Biological signal sensor module

Abstract

A biological signal sensor module comprising: sensors that each have a piezoelectric film and output a signal corresponding to deformation of a skin surface; a detection circuit that, by amplifying the signals outputted from the sensors, outputs a first signal including a biological signal and a body motion signal, and a second signal including the body motion signal; a biological signal estimation unit that uses a preset biological signal estimation algorithm to estimate the biological signal on the basis of the first signal and the second signal, and outputs the estimated biological signal; and a biological signal determination unit that calculates a feature quantity of the estimated biological signal, estimates a determination coefficient on the basis of the feature quantity of the estimated biological signal, and compares the determination coefficient and a preset threshold value to determine the validity of the estimated biological signal.

Description

Biosignal Sensor Module

[0001] The present invention relates to a biosignal sensor module for measuring a biosignal.

[0002] Patent Document 1 discloses a pulse detection device that detects a pulse signal derived from the pulse of a human body. The pulse detection device includes a first sensor, a second sensor, and a filtering unit. The first sensor outputs a signal in which a pulse signal and a noise signal are mixed. The second sensor outputs a body movement signal associated with the body movement of the human body. The filtering unit generates an adaptive filter based on the body movement signal to extract the noise signal from the mixed signal of the pulse signal and the noise signal, and to remove the noise signal from the mixed signal of the pulse signal and the noise signal.

[0003] JP 2010-172645 A

[0004] The pulse detection device disclosed in Patent Document 1 employs an acceleration sensor as the second sensor that outputs a body movement signal. However, while the acceleration sensor can measure the movement of the area where it is attached, it cannot accurately measure the deformation of the skin surface, which is the target of pulse wave measurement. Therefore, if the noise signal is removed from a signal that contains a mixture of a pulse signal and a noise signal based on the body movement signal from the acceleration sensor, pulse measurement is possible, but the accuracy of the pulse wave measurement may be reduced.

[0005] Furthermore, this pulsation detection device is not intended to be used to measure biological signals other than pulsation signals.

[0006] An object of the present invention is to provide a biosignal sensor module that can measure biosignals with high accuracy even when there is body movement.

[0007] The biosignal sensor module of the present invention comprises a sensor having a piezoelectric film and outputting a signal corresponding to deformation of the skin surface; a detection circuit that amplifies the signal output from the sensor to output a first signal including a biosignal and a body movement signal, and a second signal including the body movement signal; a biosignal estimation unit that estimates the biosignal based on the first signal and the second signal using a preset biosignal estimation algorithm and outputs an estimated biosignal; and a biosignal judgment unit that calculates features of the estimated biosignal, estimates a judgment coefficient based on the features of the estimated biosignal, and compares the judgment coefficient with a preset threshold to judge the validity of the estimated biosignal.

[0008] According to the present invention, a biological signal can be measured with high accuracy even when there is body movement.

[0009] FIG. 1 is a functional block diagram showing a biosignal sensor module according to an embodiment. FIG. 2A is an external perspective view showing a biosignal sensor. FIG. 2B is a cross-sectional view showing the biosignal sensor. FIG. 3 is a plan view showing a piezoelectric film. FIG. 4 is a conceptual diagram showing the arrangement of a biosignal sensor and a body movement sensor when using a biosignal sensor module. FIG. 5 is a diagram showing an example of a mixed signal, a body movement signal, and a pulse wave signal used to set a biosignal estimation algorithm. FIG. 6 is a diagram showing an example of a mixed signal and an estimated pulse wave signal. FIG. 7 is a flowchart showing an example of a process performed by a biosignal determination unit to determine the validity of an estimated pulse wave signal. FIGS. 8A and 8B are diagrams showing an example of an estimated pulse wave signal. FIG. 9 is a functional block diagram showing a biosignal sensor module according to a second modification. FIGS. 10A and 10B are conceptual diagrams showing an example of a time series of bioinformation. FIG. 11 is a boxplot showing the distribution of errors between a calculated pulse rate and an actual pulse rate.

[0010] Hereinafter, several embodiments of the present invention will be described. In each embodiment, differences from the previous embodiments will be described. Similar effects due to similar configurations will not be mentioned for each embodiment.

