peak group may be a part or all of the plurality of peak groups GR set by the peak group calculation unit 43. The "selection of a plurality of representative peak groups" includes at least one of selecting the representative peak group itself and selecting the position of the representative peak group in the time direction. The representative peak group is a peak group GR located in the time direction at a plausible interval relative to the period of the movement of the living organism M. The peak group selection unit 44 selects, from the plurality of peak groups GR, a combination of peak groups GR located in the time direction at a plausible interval relative to the period of the movement of the living organism M as the plurality of representative peak groups. The movement of the living organism M is, for example, heartbeat.

[0052] The peak group selection unit 44 uses, for example, the Viterbi algorithm to identify combinations of peak groups GR located at plausible intervals in the time direction, and selects peak groups GR included in the identified combinations as multiple representative peak groups. The Viterbi algorithm is one of the maximum likelihood estimation methods that selects the most likely sequence from multiple sequences. "Most likely" and "likely" have the same meaning.

[0053] The peak group selection unit 44 calculates branch metrics based on adjacent RRIs (RR intervals) in the Viterbi algorithm. "RRI" refers to the interval between nodes. In this embodiment, the nodes include positions P1, P2, P3, P4, P5, P6, P7, and P8 of each peak group GR, as shown in FIG. 7. In FIG. 7, positions P17 to P19 are shown as the positions of the peak group GR in addition to positions P1 to P8. "Metric" refers to an index indicating the likelihood of a state within a sequence. "Branch metric" refers to an index indicating the likelihood between states. For example, the branch metric is calculated based on adjacent RRIs. The branch metric is, for example, the square of the difference between adjacent RRIs. In this embodiment, it has been confirmed that the difference between adjacent RRIs follows a normal distribution with a mean of zero.

[0054] The peak group selection unit 44 uses the Viterbi algorithm to identify a combination of peak groups GR with the smallest or largest path metric at positions P1 to P19 of the peak groups GR. A "path metric" is the accumulation of branch metrics in a sequence. The sequence with the smallest or largest path metric is the most likely sequence. The peak group selection unit 44 identifies each node included in the most likely sequence as a representative peak group or the position of a representative peak group. In this embodiment, the sequence of nodes connected by a thick line among the lines connecting the nodes is the sequence identified as the most likely sequence. In other words, in the example shown in FIG. 7, the peak groups GR located at positions P1, P2, P4, ... P19 are selected as representative peak groups.

[0055] The peak interval calculation unit 45 calculates a representative peak group interval that indicates the interval between multiple representative peak groups in the time direction. In the example shown in Fig. 7, the difference between position P1 and position P2 and the difference between position P2 and position P4 are the intervals between each representative peak group. The peak interval calculation unit 45 outputs the average of the multiple representative peak group intervals as the representative peak group interval.

[0056] The filter setting unit 46 sets a frequency filter based on the positions in the time direction of multiple tentative peaks of multiple pieces of signal information acquired by the signal information acquisition unit 41. The frequency filter removes desired frequency components from the signal information by filtering. The frequency filter is, for example, a band-pass filter. The "setting of the frequency filter" includes the band setting of a variable filter. Hereinafter, the frequency filter set by the filter setting unit 46 will be simply referred to as the "frequency filter."

[0057] The filter setting unit 46 sets a frequency filter based on the position in the time direction of each peak group GR calculated by the peak group calculation unit 43. The filter setting unit 46 sets the frequency filter based on, for example, the representative peak group interval output from the peak interval calculation unit 45. The filter setting unit 46 sets the frequency filter so that it has a center frequency calculated based on the position of each peak group GR. For example, the filter setting unit 46 sets the frequency filter so that its center frequency is the inverse of the interval between the representative peak groups output from the peak interval calculation unit 45.

[0058] The information calculation unit 47 calculates and outputs, for example, a combination of multiple pieces of signal information based on the signal information output from the signal information acquisition unit 41. For example, the information calculation unit 47 does not need to use all of the signal information acquired by the signal information acquisition unit 41. The information calculation unit 47 combines at least two pieces of signal information acquired by the signal information acquisition unit 41. The combination of signal information is, for example, an average of the multiple pieces of signal information. In this case, the information calculation unit 47 averages at least two pieces of signal information acquired by the signal information acquisition unit 41. As a modification of this embodiment, the combination of signal information may be a synthesis of multiple pieces of signal information, such as a sum of the multiple pieces of signal information.

