Biological information acquisition device and biological information acquisition method

Abstract

To realize a biological information acquisition device and biological information acquisition method that can improve the acquisition accuracy of biological information.SOLUTION: A biological information acquisition device includes: non-contact type observation sensors (first observation sensor 2_1 and second observation sensor 2_2) that detect displacements of multiple portions of a biological information acquisition object; a reference sensor 3 that detects vibrations in the environment where at least the biological information acquisition object exists; and a signal processing unit 6 that generates a time-frequency mask from a reference signal acquired by the reference sensor 3 and generates a separation signal obtained by separating vibration components from observation signals acquired by the observation sensors (first observation sensor 2_1 and second observation sensor 2_2) on the basis of the time-frequency mask.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a biometric information acquiring device and a biometric information acquiring method. [Background technology]

[0002] For example, an occupant status detection system has been disclosed that uses a non-contact biosensor such as a radio wave sensor to detect occupants in the driver's seat, passenger seat, and rear seat of a vehicle, and that not only detects occupants but also acquires biosensor information in the driver's seat, etc. (for example, Patent Document 1). In the occupant status detection system of Patent Document 1, a radio wave sensor installed in a vehicle transmits radio waves, receives the reflected waves, and detects the distance from a reflecting object. The occupant status detection system checks whether the detected distance has changed by calculating a distance change from the distance from the reflecting object detected over time. As a result, if there is no distance change or the distance change is equal to or less than a certain detection value, it is determined that there is no person (occupant) in the vehicle. Also, if the distance changes or the distance change is equal to or more than a certain value, it is determined that there is a person in the vehicle. However, when vibrations of the radio wave sensor or vibrations of an object such as a person occur, the radio wave sensor signal output by the radio wave sensor contains a vibration component caused by the vibration. If the vibration component is contained in the radio wave sensor signal, the detection accuracy of the signal processing system may be reduced.

[0003] For example, a signal processing system and a sensor system have been disclosed that can improve the accuracy of detecting the state of an object by attenuating vibration components from a radio wave sensor signal (for example, Patent Document 2). The signal processing system of Patent Document 2 includes a first receiving unit, a second receiving unit, and a signal processing unit. The first receiving unit receives a radio wave sensor signal from a radio wave sensor that receives radio waves reflected by an object. The second receiving unit receives a vibration sensor signal from a vibration sensor that corresponds to vibrations of at least one of the radio wave sensor and the object. The signal processing unit detects information regarding the state of the object based on the radio wave sensor signal and the vibration sensor signal. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-202921 A [Patent Document 2] Patent Publication No. 2021-71326 Summary of the Invention [Problem to be solved by the invention]

[0005] It is conceivable that time-frequency masking, a signal processing technology targeted at the acoustic field, can be applied to the sensing field to separate unnecessary vibration components from observed signals and obtain bioinformation. Time-frequency masking can separate the desired signal components from the unnecessary vibration components with high accuracy, but it can cause artificial distortions specific to nonlinear processing, which can reduce the accuracy of obtaining bioinformation.

[0006] The present disclosure has been made in view of the above, and has an object to provide a biometric information acquiring device and a biometric information acquiring method that can improve the accuracy of acquiring biometric information. [Means for solving the problem]

[0007] A biometric information acquisition device according to one aspect of the present disclosure includes a non-contact observation sensor that detects displacement of multiple parts of a subject from which biometric information is acquired, a reference sensor that detects vibrations in an environment in which at least the subject from which biometric information is acquired is present, and a signal processing unit that generates a time-frequency mask from a reference signal acquired by the reference sensor, and generates a separated signal by separating vibration components from the observation signal acquired by the observation sensor based on the time-frequency mask.

[0008] In this configuration, linear (low distortion) source separation is possible by applying the time-frequency mask as a signal source model for blind source separation based on time-frequency masking, which can improve the accuracy of acquiring biometric information.

[0009] A biometric information acquisition method according to one aspect of the present disclosure includes an observation signal acquisition step of detecting displacements of multiple parts of a biometric information acquisition subject and acquiring an observation signal, a reference signal acquisition step of detecting vibrations in an environment in which at least the biometric information acquisition subject is present and acquiring a reference signal, a time-frequency mask generation step of generating a time-frequency mask from the reference signal, and a vibration component separation step of generating a separated signal by separating vibration components from the observation signal based on the time-frequency mask.

[0010] In this configuration, linear (low distortion) source separation is possible by applying the time-frequency mask as a signal source model for blind source separation based on time-frequency masking, which can improve the accuracy of acquiring biometric information. Effect of the Invention

[0011] According to the present disclosure, it is possible to realize a biometric information acquiring device and a biometric information acquiring method that can improve the accuracy of acquiring biometric information. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing a schematic configuration of a biometric information acquiring device according to the first embodiment. [Diagram 2] FIG. 2 is a flowchart illustrating an example of a biometric information acquisition process according to the first embodiment. [Diagram 3] FIG. 3 is a schematic diagram illustrating the concept of independent component analysis. [Figure 4] FIG. 4 is an algorithm showing a specific example of TFMBSS. [Figure 5A] FIG. 5A is a sub-flowchart illustrating an example of the time-frequency mask generation process. [Figure 5B] FIG. 5B is a sub-flowchart showing a first modified example of the time-frequency mask generation process. [Figure 5C] FIG. 5C is a sub-flowchart showing a second modified example of the time-frequency mask generation process. [Figure 5D]FIG. 5D is a sub-flowchart showing a third modified example of the time-frequency mask generation process. [Figure 6] FIG. 6 is a flowchart illustrating an example of a biological information acquisition process according to the second embodiment. [Figure 7] FIG. 7 is a block diagram showing a schematic configuration of a biometric information acquiring device according to the third embodiment. [Figure 8A] FIG. 8A is a diagram illustrating a first example of an antenna configuration of an observation sensor according to the third embodiment. [Figure 8B] FIG. 8B is a diagram showing a second example of the antenna configuration of the observation sensor according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, a biometric information acquiring device and a biometric information acquiring method according to an embodiment will be described in detail with reference to the drawings. Note that the present disclosure is not limited to the embodiment.