[0011] [Embodiment] FIG. 1 is a functional block diagram showing a biosignal sensor module according to an embodiment. The biosignal sensor module estimates a biosignal corresponding to a pulse wave, i.e., a pulse wave signal. The biosignal sensor module includes a biosignal sensor 11, a body motion sensor 12, a limiter circuit 13, a low-gain circuit 14, and a processing unit 15. The processing unit 15 includes a biosignal estimation unit 16 and a biosignal determination unit 17. The biosignal sensor 11 and the body motion sensor 12 each have a piezoelectric film and output a signal corresponding to deformation of the skin surface. The limiter circuit 13 amplifies the signal output from the biosignal sensor 11 to output a mixed signal (first signal) including a pulse wave signal and a body motion signal. The low-gain circuit 14 amplifies the signal output from the body motion sensor 12 to output a body motion signal (second signal). The limiter circuit 13 and the low-gain circuit 14 form a detection circuit that outputs the mixed signal and the body motion signal. The biological signal estimation unit 16 estimates a pulse wave signal based on the mixed signal and the body movement signal using a preset biological signal estimation algorithm, and outputs an estimated pulse wave signal (estimated biological signal). The biological signal determination unit 17 calculates feature quantities of the estimated pulse wave signal, estimates a determination coefficient based on the feature quantities of the estimated pulse wave signal, and compares the determination coefficient with a preset threshold value to determine the validity of the estimated pulse wave signal.

[0012] The limiter circuit 13 adjusts the gain so as to appropriately amplify small sensor outputs corresponding to small skin surface displacements, such as those caused by pulse waves. On the other hand, the limiter circuit 13 lowers the gain for relatively large sensor outputs corresponding to relatively large skin surface displacements, such as those caused by body movement. This allows the limiter circuit 13 to output a signal that can be processed by the biological signal estimation unit 16 without saturating its output. In this way, the limiter circuit 13 detects deformations of the skin surface caused by pulse waves and body movement and outputs a mixed signal.

[0013] The low-gain circuit 14 has a relatively low gain, and the gain is adjusted so as to appropriately amplify a relatively large sensor output corresponding to a relatively large skin surface displacement, such as a skin surface displacement due to body movement. In this way, the low-gain circuit 14 does not saturate its output, detects deformation of the skin surface due to body movement, and outputs a body movement signal.

[0014] The processing unit 15 is composed of a CPU, a memory, etc., stores a program, and executes the program to perform predetermined processing.

[0015] Fig. 2(A) is a perspective view showing the appearance of the biosignal sensor. Fig. 2(B) is a cross-sectional view showing the biosignal sensor. As shown in Fig. 2(A), the biosignal sensor 11 is a film type having an elongated shape. As shown in Fig. 2(B), the biosignal sensor 11 includes a piezoelectric film 21, adhesive materials 22 and 23, a signal electrode 24, a reference electrode 25, and flexible substrates 26 and 27.

[0016] The signal electrode 24 is formed on a flexible substrate 26 and attached to one main surface of the piezoelectric film 21 by an adhesive material 22. The reference electrode 25 is formed on a flexible substrate 27 and attached to the other main surface of the piezoelectric film 21 by an adhesive material 23. The signal electrode 24 is disposed so as to face the reference electrode 25 with the piezoelectric film 21 interposed therebetween.

[0017] The biosignal sensor module further includes an adhesive material 31 disposed on the main surface of the biosignal sensor 11 .

[0018] 3 is a plan view showing a piezoelectric film. The piezoelectric film 21 is made of uniaxially stretched poly-L-lactic acid (PLLA). The stretching direction of the PLLA in the piezoelectric film 21 forms an angle of 45° with respect to the longitudinal direction of the piezoelectric film 21, and therefore the longitudinal direction of the biosignal sensor 11. This allows deformation in the thickness direction of the biosignal sensor 11 to be detected with high sensitivity.

[0019] In a variant, the piezoelectric film 21 may be formed from another piezoelectric material instead of PLLA.