[0059] The information calculation unit 47 receives, for example, signal information corresponding to the reflected wave R reflected at a position where the skin fluctuation corresponding to the biological information of the detection target is greatest among the positions where the multiple reflected waves R are reflected. The information calculation unit 47 receives, for example, signal information corresponding to the reflected wave R reflected at a position closest to the heart or lungs of the living body M among the multiple reflected waves R. The reflected wave R reflected at the position closest to the heart or lungs of the living body M is the reflected wave R of the transmitted wave T0 in FIG. 3. In this embodiment, the information calculation unit 47 receives, for example, some of the multiple pieces of signal information acquired by the signal information acquisition unit 41. In other words, the information calculation unit 47 receives, for example, signal information corresponding to some of the multiple reflected waves R. For example, the information calculation unit 47 acquires multiple pieces of signal information corresponding to the multiple reflected waves R and averages the signal information. The information input to the information calculation unit 47 includes signal information corresponding to the transmitted wave T0, the transmitted wave T3, and the transmitted wave T4 shown in FIG. 3. As a modification of this embodiment, one piece of signal information may be input to the information calculation unit 47. In this case, the information calculation unit 47 outputs the input signal information as is.

[0060] The biometric information acquiring unit 48 acquires biometric information of the living organism M by performing filtering processing using a frequency filter on information based on at least one of the multiple pieces of signal information. FIG. 8 is a diagram illustrating an example of the result of the filtering processing using the frequency filter. For example, the biometric information acquiring unit 48 acquires peaks indicating the period of time change of the biometric information from the result of the filtering processing using the frequency filter. For example, the biometric information acquiring unit 48 acquires the number of acquired peaks and the peak interval as biometric information from the result of the filtering processing using the frequency filter. For example, the biometric information acquiring unit 48 may acquire the number of acquired peaks and the peak interval as the heart rate and the heartbeat interval from the result of the filtering processing using the frequency filter. The biometric information acquiring unit 48 may output the result of the filtering processing as biometric information. The biometric information acquiring unit 48 outputs the acquired biometric information to the storage unit 49. The biometric information acquiring unit 48 may output the acquired biometric information to the outside of the biometric information detection system 1.

[0061] The information based on at least one of the plurality of pieces of signal information, on which the filtering process is performed in the biometric information acquiring unit 48, is, for example, information output from the information calculating unit 47. As a modification of the present embodiment, the biometric information acquiring unit 48 may acquire signal information directly from the signal information acquiring unit 41 without using the information calculating unit 47. The information based on at least one of the plurality of pieces of signal information, on which the filtering process is performed in the biometric information acquiring unit 48, is, for example, based on signal information corresponding to a reflected wave R reflected at a position where the skin variation corresponding to the biometric information of the detection target is greatest, among the positions where the plurality of reflected waves R are reflected. The information based on at least one of the plurality of pieces of signal information, on which the filtering process is performed in the biometric information acquiring unit 48, is, for example, based on signal information corresponding to a reflected wave R reflected at a position closest to the heart or lungs of the living body M, among the plurality of reflected waves R. For example, the information calculating unit 47 acquires signal information corresponding to the plurality of transmitted waves T0, T3, and T4 in FIG. 3 and averages this signal information.

[0062] The storage unit 49 stores information used by each functional unit in advance. The storage unit 49 stores outputs from each functional unit. The storage unit 49 stores, for example, information acquired by the signal information acquisition unit 41. The storage unit 49 stores, for example, calculation results from the Fourier transform unit 53, the integrator 54, the peak group calculation unit 43, the peak group selection unit 44, the peak interval calculation unit 45, and the information calculation unit 47. The storage unit 49 stores, for example, band information of a frequency filter. The storage unit 49 stores biological information acquired by the biological information acquisition unit 48.

[0063] Next, a hardware configuration of the signal processing unit 3 of the biological information detection system 1 will be described with reference to Fig. 9. Fig. 9 is a diagram showing an example of the hardware configuration of the signal processing unit 3 of the biological information detection system 1.

[0064] The signal processing unit 3 includes a processor 101, a main memory device 102, an auxiliary memory device 103, a communication device 104, an input device 105, an output device 106, and a display device 107. The signal processing unit 3 includes one or more computers configured with these hardware devices and software such as programs. Each of the signal information acquiring unit 41, the tentative peak detecting unit 42, the peak group calculating unit 43, the peak group selecting unit 44, the peak interval calculating unit 45, the filter setting unit 46, the information calculating unit 47, the biological information acquiring unit 48, and the storage unit 49 may be configured with one computer or multiple computers. The biological information detection system 1 is realized in cooperation with hardware.