[0014] (Embodiment 1) Fig. 1 is a block diagram showing a schematic configuration of a biometric information acquisition device according to embodiment 1. As shown in Fig. 1, the biometric information acquisition device 1 includes a first observation sensor 2_1, a second observation sensor 2_2, a reference sensor 3, a first observation signal acquisition unit 4_1, a second observation signal acquisition unit 4_2, a reference signal acquisition unit 5, a signal processing unit 6, and a data storage unit 7. In the present disclosure, the biometric information acquisition device 1 is, for example, a driver monitoring system (DMS) installed in a vehicle.

[0015] The first observation sensor 2_1 and the second observation sensor 2_2 are so-called non-contact type biosensors that transmit transmission waves to different parts of the driver (hereinafter, simply referred to as the "subject") from whom bioinformation is to be acquired, and detect the displacement of each part based on the reflected waves of the transmission waves. The first observation sensor 2_1 and the second observation sensor 2_2 are provided, for example, on the rearview mirror or inside the seat of the vehicle in which the bioinformation acquisition device 1 is mounted.

[0016] For example, when the first observation sensor 2_1 and the second observation sensor 2_2 are provided inside the seat, one of the first observation sensor 2_1 and the second observation sensor 2_2 detects the displacement of a part corresponding to the subject's back, and the other detects the displacement of a part corresponding to the subject's buttocks. The parts where the first observation sensor 2_1 and the second observation sensor 2_2 detect the displacement may be, for example, parts about 5 cm apart from each other.

[0017] More specifically, the first observation sensor 2_1 and the second observation sensor 2_2 are, for example, radio wave sensors. The first observation sensor 2_1 and the second observation sensor 2_2 generate, for example, continuous wave (CW) electromagnetic waves as transmission waves and irradiate them toward the subject. For example, in a configuration in which the Doppler method is adopted as a modulation method of electromagnetic waves by the first observation sensor 2_1 and the second observation sensor 2_2, the first observation sensor 2_1 and the second observation sensor 2_2 output signals corresponding to the difference in frequency between the transmitted and received transmission waves and the reflected waves to the first observation signal acquisition unit 4_1 and the second observation signal acquisition unit 4_2. The electromagnetic waves may be, for example, millimeter waves or microwaves.

[0018] The first observation sensor 2_1 and the second observation sensor 2_2 are not limited to Doppler radar. The first observation sensor 2_1 and the second observation sensor 2_2 may be, for example, an FMCW (Frequency Modulated Continuous Wave) radar, an UWB (Ultra Wide Band) radar, a pulse radar, or the like. The first observation sensor 2_1 and the second observation sensor 2_2 are not limited to irradiating electromagnetic waves as transmission waves. The transmission waves handled by the first observation sensor 2_1 and the second observation sensor 2_2 may be any wave capable of measuring the displacement of a target person, and may include, for example, sound waves, light waves, and the like.

[0019] The first observation signal acquirer 4_1 outputs the signal acquired from the first observation sensor 2_1 to the signal processor 6 as a first observation signal.

[0020] The second observation signal acquirer 4_2 outputs the signal acquired from the second observation sensor 2_2 to the signal processor 6 as a second observation signal.

[0021] The reference sensor 3 is provided at a predetermined position in the vehicle to detect vibrations of the vehicle. Examples of the position where the reference sensor 3 is provided include on the dashboard, an airbag, and inside a seat of the vehicle in which the biometric information acquisition device 1 is mounted.

[0022] More specifically, the reference sensor 3 is, for example, an acceleration sensor. The reference sensor 3 outputs to the reference signal acquisition unit 5 a signal corresponding to the detected vibration.

[0023] The reference sensor 3 is not limited to a mode that detects vibrations at an installation location, such as an acceleration sensor. The reference sensor 3 may be, for example, a non-contact type sensor similar to the first observation sensor 2_1 and the second observation sensor 2_2. In this case, the reference sensor 3 transmits a transmission wave toward a predetermined position in the vehicle (for example, the dashboard of the vehicle in which the biometric information acquisition device 1 is mounted) and outputs a signal corresponding to the difference in frequency between the transmitted and received transmission wave and the reflected wave to the reference signal acquisition unit 5. The reference sensor 3 may be of any mode as long as it can detect vibrations in the environment in which a subject (biometric information acquisition subject) is present (specifically, for example, vibrations in the vehicle in which the biometric information acquisition device 1 is mounted).

[0024] The reference signal acquisition unit 5 outputs the signal acquired from the reference sensor 3 to the signal processing unit 6 as a reference signal.