[0020] 1, 2(A), and 2(B), the body motion sensor 12 has the same structure as the biosignal sensor 11, and the adhesive material 31 is disposed in the same manner as the biosignal sensor 11. Therefore, illustration and description of the structure of the body motion sensor 12 will be omitted.

[0021] In a modified embodiment, the biosignal sensor 11 and the body motion sensor 12 may have other shapes instead of an elongated shape. Also, the biosignal sensor 11 and the body motion sensor 12 may have different shapes or sizes.

[0022] Figure 4 is a conceptual diagram showing the arrangement of the biosignal sensor and the body motion sensor when using the biosignal sensor module. With reference to Figures 1, 2(A), 2(B), and 4, when using the biosignal sensor module, the biosignal sensor 11 and the body motion sensor 12 are positioned close to each other and attached to the skin surface with an adhesive material 31. When the skin surface deforms in a direction perpendicular to the skin surface as the pulse wave propagates, the piezoelectric film 21 deforms in its thickness direction, generating a polarization charge. The limiter circuit 13 and the low-gain circuit 14 convert the polarization charge into a voltage, amplify the voltage with a predetermined gain, and output the amplified voltage.

[0023] The biosignal estimation algorithm is an LSTM neural network. The biosignal estimation algorithm is set by machine learning using the mixed signal, the body movement signal, and the pulse wave signal as training data. That is, the biosignal estimation algorithm is set by machine learning using the mixed signal and the body movement signal as input data and the pulse wave signal as target output data.

[0024] In a variant, the biosignal estimation algorithm may be a neural network different from the LSTM, or may be an algorithm different from a neural network.

[0025] 5 shows an example of a mixed signal, a body movement signal, and a pulse wave signal used to set a biological signal estimation algorithm. The mixed signal has an irregular waveform caused by the deformation of the skin surface due to the pulse wave being disturbed by body movement.

[0026] For example, the training data is prepared as follows: First, a biosignal sensor and a body motion sensor are placed on the right wrist, and another biosignal sensor is placed on the left wrist. By moving the right hand while keeping the left hand still, a mixed signal, a body motion signal, and a pulse wave signal can be obtained from the output of a limiter circuit connected to the biosignal sensor, the output of a low-gain circuit connected to the body motion sensor, and the output of another limiter circuit connected to another biosignal sensor.

[0027] 6 shows an example of a mixed signal and an estimated pulse wave signal. In the waveform of the mixed signal, peaks appear randomly, making it impossible to determine which peaks correspond to the pulse wave. In contrast, in the waveform of the estimated pulse wave signal, peaks appear at approximately one-second intervals, demonstrating that the pulse wave was successfully measured.

[0028] FIG. 7 is a flowchart showing an example of a process performed by the biological signal determining unit to determine the validity of an estimated pulse wave signal.

[0029] First, upon receiving an estimated pulse wave signal from the biological signal estimation unit, the biological signal determination unit calculates feature quantities of the estimated pulse wave signal (s1). The feature quantities of the estimated pulse wave signal include descriptive statistics obtained from the estimated pulse wave signal data, i.e., descriptive statistics obtained from a data set consisting of the values ​​of the estimated pulse wave signal at each time point. The feature quantities of the estimated pulse wave signal include one or more descriptive statistics, such as skewness, root mean square (RMS), standard deviation, and peak value.

[0030] In a modified example, the feature quantities of the estimated pulse wave signal may include values ​​other than the descriptive statistics that quantitatively represent the features of the estimated pulse wave signal, in addition to or instead of the descriptive statistics.

[0031] Next, the biological signal determination unit estimates a determination coefficient by substituting the feature quantities of the estimated pulse wave signal into a preset regression equation (s2). The regression equation is obtained, for example, by linear regression, with the feature quantities of the estimated pulse wave signal as explanatory variables and the determination coefficient as a response variable. The determination coefficient quantitatively represents the similarity between the pulse wave signal and the estimated pulse wave signal. Specifically, the determination coefficient is the correlation coefficient (Pearson's product-moment correlation coefficient) between the pulse wave signal and the estimated pulse wave signal. In other words, the determination coefficient is the correlation coefficient between a data set consisting of values ​​of the pulse wave signal at each time point and a data set consisting of values ​​of the estimated pulse wave signal obtained by estimating the pulse wave signal at the same time point as the pulse wave signal.