[0065] When the signal processing unit 3 is configured by multiple computers, these computers may be connected locally or via a communication network such as the Internet or an intranet. This connection logically constructs one biological information detection system 1.

[0066] The processor 101 executes an operating system, application programs, etc. The main memory device 102 is composed of a read-only memory (ROM) and a random-access memory (RAM). For example, at least some of the various functional units of the signal processing unit 3 can be realized by the processor 101 and the main memory device 102.

[0067] The auxiliary storage device 103 is a storage medium configured with a hard disk, a flash memory, etc. The auxiliary storage device 103 generally stores a larger amount of data than the main storage device 102. For example, at least a part of the storage unit 49 can be realized by the auxiliary storage device 103.

[0068] The communication device 104 is configured by a network card or a wireless communication module. For example, at least a part of the signal information acquisition unit 41 can be realized by the communication device 104. The input device 105 is configured by a keyboard, a mouse, a touch panel, etc. For example, at least a part of the signal information acquisition unit 41 can be realized by the input device 105. The output device 106 is configured by a printer, a display, etc. For example, at least a part of the biometric information acquisition unit 48 can be realized by the output device 106.

[0069] The auxiliary storage device 103 stores in advance a program and data necessary for processing. This program causes a computer to execute each functional element of the biometric information detection system 1. This program causes, for example, each process in a biometric information detection method described below to be executed on the computer. This program may be provided in a state recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. This program may also be provided as a data signal via a communication network.

[0070] Next, a biological information detection method will be described with reference to Fig. 10. Fig. 10 is a flowchart showing an example of biological information detection.

[0071] First, the signal control unit 2 performs beamforming (process S1). For example, in process S1, the signal transmission unit 11 transmits a transmission wave T toward a living body, and the signal reception unit 12 receives a reflected wave R from the living body. The information received by the signal reception unit 12 is acquired by the reception information acquisition unit 51.

[0072] Next, the first filter unit 52 performs a filtering process on the information acquired by the reception information acquisition unit 51 (process S2). When the biological information detection system 1 detects a heartbeat, the filtering process in process S2 extracts a frequency band of 5.0 Hz or more and 30 Hz or less from the information acquired by the reception information acquisition unit 51.

[0073] Next, the Fourier transform unit 53 performs a short-time Fourier transform on the information output from the first filter unit 52 (process S3). In process S3, the Fourier transform unit 53 performs a short-time Fourier transform on the information output from the first filter unit 52, thereby creating and outputting information indicating the intensity of each frequency for each time segment. For example, the Fourier transform unit 53 performs a short-time Fourier transform on the information output from the first filter unit 52, thereby creating and outputting a spectrogram.

[0074] Next, the integrator 54 performs spectral integration in a frequency range set in advance for each time segment on the information output from the Fourier transformer 53 (process S4). For example, in process S4, the integrator 54 performs spectral integration on the information output from the Fourier transformer 53 for each time segment in a frequency range that is the same as the pass band of the filtering process in the first filter 52.

[0075] Next, the second filter unit 55 performs a filtering process on the information output from the integrating unit 54 (process S5). When the biological information detection system 1 detects a heartbeat, the filtering process in process S5 extracts a frequency band of 0.8 Hz or more and 2.0 Hz or less from the information output from the integrating unit 54. When process S5 ends, processes S6 and S12 are executed. Processes S6 to S11 and process S12 may be executed in parallel. Either process S6 to S11 or process S12 may be executed first.

[0076] Next, the tentative peak detection unit 42 detects a plurality of tentative peaks for each of the plurality of pieces of signal information output from the second filter unit 55 (process S6). In process S6, the tentative peak detection unit 42 detects a plurality of tentative peaks PE0, PE1, PE2, and PE3 for each of the plurality of pieces of signal information B0, B1, B2, and B3 output from the second filter unit 55, for example.

[0077] After step S6 is completed, the peak group calculation unit 43 sets multiple peak groups GR based on the positions of each tentative peak detected by the tentative peak detection unit 42 (step S7). In step S7, the peak group calculation unit 43 determines, for example, signal information B0 corresponding to the transmitted wave T0 as the reference signal information. In step S7, the peak group calculation unit 43 identifies, for example, at least one tentative peak PE0, PE1, PE2, or PE3 within a predetermined time range W from each of the multiple tentative peaks PE0 included in the reference signal information as peaks that constitute each peak group GR. This time range W is, for example, ±0.05 seconds from the tentative peak PE0.