[0025] The biometric information acquisition process described later is realized by a computer including a signal processing unit 6 exemplified by a central processing unit (CPU), a data storage unit 7 exemplified by a storage device such as a RAM for storing data, and a program memory for storing programs. In this case, the central processing unit (CPU) reads out a program stored in the program memory to realize the function of the signal processing unit 6.

[0026] Alternatively, the signal processing unit 6 can be realized by software control processing of a microcomputer, or by a hardware configuration of an electronic circuit, or by both the software control processing of a microcomputer and the hardware configuration of an electronic circuit. Specifically, the signal processing unit 6 may be configured as, for example, an IC (integrated circuit).

[0027] In this disclosure, the signal processing unit 6 applies blind source separation (BBS), which is a signal processing technique targeted at the acoustic field, and separates unnecessary vibration components from the observed signal using a time-frequency masking-based determined BSS (TFMBSS).

[0028] In BBS, at least the same number of observation signals as the number N of signal sources to be separated are required, whereas in TFMBSS, the number N of signal sources and the number of observation signals must be the same. In the present disclosure, the body surface displacement of the subject is defined as the first signal source, and the vibration component is defined as the second signal source. That is, in the present disclosure, the number N of signal sources is 2 (N=2), and at least two observation sensors are required. In other words, the number of observation sensors may be greater than the number N of signal sources. In this case, for example, it is possible to obtain the displacements of parts greater than the number N of signal sources by the observation sensors corresponding to each part, and perform principal component analysis of the signals obtained by each observation sensor to generate the same number of observation signals as the number N of signal sources.

[0029] FIG. 2 is a flowchart illustrating an example of a biometric information acquisition process according to the first embodiment.

[0030] In the biometric information acquisition process of embodiment 1 shown in FIG. 2, the signal processing unit 6 acquires a first observation signal, a second observation signal, and a reference signal for a predetermined period of time, converts them into digital signals, and sequentially stores the first observation signal, the second observation signal, and the reference signal for the predetermined period of time that have been converted into digital signals in the data storage unit 7 (step S100).

[0031] The signal processing unit 6 reads out the first observation signal, the second observation signal, and the reference signal stored in the data storage unit 7, and converts them into signals in the time-frequency domain. Specifically, the signal processing unit 6 performs a Short-Time Fourier Transform (STFT) on each signal to generate a complex spectrogram (step S200).

[0032] In the present disclosure, the number of frequency bins I and the number of time frames (hereinafter also simply referred to as the "number of frames") J of the complex spectrogram of each signal after STFT processing are assumed to be the same. Also, in the present disclosure, the number of channels M indicating the number of observed signals is 2 (M=2). The first observed signal acquired by the first observation sensor 2_1 can also be expressed as the "first channel signal". The second observed signal acquired by the second observation sensor 2_2 can also be expressed as the "second channel signal".

[0033] BBS is a technology that estimates pre-mixture separated signals from observed signals in which multiple signal sources are mixed. In BBS, for example, independent component analysis (ICA) is used as a method based on the assumption of statistical independence of signal sources. The separation accuracy in independent component analysis is characterized by the validity of the signal source model given as prior information. Figure 3 is a schematic diagram explaining the concept of independent component analysis.

[0034] Observation signals x acquired by the first observation sensor 2_1 and the second observation sensor 2_2 ij and the complex spectrogram X m is expressed by the following formula (1). In the following formula (1), i indicates a frequency index (i is an integer from 1 to I), j indicates a frame index (j is an integer from 1 to J), and m indicates a channel index (m is an integer from 1 to M). m The matrix element that indicates ijm It is said.

[0035]

number

[0036] Observed signal x ij The unknown signal source s contained in ij and the complex spectrogram S n is expressed by the following equation (2). In the following equation (2), n indicates the signal source index (n is an integer from 1 to N). The complex spectrogram S n The matrix element that indicates ijn It is said.

[0037]

number

[0038] separation signal y ij and the complex spectrogram Y n is expressed by the following equation (3). n The matrix element that indicates ijn It is said.

[0039]

number

[0040] Observed signal x ij is the number of unknown signal sources s ij The observed signal xij is treated as a mixed signal of the complex mixing matrix A i Using the above, the complex mixing matrix A can be expressed as the following equation (5). i is the steering vector a in (=(a in1 ,···,a inM ) T )

[0041]

number

[0042]

number

[0043] The number of channels M and the number of signal sources N are equal (M=N), and the complex mixing matrix A i When is a regular matrix, the separated signal y ij is the separation matrix W shown in the following equation (6). i (=A i -1 ), the separation matrix W can be expressed by the following equation (7). i is the isolated vector w in (=(w in1 ,···,w inM ) T )

[0044]

number

[0045]

number

[0046] The signal processing unit 6 calculates the separation matrix W i Fig. 4 shows an algorithm that illustrates a specific example of TFMBSS. In the algorithm shown in Fig. 4, the number of iterations in TFMBSS is K.

[0047] In the algorithm shown in Fig. 4, X is the complex spectrogram of the observed signal, X1,...,X M is a complex matrix composed of the elements of W1, W2, and is expressed by the following equations (8) to (11). In addition, w is the separation matrix W1, W2, and I is a complex vector obtained by vectorizing the above, and is expressed as a vector with I×N×M rows and 1 column, as shown in the following equation (12).