[0032] The parameters of the regression equation are determined, for example, as follows. First, as described above, a biosignal sensor and a body movement sensor are placed on the right wrist, and another biosignal sensor is placed on the left wrist. The right hand is moved while the left hand is at rest, thereby acquiring a mixed signal, a body movement signal, and a pulse wave signal. Next, an estimated pulse wave signal is calculated from the mixed signal and the body movement signal. Descriptive statistics are calculated from the estimated pulse wave signal, and an actual correlation coefficient is calculated from the pulse wave signal and the estimated pulse wave signal. Finally, the parameters of the regression equation are determined by minimizing an error function determined by the descriptive statistics and the actual correlation coefficient.

[0033] In a modified example, the biological signal determination unit may estimate the determination coefficient based on the feature quantities of the estimated pulse wave signal using a method other than the regression equation. The determination coefficient may be a value different from the correlation coefficient that quantitatively represents the similarity between the pulse wave signal and the estimated pulse wave signal.

[0034] If the determination coefficient is equal to or greater than the threshold (s3: yes), the vital sign determination unit determines that the estimated pulse wave signal is valid and outputs the estimated pulse wave signal (s4).If the determination coefficient is less than the threshold (s3: no), the vital sign determination unit determines that the estimated pulse wave signal is invalid and discards the estimated pulse wave signal (s5).

[0035] If the determination coefficient is equal to the threshold value, the biological signal determination unit may determine that the estimated pulse wave signal is invalid.

[0036] 8A and 8B are diagrams showing examples of estimated pulse wave signals. For the estimated pulse wave signal shown in FIG. 8A, the estimated judgment coefficient is 0.8. For the estimated pulse wave signal shown in FIG. 8B, the estimated judgment coefficient is 0.45. If the judgment coefficient threshold is set to 0.6, the estimated pulse wave signal shown in FIG. 8A is determined to be valid, but the estimated pulse wave signal shown in FIG. 8B is determined to be invalid and discarded. The estimated pulse wave signal shown in FIG. 8B has an unstable waveform, and the peaks corresponding to pulsations are unclear. For the estimated pulse wave signal shown in FIG. 8A, the peaks corresponding to pulsations are clearly visible. Therefore, the estimated pulse wave signal shown in FIG. 8A is appropriate as a pulse wave signal. In this way, the biosignal sensor module can output an accurately estimated pulse wave signal.

[0037] According to this embodiment, the biosignal sensor and the body motion sensor are attached directly to the skin surface, providing excellent sensitivity to deformations of the skin surface. Furthermore, both the outputs of the biosignal sensor and the body motion sensor are generated by piezoelectric films. Therefore, the mixed signal and the body motion signal have a relatively simple relationship as input data for the biosignal estimation algorithm, improving the accuracy of estimating the pulse wave signal. Therefore, the pulse wave signal can be measured accurately even in the presence of body motion.

[0038] Furthermore, LSTM estimates the target waveform by taking into account the strong influence of the previous input waveform when inputting time-series waveform data. Therefore, LSTM can accurately estimate short, regular waveforms, such as pulse wave signal waveforms. This further increases the accuracy of pulse wave signal estimation. Therefore, pulse wave signals can be measured with even greater accuracy, even when there is body movement.

[0039] Furthermore, the biological signal determination unit estimates a determination coefficient from the feature quantities of the estimated pulse wave signal and compares the determination coefficient with a threshold to determine the validity of the estimated pulse wave signal, thereby discarding inaccurate estimated pulse wave signals from which the body movement signal has not been sufficiently removed, thereby enabling more accurate measurement of the pulse wave signal even in the presence of body movement.

[0040] The feature quantity of the estimated pulse wave signal includes descriptive statistics, and the biological signal determination unit estimates the determination coefficient from the descriptive statistics. Estimating the determination coefficient from the descriptive statistics is advantageous in that the calculation load for calculating the descriptive statistics is small.

[0041] Furthermore, the biosignal sensor and the body movement sensor are film-type sensors, so even if the biosignal sensor and the body movement sensor are worn when using the biosignal sensor module, discomfort to the user is reduced.