[0078] When process S7 is completed, the peak group calculation unit 43 calculates the positions of the multiple peak groups GR in the time direction (process S8). In process S8, for example, for each peak group GR, the peak group calculation unit 43 calculates the average of the positions in the time direction of at least one tentative peak PE0, PE1, PE2, PE3 included in the peak group GR as the positions P1 to P8 of the peak group GR.

[0079] When process S8 is completed, the peak group selection unit 44 selects a representative peak group from the plurality of peak groups GR based on the position of each peak group GR (process S9). In process S9, the peak group selection unit 44 identifies a combination of peak groups GR that has the smallest or largest path metric at positions P1 to P19 of the peak groups GR, for example, by the Viterbi algorithm. In the example shown in Fig. 7, the peak groups GR located at positions P1, P2, P4, ... P19 are selected as the representative peak groups.

[0080] When process S9 is completed, the peak interval calculation unit 45 calculates a representative peak group interval indicating the interval between the plurality of representative peak groups in the time direction (process S10). In process S10, the peak interval calculation unit 45 outputs, for example, the average of the plurality of representative peak group intervals as the representative peak group interval.

[0081] When process S10 is completed, the filter setting unit 46 sets a frequency filter based on the positions of the multiple tentative peaks in the time direction (process S11). In process S11, the filter setting unit 46 sets the frequency filter so that the center frequency is the inverse of the interval between the representative peak groups output from the peak interval calculation unit 45, for example.

[0082] When process S5 is completed, the information calculation unit 47 calculates the average of the signal information (process S12). In process S12, the information calculation unit 47 acquires the multiple pieces of signal information B0, B1, B2, and B3 processed in process S5 and averages these pieces of signal information.

[0083] When process S11 and process S12 are completed, the biological information acquisition unit 48 acquires biological information (process S13). In process S13, the biological information acquisition unit 48 performs a filtering process on the information output from the information calculation unit 47 in process S12 using the frequency filter set in process S11, thereby acquiring biological information of the living body. The biological information acquisition unit 48 acquires, for example, information indicating at least one of a heart rate and a heartbeat interval, or at least one of a respiratory rate and a respiratory interval, as the biological information.

[0084] Next, the effects of the biological information detecting system 1, the program, and the biological information detecting method in the above-described embodiment will be described.

[0085] The biological information detection system 1 includes a signal information acquisition unit 41, a filter setting unit 46, and a biological information acquisition unit 48. The signal information acquisition unit 41 acquires a plurality of pieces of signal information B0, B1, B2, and B3 corresponding to different reflected waves R from a living organism. The filter setting unit 46 sets a frequency filter based on the positions in the time direction of a plurality of tentative peaks PE0, PE1, PE2, and PE3 of the plurality of pieces of signal information B0, B1, B2, and B3. The biological information acquisition unit 48 acquires biological information of the living organism by performing a filtering process using the frequency filter on information based on at least one of the plurality of pieces of signal information B0, B1, B2, and B3.

[0086] As described above, multiple pieces of signal information B0, B1, B2, and B3 corresponding to different reflected waves R are used. Therefore, the diversity effect can improve the extraction accuracy of biometric information that is difficult to capture due to noise generated by the propagation of the transmitted wave T and the reflected wave R between the signal transmitter 11 and the living body or due to the living body's posture. Furthermore, since a frequency filter is set based on the tentative peaks PE0, PE1, PE2, and PE3 of the multiple pieces of signal information B0, B1, B2, and B3 corresponding to the multiple reflected waves R, the frequency band corresponding to the biometric information can be accurately extracted by the frequency filter. As a result, the detection accuracy of the biometric information can be ensured while detecting the biometric information in a non-contact manner.

[0087] The peak group calculation unit 43 sets a plurality of peak groups GR based on the positions in the time direction of a plurality of tentative peaks PE0, PE1, PE2, and PE3 of a plurality of pieces of signal information B0, B1, B2, and B3. Each of the plurality of peak groups GR includes at least one of the plurality of tentative peaks PE0, PE1, PE2, and PE3. The peak group calculation unit 43 calculates positions P1 to P8 of each peak group GR. The filter setting unit 46 sets a frequency filter based on the positions P1 to P8 of each peak group GR calculated by the peak group calculation unit 43. In this case, the frequency filter is set based on the positions P1 to P8 of the peak group GR including at least one of the plurality of tentative peaks PE0, PE1, PE2, and PE3. Therefore, the frequency filter can more accurately extract a frequency band corresponding to biological information.