[0048]

number

[0049]

number

[0050]

number

[0051]

number

[0052]

number

[0053] χ shown in the above formula (10) i is the complex spectrogram X1, ,X M Each element x of ijm The matrix χ shown in the above formula (10) is a J-row, M-column matrix. i is repeated M times to obtain a block diagonal matrix operator (the above formula (9)), which is then further block diagonalized to obtain the complex matrix X (the above formula (8)).

[0054] The function M(z) shown in Fig. 4 indicates a time-frequency mask that separates biological information and vibration components using an intermediate variable z as an argument. The signal processing unit 6 generates the function M(z) indicating the time-frequency mask from the complex spectrogram of the reference signal (step S300). Fig. 5A is a sub-flowchart showing an example of the time-frequency mask generation process.

[0055] The complex spectrogram R of the reference signal acquired by the reference sensor 3 is expressed by the following equation (13). The elements of the matrix representing the complex spectrogram R are r ij It is said.

[0056]

number

[0057] The signal processing unit 6 calculates each element r of the complex spectrogram R of the reference signal. ij is subjected to absolute value processing (step S321), and each element |r ij (step S324), and generates a matrix indicating the complex spectrogram after the standardization as a vibration component mask (step S326). ij The maximum value rmax of | is extracted, and each element |r of the complex spectrogram after absolute value processing is ij Divide each element m of the complex spectrogram that represents the vibration component mask. ij can be expressed by the following equation (14): A complex spectrogram M2 indicating the vibration component mask is expressed by the following equation (15).

[0058]

number

[0059]

number

[0060] Next, the signal processing unit 6 calculates each element m of the complex spectrogram of the vibration component mask. ij (step S327), and generates a matrix indicating the complex spectrogram after the inversion as a displacement component mask (step S328). ij is subtracted from 1. The complex spectrogram M1 indicating the displacement component mask is expressed by the following equation (16).

[0061]

number

[0062] Then, the signal processing unit 6 generates a function M(z) representing a time-frequency mask composed of each element of the complex spectrogram M1 of the displacement component mask and the complex spectrogram M2 of the vibration component mask (step S329), and applies the function M(z) representing the time-frequency mask to the algorithm shown in Fig. 4 to execute the iterative process in TFMBSS (step S400, Fig. 2). The function M(z) representing the time-frequency mask is expressed by a vector of I x J x M rows and 1 column, as shown in the following formula (17).

[0063]

number

[0064] TFMBSS applies the primal-dual splitting method, which is one of the neighboring separation methods, as an optimization algorithm, and further makes it possible to use a time-frequency mask as a signal source model to promote separation using the intermediate variable z as an argument.

[0065] In this disclosure, a time-frequency mask is generated from a reference signal acquired simultaneously with the observed signal, and a function M(z) representing the time-frequency mask is used as a signal source model in TFMBSS, which enables linear (low-distortion) signal source separation.

[0066] By the iterative process in TFMBSS (step S400, FIG. 2), a vector y of I×J×M rows and 1 column shown in the following equation (18) is obtained.

[0067]

number

[0068] Each element y of vector y shown in the above formula (18) ij1 indicates each element of a complex spectrogram indicating a separated signal in the time-frequency domain in which at least the vibration component contained in the reference signal is separated from the observed signal.

[0069] In the time-frequency mask shown in the above formula (17), the element m of the complex spectrogram M1 of the displacement component mask ij1 and element m of the complex spectrogram M2 of the vibration component mask ij2 The function M(z) representing the time-frequency mask when and are replaced is expressed by the following (19). In this case, each element y of the vector y shown in the above formula (18) ij2 represents each element of a complex spectrogram indicating a separated signal in the time-frequency domain in which at least the vibration component contained in the reference signal is separated from the observed signal.

[0070]

number

[0071] Next, the signal processing unit 6 extracts the separated signals in the time-frequency domain after the TFMBSS processing, in other words, each element y of the vector y shown in the above equation (18) ij1 The separated signals shown by the complex spectrogram including ij2 (separated signals shown as complex spectrograms including the above-mentioned components) are transformed into signals in the time domain. Specifically, the signal processing unit 6 performs an inverse short-time Fourier transform (ISTFT) on the separated signals in the time-frequency domain after the TFMBSS processing to generate separated signals in the time domain (step S500).

[0072] Then, the signal processing unit 6 acquires vital signs of the subject, such as heart rate, heart rate variability, respiratory rate, and respiratory depth, as biological information from the separated signals in the time domain after the ISTFT process (step S600). A detailed description of a method for acquiring the vital signs is omitted here. The present disclosure is not limited by the method for acquiring the vital signs.

[0073] (First Modification) 5B is a sub-flowchart showing a first modified example of the time-frequency mask generation process. In the first modified example, the signal processing unit 6 calculates each element |r ij | frequency characteristic correction processing is executed (step S322).

[0074]

number

[0075] Specifically, in the first modification, the signal processing unit 6 calculates each element |r ij |Correction coefficient c i Multiply by.

[0076] The above equation (20) shows each element of the complex spectrogram after frequency characteristic correction processing, and the correction coefficient c i A column vector (c1, ,c I ) T and the I-row, J-column complex spectrogram after absolute value processing. The correction coefficient c i can be estimated by solving the optimization problem shown in the following equation (21).

[0077]

number

[0078] The solution to the optimization problem expressed by the above formula (21) is derived by the following formula (22). * " means complex conjugate.

[0079]

number

[0080] Correction factor c i can be expressed as the optimal solution in which the differential value of the function J shown in the above equation (22) becomes 0, by the following equation (23).