[0042] [First Modification] A biosignal sensor module according to a first modification differs from the biosignal sensor module according to the embodiment in that the feature quantities of the estimated pulse wave signal include descriptive statistics and power spectrum values.

[0043] Specifically, the feature quantities of the estimated pulse wave signal include not only descriptive statistics but also the power spectrum values ​​of the estimated pulse wave signal at predetermined frequencies, in other words, the intensity of the estimated pulse wave signal at predetermined frequencies. The feature quantities of the estimated pulse wave signal include, for example, a data set consisting of power spectrum values ​​selected and set so as to minimize the minimum value of the error function for regression. The feature quantities of the estimated pulse wave signal include, for example, skewness and power spectrum values ​​at frequencies of 38 Hz, 8 Hz, 12 Hz, 10 Hz, and 14 Hz.

[0044] In the step of calculating the feature quantities of the estimated pulse wave signal, the biological signal determination unit calculates descriptive statistics and power spectrum values, and then estimates a determination coefficient from the descriptive statistics and power spectrum values.

[0045] Table 1 below shows an example of the results of the determination made by the biological signal determination unit. The quality of the estimated pulse wave signal indicates the clarity of the peaks corresponding to the pulsations of the estimated pulse wave signal that should be determined to be valid, and is quantitatively represented by the value of the determination coefficient of the estimated pulse wave signal that should be determined to be valid. The condition that the quality of the estimated pulse wave signal is relatively low (or high) means that the threshold value of the determination coefficient is set relatively low (or high). The term "descriptive statistics only" means that the feature quantities of the estimated pulse wave signal consist of descriptive statistics. The term "descriptive statistics and power spectrum" means that the feature quantities of the estimated pulse wave signal consist of descriptive statistics and power spectrum values. The values ​​shown in Table 1 indicate the percentage of estimated pulse wave signals that should be determined to be valid that were correctly determined to be valid under the conditions of the quality of the estimated pulse wave signal and the feature quantities of the estimated pulse wave signal shown in Table 1. More specifically, the values ​​shown in Table 1 indicate the ratio of the number of samples whose actual determination coefficient values ​​are greater than the threshold and whose estimated determination coefficient values ​​are greater than the threshold to the number of samples whose actual determination coefficient values ​​are greater than the threshold under the conditions of the quality of the estimated pulse wave signal and the feature quantities of the estimated pulse wave signal shown in Table 1.

[0046]

[0047] As shown in Table 1, adding the power spectrum value to the feature quantity of the estimated pulse wave signal increases the rate at which the validity of the estimated pulse wave signal is correctly determined. In this way, the biological signal sensor module according to the first modification can reduce the number of missed estimated pulse wave signals.

[0048] According to the first modification, the biological signal determination unit estimates the determination coefficient based on the power spectrum value in addition to the descriptive statistics, thereby improving the accuracy of estimating the determination coefficient and therefore the accuracy of determining the validity of the estimated pulse wave signal.

[0049] [Second Modification] Fig. 9 is a functional block diagram showing a biosignal sensor module according to a second modification. The biosignal sensor module according to the second modification differs from the biosignal sensor module according to the embodiment in that it includes a bioinformation calculation unit 18 that calculates bioinformation based on an estimated pulse wave signal determined to be valid. Specifically, the processing unit 15 includes the bioinformation calculation unit 18. Upon receiving an estimated pulse wave signal from the biosignal determination unit 17, the bioinformation calculation unit 18 calculates bioinformation, particularly a time series of the bioinformation, from the estimated pulse wave signal. The bioinformation may be, for example, the pulse rate, the beat-to-beat interval (RRI), or the pulse wave velocity (PWV).

[0050] 10(A) and 10(B) are conceptual diagrams showing an example of a time series of biological information. In FIGS. 10(A) and 10(B), T indicates a period during which the estimated pulse wave signal is discarded. As shown in FIG. 10(A), the biological information calculation unit may not output the value of the biological information for the period during which the estimated pulse wave signal is discarded, but may output a time series of biological information that is interrupted for a certain period. Alternatively, as shown in FIG. 10(B), the biological information calculation unit may output the value of the biological information immediately before the period during which the estimated pulse wave signal is discarded as the value of the biological information for the period during which the estimated pulse wave signal is discarded.