[0088] The peak group selection unit 44 selects at least two peak groups GR from the plurality of peak groups GR as representative peak groups based on the positions P1 to P8 of each peak group GR. The peak interval calculation unit 45 calculates a representative peak group interval that indicates the interval between the plurality of representative peak groups in the time direction. The filter setting unit 46 sets a frequency filter based on the representative peak group interval calculated by the peak interval calculation unit 45. In this case, the frequency filter can more accurately extract a frequency band corresponding to biological information.

[0089] The biological information is a periodically repeated movement of a living organism. The peak group selection unit 44 selects, from among the plurality of peak groups GR, combinations of peak groups GR located at intervals in the time direction that are likely to correspond to the period of the movement of the living organism as a plurality of representative peak groups. In this case, since the combination of peak groups GR located at intervals closer to the period of the movement of the living organism is selected, the frequency band corresponding to the biological information can be extracted more accurately by the frequency filter.

[0090] The filter setting unit 46 sets the frequency filter to have a center frequency calculated based on the positions P1 to P8 in the time direction of the multiple peak groups GR of the multiple pieces of signal information B0, B1, B2, and B3. In this case, the frequency band corresponding to the biological information can be extracted more accurately.

[0091] The filter setting unit 46 sets a frequency filter so that the center frequency is the inverse of the average interval between a plurality of representative peak groups, thereby enabling more accurate extraction of the frequency band corresponding to the biological information.

[0092] The peak group calculation unit 43 identifies at least one tentative peak within a predetermined time range W from each of the tentative peaks PE0 included in the reference signal information from among the tentative peaks PE0, PE1, PE2, and PE3 included in the signal information B0, B1, B2, and B3. The peak group calculation unit 43 calculates positions P1 to P8 of each peak group GR in the time direction based on the identified tentative peaks. In this case, information on the tentative peaks PE0, PE1, PE2, and PE3 of the signal information B0, B1, B2, and B, respectively corresponding to the reflected waves R, is consolidated into one, and the intervals between the positions P1 to P8 of each peak group GR in the time direction are considered to be closer to the period of the movement of the living body. As a result, a frequency filter that can more accurately extract the frequency band corresponding to the living body information can be set.

[0093] The peak group calculation unit 43 calculates, for each peak group GR, the average of the positions in the time direction of the tentative peaks PE0, PE1, PE2, and PE3 included in the peak group GR as positions P1 to P8 of the peak group GR. In this case, information related to the tentative peaks PE0, PE1, PE2, and PE3 of the multiple pieces of signal information B0, B1, B2, and B corresponding to the multiple reflected waves R is combined into one, and it is considered that the intervals between the positions P1 to P8 in the time direction of each peak group GR become closer to the period of the movement of the living body. As a result, a frequency filter that can more accurately extract the frequency band corresponding to the living body information can be set.

[0094] Each of the signal information B0, B1, B2, and B3 is information obtained by performing a short-time Fourier transform on information corresponding to the reflected waves and then performing spectral integration for each time segment. In this case, noise can be removed from the signal information.

[0095] The information based on at least one of the plurality of pieces of signal information that is filtered in the biological information acquisition unit 48 is information that combines at least two of the plurality of pieces of signal information B0, B1, B2, and B3. In this case, noise in the signal information can be further reduced by a diversity effect or the like.

[0096] The information on at least one of the plurality of pieces of signal information filtered by the biological information acquisition unit 48 is based on the signal information B0 corresponding to the reflected wave R reflected at a position where the skin variation corresponding to the biological information of the detection target is greatest among the positions where the plurality of reflected waves R are reflected. In this case, the detection accuracy of the biological information can be further improved. For example, when detecting a heartbeat, the heartbeat detection accuracy can be further improved by using the signal information B0 corresponding to the reflected wave R reflected at a position close to the heart. For example, when detecting respiration, the respiration detection accuracy can be further improved by using the signal information B0 corresponding to the reflected wave R reflected at a position close to the lungs.