[0081]

number

[0082] The observation signal and the reference signal acquired at the same time in the bioinformation acquisition process shown in FIG. 2 contain the same vibration components. By applying each element of the complex spectrogram of the observation signal and the reference signal acquired in this bioinformation acquisition process to the above formula (23), the correction coefficient c i can be calculated.

[0083] When the sensor that acquires the observation signal and the sensor that acquires the reference signal are different sensors, it is assumed that the frequency characteristics of the signals acquired by each sensor are different. Specifically, when the first observation sensor 2_1 and the second observation sensor 2_2 are radio wave sensors and the reference sensor 3 is an acceleration sensor, it is assumed that the reference signal acquired by the acceleration sensor has a higher-frequency characteristic than the observation signal acquired by the radio wave sensor.

[0084] In the first modification, a correction coefficient is used to compensate for the difference in frequency characteristics due to the difference in sensors. This reduces the difference in characteristics between the observed signal and the reference signal due to the difference in sensors, and improves the accuracy of signal separation by the subsequent TFMBSS processing.

[0085] In addition, the correction coefficient c shown in the above formula (20) i may be a mode that is preset based on the frequency characteristic difference between the sensors, or may be a mode that is acquired before the execution of the biometric information acquisition process and stored in the data storage unit 7, and is read out from the data storage unit 7 and applied when the frequency characteristic correction process is executed in the biometric information acquisition process.

[0086] (Second Modification) 5C is a sub-flowchart showing a second modified example of the time-frequency mask generation process. In the second modified example, the signal processing unit 6 calculates each element |rij | threshold processing is performed (step S323).

[0087]

number

[0088] Specifically, in the second modification, the signal processing unit 6 calculates each element |r ij | is compared with the threshold rth, and elements |r ij | is replaced with 0. In the present disclosure, the threshold value rth is stored in the data storage unit 7 in advance.

[0089] The above equation (24) represents each element of the complex spectrogram after threshold processing.

[0090] The reference signal may contain a body surface displacement component of the subject depending on the installation position of the reference sensor 3. Specifically, for example, the reference sensor 3 may be installed inside a seat in which the subject (e.g., the driver) sits.

[0091] In the second modification, the above-mentioned threshold processing is used to remove elements that do not meet the threshold rth. This suppresses the body surface displacement components of the subject contained in the reference signal, thereby improving the accuracy of signal separation by the subsequent TFMBSS processing.

[0092] The threshold processing may be performed on each element of a matrix indicating a complex spectrogram after normalization processing. Furthermore, the threshold processing may be used in combination with the frequency characteristic correction processing (step S322) of the first modified example.

[0093] (Third Modification) 5D is a sub-flowchart showing a third modified example of the time-frequency mask generation process. In the third modified example, the signal processing unit 6 calculates each element m of a matrix indicating a complex spectrogram after normalization processing by using the following equation (25): ij Then, the power process is performed (step S325).

[0094] [Number]

[0095] Specifically, in the third modification example, the range in which the power value e can be taken is 0 < e < 1. In the present disclosure, the power value e is stored in the data storage unit 7 in advance.

[0096] The above equation (25) shows each element of the complex spectrogram after the power processing.

[0097] By the power processing, each element of the matrix showing the complex spectrogram becomes a value approaching 1. Thereby, minute vibration components included in the reference signal after the normalization processing are emphasized, and the signal separation accuracy by the subsequent TFMBSS processing can be improved.

[0098] Note that the power processing may be used in combination with the frequency characteristic correction processing (step S322) of the first modification example and the threshold processing (step S323) of the second modification example.

[0099] (Embodiment 2) FIG. 6 is a flowchart showing an example of the biological information acquisition process according to Embodiment 2. Since the configuration of the biological information acquisition device according to Embodiment 2 is the same as the configuration of the biological information acquisition device according to Embodiment 1, detailed description thereof is omitted here. Further, in Embodiment 2, the processes different from the biological information acquisition process according to Embodiment 1 will be described in detail, and the processes similar to the biological information acquisition process according to Embodiment 1 may be omitted from the description.

[0100] In the biological information acquisition process according to Embodiment 2, the signal processing unit 6 executes filtering processing on the first observation signal, the second observation signal, and the reference signal before the STFT processing (step S110).

[0101] The filtering process is realized by digital filtering using, for example, an infinite impulse response (IIR) filter, a finite impulse response (FIR) filter, etc. The number of taps (filter order), coefficient values, etc. in the filtering process are stored in advance in the data storage unit 7.

[0102] For example, when the subject's heart rate is the target of biometric information acquisition, the displacement component due to breathing, which is a relatively large body surface displacement contained in the observed signal, may mask the displacement component due to the heart rate, reducing the acquisition accuracy.

[0103] In the second embodiment, the above-mentioned filtering process removes components of less than 1 Hz, for example. This suppresses displacement components due to breathing (for example, components around 0.2 Hz) and improves the accuracy of acquiring displacement components due to heartbeats (for example, components of 1 Hz or higher).

[0104] In addition, the filtering process may be used in combination with the frequency characteristic correction process (step S322) of the first modified example of the time-frequency mask generation process in embodiment 1, the threshold process (step S323) of the second modified example, and the power process (step S325) of the third modified example.