[0051] 11 is a boxplot showing the distribution of errors between the calculated pulse rate and the actual pulse rate. When the threshold value of the judgment coefficient is 0.7, the range from the minimum to maximum error of the calculated pulse rate is between -5 and 5, and pulses with large errors are invalidated. As the threshold value of the judgment coefficient increases, the range from the minimum to maximum value narrows, and pulses with small errors are extracted. In this way, the biosignal sensor module according to the second modification can calculate bioinformation with small errors.

[0052] According to the second modification, the biological information calculation unit calculates the biological information based on the estimated pulse wave signal that is determined to be valid, and therefore the biological information calculation unit can calculate the biological information with little error based on the accurately estimated pulse wave signal.

[0053] [Third Modification] A bio-signal sensor module according to a third modification differs from the bio-signal sensor module according to the embodiment in that the bio-signal estimation algorithm is a bidirectional LSTM neural network.

[0054] In bidirectional LSTM, when waveform data (time series data) is input, the target waveform is estimated by taking into account not only the immediately preceding input waveform but also the immediately following input waveform. Therefore, this modification further improves the accuracy of pulse wave signal estimation. Therefore, pulse wave signals can be measured with even greater accuracy, even when body movement is present.

[0055] [Fourth Modification] The bio-signal sensor module according to the fourth modification differs from the bio-signal sensor module according to the embodiment in the following respects. Specifically, the bio-signal sensor module includes a bio-signal sensor and a body motion sensor stacked with a buffer material interposed between them. Furthermore, the bio-signal sensor module does not include a low-gain circuit, but only includes a limiter circuit. The body motion sensor is attached to the skin surface with the bio-signal sensor and the buffer material interposed between them. The limiter circuit amplifies the output of the bio-signal sensor and the output of the body motion sensor with the same gain. As a result, the limiter circuit amplifies the output of the bio-signal sensor to output a mixed signal, and amplifies the output of the body motion sensor to output a body motion signal.

[0056] According to the fourth modification, a low-gain circuit is not required to obtain a body movement signal, so that a simpler biosignal sensor module can be realized.

[0057] [Fifth Modification] The bio-signal sensor module according to the fifth modification differs from the bio-signal sensor module according to the embodiment in the following respects. Specifically, the bio-signal sensor module includes a bio-signal sensor and a body motion sensor that are integrated and formed within the same laminate. Furthermore, the bio-signal sensor module does not include a low-gain circuit, but only includes a limiter circuit. The bio-signal sensor and the body motion sensor each have a signal electrode attached to the piezoelectric film. The signal electrode of the body motion sensor has an area smaller than that of the signal electrode of the bio-signal sensor. The limiter circuit amplifies the output of the bio-signal sensor and the output of the body motion sensor with the same gain. As a result, the limiter circuit amplifies the output of the bio-signal sensor to output a mixed signal, and amplifies the output of the body motion sensor to output a body motion signal.

[0058] According to the fifth modification, a low-gain circuit is not required to obtain a body movement signal, so that a simpler biosignal sensor module can be realized.

[0059] [Sixth Modification] The biosignal sensor module according to the sixth modification differs from the biosignal sensor module according to the embodiment in the following respects. Specifically, the biosignal sensor module includes one sensor and a demultiplexer. The sensor has the same structure as the biosignal sensor described above and outputs a signal corresponding to the deformation of the skin surface. The demultiplexer continuously switches the output destination of the signal from the sensor at high speed, distributing part of the signal from the sensor to the limiter circuit and the rest of the signal from the sensor to the low-gain circuit. As a result, the mixed signal output from the limiter circuit and the body movement signal output from the low-gain circuit can be considered to reflect the deformation of the skin surface over the same time period.

[0060] According to the sixth modification, since a plurality of sensors are not required to obtain the mixed signal and the body movement signal, a simpler biological signal sensor module can be realized.