[0097] Next, an example of verification of the biological information detection system 1 will be described with reference to FIGS. 11 to 16. In this verification, heartbeat detection was performed to detect biological information. In this verification, the signal control unit 2 used unmodulated continuous waves, the oscillation frequency was 60 GHz, the sampling frequency was 1000 Hz, and the transmission power was 1 mW. In this test, the signal control unit 2 included four receiving antennas 31. There were five subjects whose biological information was to be detected. Each subject was seated and stationary when a transmission wave T was transmitted toward the living body and a reflected wave R from the living body was received. In this test, as shown in FIG. 3, transmission waves T0, T1, T2, T3, and T4 were transmitted from the signal transmission unit 11 to the living body M. Except for the verification to confirm distance characteristics, the distance from the transmitting antenna 23 and the receiving antenna 31 to the living body was 60 cm to 70 cm. Signal information was acquired for 120 seconds for each subject.

[0098] To determine the detection accuracy of the biological information detection system 1, the measured value from the electrocardiogram was used as the true value. The RMSE between the estimated RRI and the true value, and the cumulative probability distribution CDF of the error between the estimated RRI and the true value were used as evaluation indices for the detection accuracy of the biological information detection system 1. The estimated RRI is the heartbeat detection result in the biological information detection system. "N" is the number of observed RRIs, and "RRI est (i)” is the i-th estimated RRI, and “RRI refWhen (i)" is the i-th true RRI, the RMSE is expressed by the following equation (1).

number

[0099] Function F of the probability that the random variable X is less than or equal to x X (x) is expressed by the following equation (2).

number

[0100] FIG. 11 shows data D11, D12, D13, and D14 of the detection results of biological information based on signal information when transmission waves T4, T3, and T0 in the -30-degree, -15-degree, and 0-degree directions are irradiated onto a living body. That is, the detection results of biological information based on signal information corresponding to multiple reflected waves R are shown. Data D11 is data of biological information detected when a frequency filter is applied to signal information corresponding to the -30-degree direction. Data D12 is data of biological information detected when a frequency filter is applied to signal information corresponding to the -15-degree direction. Data D13 is data of biological information detected when a frequency filter is applied to signal information B0 corresponding to the 0-degree direction. Data D14 is data obtained by combining data D11, D12, and D13. As shown in region α, the waveforms of data D11, D12, and D13 are distorted. However, by using signal information corresponding to multiple reflected waves R, the waveform distortion is reduced, as in data D14. In this way, it was confirmed that by using signal information corresponding to a plurality of reflected waves R, waveform distortion is reduced and detection omissions are suppressed.

[0101] FIG. 12(a) shows the detection result of biological information by a comparative example. In this comparative example, after the above-mentioned process S5, the Viterbi algorithm is applied to the signal information output from the second filter unit 55. That is, the peaks shown are selected by applying the Viterbi algorithm to multiple tentative peaks included in the signal information output from the second filter unit 55. Therefore, each piece of data shown in FIG. 12(a) is data to which a frequency filter has not been applied. FIG. 12(b) shows the detection result of biological information by the biological information detection system 1. Therefore, each piece of data shown in FIG. 12(b) is data to which a frequency filter has been applied.

[0102] 12(a) and 12(b), data α1 is data indicating a true value. Data α2 is data of biological information detected when only the transmitted wave T0 is irradiated onto the living body and only signal information B0 corresponding to one reflected wave R is output from the signal information acquisition unit 41. Data α3 is data when the transmitted waves T4, T3, and T0 are irradiated onto the living body and signal information corresponding to each of the three reflected waves R is used.

[0103] For data α2 shown in Figure 12(a), the RMSE was 267 ms. For data α3 shown in Figure 12(a), the RMSE was 189 ms. Comparing data α2 and α3 in Figure 12(a) confirmed that the detection accuracy of RRI improves when signal information corresponding to multiple reflected waves R is used.

[0104] For data α2 shown in FIG. 12(b), the RMSE was 214 ms. For data α3 shown in FIG. 12(b), the RMSE was 67 ms. A comparison of FIG. 12(a) and FIG. 12(b) confirmed that the RRI detection accuracy improved when a frequency filter was applied. A comparison of data α2 and data α3 in FIG. 12(b) confirmed that the RRI detection accuracy further improved when a frequency filter was applied and signal information corresponding to multiple reflected waves R was used.