[0105] (Embodiment 3) 7 is a block diagram showing a schematic configuration of a biometric information acquisition device according to embodiment 3. In embodiment 3, components different from those of the biometric information acquisition devices according to embodiments 1 and 2 will be described in detail, and descriptions of components similar to those of the biometric information acquisition devices according to embodiments 1 and 2 may be omitted. In addition, since the biometric information acquisition process according to embodiment 3 is similar to the biometric information acquisition process according to embodiments 1 and 2, detailed descriptions will be omitted here.

[0106] The biometric information acquisition device 1a of embodiment 3 is equipped with an observation sensor 2 instead of the first observation sensor 2_1 and the second observation sensor 2_2 of the biometric information acquisition device 1, and is equipped with an observation signal acquisition unit 4 instead of the first observation signal acquisition unit 4_1 and the second observation signal acquisition unit 4_2.

[0107] The observation sensor 2 is, for example, a radio wave sensor. Fig. 8A is a diagram showing a first example of an antenna configuration of the observation sensor according to the embodiment 3. Fig. 8B is a diagram showing a second example of an antenna configuration of the observation sensor according to the embodiment 3.

[0108] In the first example of the antenna configuration shown in Fig. 8A, the observation sensor 2 has at least two transmitting antennas Tx1 and Tx2 and one receiving antenna Rx. In the second example of the antenna configuration shown in Fig. 8B, the observation sensor 2 has at least one transmitting antenna Tx and two receiving antennas Rx1 and Rx2.

[0109] The antenna configuration of the observation sensor 2 is not limited to the first example shown in Fig. 8A and the second example shown in Fig. 8B. For example, the first example configuration shown in Fig. 8A may have a configuration including multiple transmitting antennas Tx1, Tx2 and multiple receiving antennas Rx, and the second example configuration shown in Fig. 8B may have a configuration including multiple transmitting antennas Tx and multiple receiving antennas Rx1, Rx2.

[0110] The biometric information acquiring device 1a according to the third embodiment is configured to acquire first and second observation signals at different parts of a subject using one observation sensor 2 by using techniques such as antenna diversity, analog beam forming, and digital beam forming. Even in the third embodiment, it is possible to acquire displacements of parts that are greater than the number N of signal sources using one observation sensor, and generate the same number of observation signals as the number N of signal sources by performing principal component analysis of the signals corresponding to each part.

[0111] The above-described embodiment is for facilitating understanding of the present disclosure, and is not intended to limit the present disclosure. The present disclosure may be modified or improved without departing from the spirit of the present disclosure, and equivalents thereof are also included in the present disclosure.

[0112] The present disclosure can have the following configuration as described above or instead of the above.

[0113] (1) A biometric information acquisition device according to one aspect of the present disclosure includes a non-contact observation sensor that detects displacement of multiple parts of a subject from which biometric information is acquired, a reference sensor that detects vibrations in an environment in which at least the subject from which biometric information is acquired is present, and a signal processing unit that generates a time-frequency mask from a reference signal acquired by the reference sensor and generates a separated signal by separating vibration components from the observation signal acquired by the observation sensor based on the time-frequency mask.

[0114] (2) In the biometric information acquisition device of (1) above, the signal processing unit applies the time-frequency mask as a signal source model for blind source separation based on time-frequency masking.

[0115] This configuration enables linear (low-distortion) signal source separation, improving the accuracy of acquiring biological information.

[0116] (3) In the biometric information acquisition device of (2) above, the signal processing unit converts the time-domain observation signal acquired by the observation sensor and the time-domain reference signal acquired by the reference sensor into time-frequency domain signals, generates the time-frequency mask from the time-frequency domain reference signal, and separates vibration components from the time-frequency domain observation signal to generate the separated signal.

[0117] (4) In the biometric information acquisition device described above in (3), the signal processing unit performs absolute value processing and normalization processing on each element of a reference signal in the time-frequency domain to generate a vibration component mask, performs inversion processing on each element of the vibration component mask to generate a displacement component mask, and generates, as the time-frequency mask, a function composed of each element of the vibration component mask and the displacement component mask.

[0118] (5) In the biometric information acquiring device according to (3) or (4) above, the signal processing unit executes a frequency characteristic correction process for a reference signal in the time-frequency domain.

[0119] This configuration reduces the difference in characteristics between the observed signal and the reference signal due to differences in sensors, thereby improving the accuracy of signal separation by the subsequent TFMBSS processing.

[0120] (6) In the biometric information acquiring device according to any one of (3) to (5) above, the signal processing unit performs a predetermined threshold process on each element of the reference signal in the time-frequency domain.

[0121] This configuration suppresses the body surface displacement component of the target biological information acquisition target contained in the reference signal, thereby improving the accuracy of signal separation by the subsequent TFMBSS processing.

[0122] (7) In the biometric information acquiring device according to any one of (3) to (6) above, the signal processing unit performs a predetermined power process on each element of the time-frequency domain reference signal after normalization.

[0123] This configuration emphasizes the small vibration components contained in the reference signal, thereby improving the accuracy of signal separation by the subsequent TFMBSS processing.

[0124] (8) In the biometric information acquisition device of (3) to (7) above, the signal processing unit performs a predetermined filtering process on a time-domain observation signal acquired by the observation sensor and a time-domain reference signal acquired by the reference sensor.

[0125] In this configuration, the accuracy of acquiring desired biometric information can be improved.

[0126] (9) In the biometric information acquisition device according to any one of (3) to (8) above, the signal processing unit converts the separated signals in the time-frequency domain into signals in the time domain, and acquires the biometric information of the biometric information acquisition target from the separated signals in the time domain.