[0061] [Other Modifications] The biological signal sensor module may estimate a biological signal corresponding to deformation of the skin surface caused by tremors that occur involuntary when muscles contract periodically.

[0062] The biosignal sensor module may estimate a biosignal corresponding to vibrations on the skin surface that occur when the fingertip touches an object.

[0063] The biosignal sensor module may estimate a biosignal corresponding to deformation caused by blood vessels, deformation caused by external forces, or deformation caused when a person moves their body.

[0064] The biosignal sensor module is particularly advantageous for estimating periodic biosignals such as pulse wave signals.

[0065] The configurations shown in the above embodiments may be replaced or combined as appropriate.

[0066] The above description of the embodiments should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined not by the above embodiments but by the claims. Furthermore, the scope of the present invention is intended to include all modifications that are equivalent to the scope of the claims and fall within the scope thereof.

[0067] The present invention has the following configuration.

[0068] (1) A biosignal sensor module comprising: a sensor having a piezoelectric film and outputting a signal corresponding to deformation of the skin surface; a detection circuit amplifying the signal output from the sensor to output a first signal including a biosignal and a body movement signal, and a second signal including the body movement signal; a biosignal estimation unit estimating the biosignal based on the first signal and the second signal using a preset biosignal estimation algorithm, and outputting an estimated biosignal; and a biosignal determination unit calculating a feature of the estimated biosignal, estimating a determination coefficient based on the feature of the estimated biosignal, and comparing the determination coefficient with a preset threshold to determine the validity of the estimated biosignal.

[0069] (2) The biological signal sensor module according to (1), wherein the biological signal determination unit estimates the determination coefficient by substituting a feature amount of the estimated biological signal into a preset regression equation.

[0070] (3) The biological signal sensor module according to (1) or (2), wherein the determination coefficient is a correlation coefficient between the biological signal and the estimated biological signal.

[0071] (4) The biosignal sensor module according to any one of (1) to (3), wherein the feature amount of the estimated biosignal includes descriptive statistics.

[0072] (5) The biosignal sensor module according to any one of (1) to (3), wherein the feature quantity of the estimated biosignal includes a descriptive statistic and a power spectrum value.

[0073] (6) The biosignal sensor module according to any one of (1) to (5), further comprising a bioinformation calculation unit that calculates bioinformation based on the estimated biosignal that has been determined to be valid.

[0074] 11: Biological signal sensor 12: Body movement sensor 13: Limiter circuit 14: Low gain circuit 15: Processing unit 16: Biological signal estimation unit 17: Biological signal determination unit 18: Biological information calculation unit 21: Piezoelectric film 22: Adhesive material 23: Adhesive material 24: Signal electrode 25: Reference electrode 26: Flexible substrate 27: Flexible substrate 31: Adhesive material

Claims

1. A biosignal sensor module comprising: a sensor having a piezoelectric film and outputting a signal corresponding to deformation of the skin surface; a detection circuit amplifying the signal output from the sensor to output a first signal including a biosignal and a body movement signal, and a second signal including the body movement signal; a biosignal estimation unit estimating the biosignal based on the first signal and the second signal using a preset biosignal estimation algorithm and outputting an estimated biosignal; and a biosignal judgment unit calculating a feature of the estimated biosignal, estimating a judgment coefficient based on the feature of the estimated biosignal, and comparing the judgment coefficient with a preset threshold to judge the validity of the estimated biosignal.

2. The biosignal sensor module according to claim 1, wherein the biosignal determination unit estimates the determination coefficient by substituting the feature quantity of the estimated biosignal into a preset regression equation.

3. The biosignal sensor module according to claim 1 or 2, wherein the determination coefficient is a correlation coefficient between the biosignal and the estimated biosignal.

4. The biosignal sensor module according to any one of claims 1 to 3, wherein the feature of the estimated biosignal includes descriptive statistics.

5. The biosignal sensor module according to any one of claims 1 to 3, wherein the feature quantities of the estimated biosignal include descriptive statistics and power spectrum values.

6. A biosignal sensor module according to any one of claims 1 to 5, further comprising a bioinformation calculation unit that calculates bioinformation based on the estimated biosignal that has been determined to be valid.

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