[0105] FIG. 13 shows the cumulative probability distribution CDF of the error between the estimated RRI and the true value. In FIG. 13, the horizontal axis represents the error between the estimated RRI and the true value, and the vertical axis represents the probability function. Data D21 to D26 are data obtained when only signal information corresponding to one reflected wave R is used and a frequency filter is applied. Data D21 to D26 are data based on transmitted waves T transmitted in different directions. In data D21 to D26, the irradiation angle of the transmitted wave T toward the living body varies from 60 degrees to -60 degrees. Data D30 is data obtained when signal information corresponding to multiple reflected waves R is used and a frequency filter is applied. A comparison of data D21 to D26 with data D30 confirms that when signal information corresponding to multiple reflected waves R is used, the occurrence of errors between the estimated RRI and the true value is significantly improved compared to when signal information corresponding to a single reflected wave R is used. When signal information corresponding to multiple reflected waves R was used and a frequency filter was applied, the RRI error was reduced by an average of 50 ms at the median CDF compared to when signal information corresponding to a single reflected wave R was used and a frequency filter was applied.

[0106] FIG. 14 shows the detection accuracy of biological information for each subject using RMSE. Data D31 is data obtained when signal information corresponding to one reflected wave R is used and a frequency filter is applied. Data D32 is data obtained when signal information corresponding to multiple reflected waves R is used and a frequency filter is applied. In data D31, the irradiation angle of the transmitted wave T to the biological body is 0 degrees. As shown in FIG. 14, it was confirmed that the detection accuracy when signal information corresponding to multiple reflected waves R is used is improved independently of the subject compared to when signal information corresponding to a single reflected wave R is used. The average RMSE for five subjects was improved by 67.5 ms when signal information corresponding to multiple reflected waves R was used compared to when signal information corresponding to a single reflected wave R was used.

[0107] 15 and 16 show the cumulative probability distribution CDF of the error between the estimated RRI and the true value. In Fig. 15 and Fig. 16, the horizontal axis shows the error between the estimated RRI and the true value, and the vertical axis shows the probability function.

[0108] 15 and 16, data D41 is data obtained when only signal information corresponding to one reflected wave R is output from the signal information acquisition unit 41, the Viterbi algorithm is applied to the output signal information, and a frequency filter is not applied. Data D42 is data obtained when only signal information corresponding to one reflected wave R is output from the signal information acquisition unit 41, and a frequency filter is applied. Data D43 is data obtained when signal information corresponding to multiple reflected waves R is output from the signal information acquisition unit 41, the Viterbi algorithm is applied to the output signal information, and a frequency filter is not applied. Data D50 is data obtained when signal information corresponding to multiple reflected waves R is output from the signal information acquisition unit 41, and a frequency filter is applied to the output signal information.

[0109] In the detection results shown in Fig. 15, the distance from the transmitting antenna 23 and the receiving antenna 31 to the living body was 2.0 m. In the detection results shown in Fig. 16, the distance from the transmitting antenna 23 and the receiving antenna 31 to the living body was 3.0 m. From the data D41, D42, D43, and D50 shown in Fig. 15 and Fig. 16, it was confirmed that even if the distance from the living body to be detected is large, the occurrence of an error between the estimated RRI and the true value is suppressed when signal information corresponding to a plurality of reflected waves R is used and a frequency filter is applied.

[0110] The above describes embodiments and modifications of the present invention, but the present invention is not necessarily limited to the above-described embodiments and modifications, and various modifications are possible without departing from the spirit of the present invention.

[0111] For example, the biological information detection system 1 may be a radar such as SIMO, MISO, or MIMO. The biological information detection system 1 may be, for example, a MIMO FMCW radar. The biological information detection system 1 may include a plurality of different types of radar. For example, when a MIMO FMCW radar is used instead of a Doppler sensor, the biological information detection system 1 detects biological information based on information acquired by the MIMO FMCW radar. In this case, the biological information detection system 1 acquires the information acquired by the MIMO FMCW radar in the received information acquisition unit 51 and processes it in the same way as in the case of a Doppler signal.

[0112] In the above-described embodiment, the signal transmitter 11 transmits multiple transmission waves T0, T1, T2, T3, and T4 that are inclined relative to each other in the vertical direction. However, the direction of the transmission waves transmitted by the signal transmitter 11 is not limited to the vertical direction. For example, the transmission waves transmitted from the signal transmitter 11 may include components that are inclined in the horizontal direction with respect to the transmission wave T0 toward the living body M. The horizontal direction is a direction perpendicular to the vertical direction. The horizontal direction is also a direction perpendicular to the propagation direction of the transmission wave T0. The signal controller 2 may perform beamforming in three-dimensional directions. Even in these cases, the signal receiver 12 may be configured to receive reflected waves R corresponding to each transmission wave. [Explanation of symbols]

[0113] 1...biological information detection system, 41...signal information acquisition unit, 43...peak group calculation unit, 44...peak group selection unit, 45...peak interval calculation unit, 46...filter setting unit, 48...biological information acquisition unit, B0, B1, B2, B3...signal information, GR...peak group, M...biological organism, P1 to P19...position, PE0 to PE3...temporary peak, R...reflected wave, W...time range.