[0127] (10) In the biometric information acquisition device of (1) to (9) above, the observation sensor includes a first observation sensor that detects a displacement of a first part of the biometric information acquisition target, and a second observation sensor that detects a displacement of a second part of the biometric information acquisition target that is different from the first part.

[0128] (11) In the biometric information acquisition device of (1) to (9) above, the observation sensor has at least two transmitting antennas and one receiving antenna, and detects displacement of a first part of the biometric information acquisition target and displacement of a second part of the biometric information acquisition target, which is different from the first part, using any one of antenna diversity, analog beamforming, and digital beamforming.

[0129] (12) In the biometric information acquisition device of (1) to (9) above, the observation sensor has at least one transmitting antenna and two receiving antennas, and detects displacement of a first part of the biometric information acquisition target and displacement of a second part of the biometric information acquisition target, the second part being different from the first part, using any one of antenna diversity, analog beamforming, and digital beamforming.

[0130] (13) A biometric information acquisition method according to one aspect of the present disclosure includes a displacement detection step of detecting displacement of multiple parts of a biometric information acquisition target, a vibration detection step of detecting vibrations in an environment in which at least the biometric information acquisition target is present, a time-frequency mask generation step of generating a time-frequency mask from a reference signal acquired in the vibration detection step, and a separation signal generation step of generating a separation signal by separating a vibration component from an observation signal acquired in the displacement detection step based on the time-frequency mask.

[0131] (14) In the biometric information acquisition method described above in (13), in the separated signal generation step, the time-frequency mask is applied as a signal source model for blind source separation based on time-frequency masking.

[0132] This configuration enables linear (low-distortion) signal source separation, improving the accuracy of acquiring biological information.

[0133] (15) The bioinformation acquisition method of (14) above further includes a time-frequency domain signal generating step of converting the observation signal acquired in the displacement detection step and the reference signal acquired in the vibration detection step into signals in the time-frequency domain, wherein in the time-frequency mask generating step, a time-frequency mask is generated from the reference signal in the time-frequency domain, and in the separated signal generating step, the vibration component is separated from the observation signal in the time-frequency domain to generate the separated signal.

[0134] (16) In the biometric information acquisition method of (15) above, the time-frequency mask generation step includes a vibration component mask generation step of generating a vibration component mask by performing absolute value processing and normalization processing on each element of a reference signal in the time-frequency domain, a displacement component mask generation step of generating a displacement component mask by performing inversion processing on each element of the vibration component mask, and a function generation step of generating, as the time-frequency mask, a function composed of the vibration component mask and each element of the displacement component mask.

[0135] (17) In the biometric information acquisition method according to (15) or (16) above, in the time-frequency mask generation step, a frequency characteristic correction process is performed on a reference signal in the time-frequency domain.

[0136] In this configuration, the difference in characteristics between the observed signal and the reference signal due to the difference in the sensors is suppressed, thereby improving the accuracy of signal separation in the vibration component separation step.

[0137] (18) In the biometric information acquisition method according to any one of (15) to (17) above, in the time-frequency mask generation step, a predetermined threshold process is performed on each element of the reference signal in the time-frequency domain.

[0138] In this configuration, the body surface displacement component of the subject of biological information acquisition, which is included in the reference signal, is suppressed, thereby improving the accuracy of signal separation in the vibration component separation step.

[0139] (19) In the biometric information acquisition method according to any one of (15) to (18) above, in the time-frequency mask generation step, a predetermined power process is performed on each element of the reference signal in the time-frequency domain after normalization.

[0140] In this configuration, minute vibration components contained in the reference signal are emphasized, which can improve the accuracy of signal separation in the vibration component separation step.

[0141] (20) In the bioinformation acquisition method according to any one of (15) to (19) above, a predetermined filtering process is performed on the time-domain observation signal acquired in the displacement detection step and the time-domain reference signal acquired in the vibration detection step.

[0142] In this configuration, the accuracy of acquiring desired biometric information can be improved.

[0143] (21) The biometric information acquisition method according to any one of (13) to (20) above further includes a time domain signal generating step of converting a separated signal in the time-frequency domain into a time domain signal, and a biometric information acquiring step of acquiring biometric information of the subject from the separated signal in the time domain.

[0144] According to the present disclosure, it is possible to realize a biometric information acquiring device and a biometric information acquiring method that can improve the accuracy of acquiring biometric information. [Explanation of symbols]

[0145] 1,1a Biometric information acquisition device 2. Observation Sensor 2_1 First observation sensor 2_2 Second Observation Sensor 3 Reference Sensor 4. Observation signal acquisition section 4_1 First observation signal acquisition section 4_2 Second observation signal acquisition section 5 Reference signal acquisition section 6 Signal Processing Section 7 Data storage section

Claims

1. A non-contact observation sensor that detects displacements of multiple parts of a subject from which biometric information is to be acquired; A reference sensor that detects vibrations in an environment in which at least the biometric information acquisition subject exists; a signal processing unit that generates a time-frequency mask from a reference signal acquired by the reference sensor, and generates a separated signal by separating a vibration component from an observation signal acquired by the observation sensor based on the time-frequency mask; Equipped with Biometric information acquisition device.

2. The biometric information acquisition device according to claim 1 , The signal processing unit includes: applying the time-frequency mask as a source model for time-frequency masking based blind source separation; Biometric information acquisition device.