Claims

1. a signal information acquiring unit that acquires a plurality of pieces of signal information, each of which corresponds to a reflected wave arriving from a different part of the living body and has a plurality of peaks in the time domain; a filter setting unit that sets a frequency filter based on positions of the plurality of peaks of the plurality of signal information in a time direction; A biometric information detection system comprising: a biometric information acquisition unit that acquires biometric information of the living body by performing a filtering process using the frequency filter on information based on at least one of the plurality of signal information.

2. a peak group calculation unit that sets a plurality of peak groups, each including at least one of the plurality of peaks, based on positions of the plurality of peaks of the plurality of signal information in the time direction, and calculates a position of each of the peak groups in the time direction; The biological information detection system according to claim 1 , wherein the filter setting unit sets the frequency filter based on the position in the time direction of each of the peak groups calculated by the peak group calculation unit.

3. a peak group selection unit that selects at least two peak groups from the plurality of peak groups as representative peak groups based on the positions of the respective peak groups in the time direction; a peak interval calculation unit that calculates a representative peak group interval that indicates an interval between the plurality of representative peak groups in the time direction, The biological information detection system according to claim 2 , wherein the filter setting unit sets the frequency filter based on the representative peak group interval calculated by the peak interval calculation unit.

4. the biological information is a periodically repeated movement of the living body, 4. The biological information detection system according to claim 3, wherein the peak group selection unit selects, from the plurality of peak groups, combinations of the peak groups that are located in the time direction at plausible intervals with respect to the period of the operation as the plurality of representative peak groups.

5. The biological information detection system according to claim 2 , wherein the filter setting unit sets the frequency filter to have a center frequency calculated based on positions in the time direction of the plurality of peak groups of the plurality of signal information.

6. the peak interval calculation unit calculates intervals between the plurality of representative peak groups; The biological information detection system according to claim 3 , wherein the filter setting unit sets the frequency filter so that a center frequency thereof is an inverse number of an average of intervals between the plurality of representative peak groups.

7. 7. The biological information detection system according to claim 2, wherein the peak group calculation unit identifies at least one peak from the plurality of peaks included in the plurality of signal information, the at least one peak being within a predetermined time range from each of the plurality of peaks included in reference signal information that serves as a reference among the plurality of signal information, and calculates a position of each of the peak groups in the time direction based on the identified peak.

8. 8. The biological information detection system according to claim 2, wherein the peak group calculation unit calculates, for each peak group, an average of positions in the time direction of the at least one peak included in the peak group as the position of the peak group in the time direction.

9. The biological information detection system according to claim 1 , wherein each of the signal information is information obtained by performing a short-time Fourier transform on information corresponding to the reflected wave and then spectrally integrating the information for each time segment.

10. The biometric information detection system according to claim 1 , wherein the information based on at least one of the plurality of signal information subjected to filtering processing in the biometric information acquisition unit is information combining at least two of the plurality of signal information.

11. 11. The biometric information detection system according to claim 1, wherein the information regarding at least one of the plurality of signal information subjected to filtering processing in the biometric information acquisition unit is based on the signal information corresponding to the reflected wave reflected at a position among the positions at which the plurality of reflected waves are reflected where the skin variation corresponding to the biometric information is greatest.

12. acquiring a plurality of pieces of signal information, each of which corresponds to a reflected wave arriving from a different part of the living body and has a plurality of peaks in the time domain; setting a frequency filter based on positions of the plurality of peaks in the time direction in the plurality of pieces of signal information; performing a filtering process using the frequency filter on information based on at least one of the plurality of pieces of signal information to obtain biometric information of the living body; A program that causes a computer to execute the following.

13. acquiring a plurality of pieces of signal information, each of which corresponds to a reflected wave arriving from a different part of the living body and has a plurality of peaks in the time domain; setting a frequency filter based on positions of the plurality of peaks in the time direction in the plurality of pieces of signal information; and performing a filtering process using the frequency filter on information based on at least one of the plurality of pieces of signal information, thereby acquiring biometric information of the living body.

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