3. The biometric information acquisition device according to claim 2, The signal processing unit includes: converting a time-domain observation signal acquired by the observation sensor and a time-domain reference signal acquired by the reference sensor into signals in a time-frequency domain; generating the time-frequency mask from a time-frequency domain reference signal; Separating a vibration component from an observation signal in a time-frequency domain to generate the separated signal; Biometric information acquisition device.

4. The biometric information acquisition device according to claim 3, The signal processing unit includes: performing absolute value processing and normalization processing on each element of the time-frequency domain reference signal to generate a vibration component mask; Inverting each element of the vibration component mask to generate a displacement component mask; generating a function configured with each element of the displacement component mask and the vibration component mask as the time-frequency mask; Biometric information acquisition device.

5. The biometric information acquisition device according to claim 3, The signal processing unit performs a frequency characteristic correction process for a reference signal in the time-frequency domain. Biometric information acquisition device.

6. The biometric information acquisition device according to claim 3, The signal processing unit performs a predetermined threshold process on each element of the reference signal in the time-frequency domain. Biometric information acquisition device.

7. The biometric information acquisition device according to claim 3, The signal processing unit performs a predetermined power process on each element of the time-frequency domain reference signal after the normalization process. Biometric information acquisition device.

8. The biometric information acquisition device according to claim 3, The signal processing unit performs a predetermined filtering process on the time-domain observation signal acquired by the observation sensor and the time-domain reference signal acquired by the reference sensor. Biometric information acquisition device.

9. The biometric information acquisition device according to claim 3, The signal processing unit includes: Transforming the separated signals in the time-frequency domain into signals in the time domain; acquiring biometric information of the biometric information acquisition target from the separated signals in the time domain; Biometric information acquisition device.

10. The biometric information acquisition device according to any one of claims 1 to 9, The observation sensor includes: a first observation sensor that detects a displacement of a first portion of the biological information acquisition target; a second observation sensor configured to detect a displacement of a second portion different from the first portion of the biological information acquisition target; Including, Biometric information acquisition device.

11. The biometric information acquisition device according to any one of claims 1 to 9, The observation sensor at least two transmit antennas and one receive antenna, Detecting a displacement of a first portion of the biometric information acquisition target and a displacement of a second portion of the biometric information acquisition target, the second portion being different from the first portion, using any one of antenna diversity, analog beam forming, and digital beam forming; Biometric information acquisition device.

12. The biometric information acquisition device according to any one of claims 1 to 9, The observation sensor At least one transmitting antenna and two receiving antennas, Detecting a displacement of a first portion of the biometric information acquisition target and a displacement of a second portion of the biometric information acquisition target, the second portion being different from the first portion, using any one of antenna diversity, analog beam forming, and digital beam forming; Biometric information acquisition device.

13. a displacement detection step of detecting displacements of a plurality of parts of a biometric information acquisition target; a vibration detection step of detecting vibration in an environment in which at least the biometric information acquisition subject exists; a time-frequency mask generating step of generating a time-frequency mask from the reference signal acquired in the vibration detecting step; a separation signal generating step of generating a separation signal by separating a vibration component from the observation signal acquired in the displacement detecting step based on the time-frequency mask; having Method of acquiring biometric information.

14. The biometric information acquisition method according to claim 13, applying the time-frequency mask as a signal source model for blind source separation based on time-frequency masking in the separated signal generating step; Method of acquiring biometric information.

15. The biometric information acquisition method according to claim 14, a time-frequency domain signal generating step of converting the observation signal acquired in the displacement detection step and the reference signal acquired in the vibration detection step into a time-frequency domain signal; In the time-frequency mask generating step, a time-frequency mask is generated from a reference signal in a time-frequency domain; In the separated signal generating step, a vibration component is separated from an observation signal in a time-frequency domain to generate the separated signal. Method of acquiring biometric information.

16. The biometric information acquisition method according to claim 15, The time-frequency mask generating step includes: a vibration component mask generating step of performing absolute value processing and normalization processing on each element of the reference signal in the time-frequency domain to generate a vibration component mask; a displacement component mask generating step of generating a displacement component mask by performing an inversion process on each element of the vibration component mask; a function generating step of generating, as the time-frequency mask, a function constituted by each element of the displacement component mask and the vibration component mask; Including, Method of acquiring biometric information.

17. The biometric information acquisition method according to claim 15, In the time-frequency mask generation step, a frequency characteristic correction process is performed on the reference signal in the time-frequency domain. Method of acquiring biometric information.

18. The biometric information acquisition method according to claim 15, In the time-frequency mask generation step, a predetermined threshold process is performed on each element of the reference signal in the time-frequency domain. Method of acquiring biometric information.

19. The biometric information acquisition method according to claim 15, In the time-frequency mask generation step, a predetermined power process is performed on each element of the time-frequency domain reference signal after the normalization process. Method of acquiring biometric information.

20. The biometric information acquisition method according to claim 15, performing a predetermined filtering process on the time-domain observation signal acquired in the displacement detection step and the time-domain reference signal acquired in the vibration detection step; Method of acquiring biometric information.

21. The biometric information acquisition method according to any one of claims 13 to 20, A time domain signal generation step of converting the separated signals in the time-frequency domain into a signal in the time domain; a biometric information acquisition step of acquiring biometric information of the biometric information acquisition subject from a separated signal in a time domain; Further comprising Method of acquiring biometric information.

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