Heartbeat information acquisition device, heartbeat information acquisition method, and body motion determination device

Abstract

A heartbeat information acquisition device (30) acquires heartbeat information of a subject (S) on a bed (BD), and comprises: a signal acquisition unit (31) that acquires a plurality of signals each including a component that varies according to the heartbeat of the subject; a signal selection unit (34) that selects, on the basis of the kurtosis of each of the plurality of signals, an information acquisition signal (IS) to be used for acquiring the heartbeat information from the plurality of signals; and a heartbeat information acquisition unit (36) that acquires the heartbeat information of the subject on the basis of the information acquisition signal.

Description

Heart rate information acquisition device, heart rate information acquisition method, and body movement detection device 【0001】 This disclosure relates to a heart rate information acquisition device, a heart rate information acquisition method, and a body movement detection device. 【0002】 Devices are used to acquire a subject's biological information based on signals that indicate the temporal changes in the subject's state. Some such devices use multiple signals, each indicating a temporal change in the subject's state. 【0003】 Patent Document 1 discloses a biological information acquisition device that includes a selection means for selecting biological information with a smaller outlier parameter from among multiple sets of biological information of the same type. 【0004】 Patent No. 7363268 specification 【0005】 In devices that acquire a subject's biological information based on signals indicating temporal changes in the subject's state, it is desirable to acquire the biological information with high accuracy. 【0006】 For example, when using multiple signals, each indicating a temporal variation in the subject's state, the accuracy of the subject's biological information acquired may differ depending on which of the multiple signals is used. Therefore, it is desirable to accurately determine which of the multiple signals to use. In this respect, Patent Document 1 is not sufficient. Furthermore, when determining whether or not the subject is moving, it is desirable to perform the movement detection with high accuracy. 【0007】 The purpose of this disclosure is to provide a heart rate information acquisition device and a heart rate information acquisition method that can select an appropriate signal from multiple signals to acquire highly accurate heart rate information. 【0008】 This disclosure also aims to provide a body movement detection device that can determine with high accuracy whether or not a subject is moving. 【0009】A heart rate information acquisition device is provided for acquiring heart rate information of a subject lying in bed, comprising: a signal acquisition unit that acquires a plurality of signals, each containing a component that varies according to the subject's heart rate; a signal selection unit that selects an information acquisition signal from the plurality of signals to be used for acquiring the heart rate information based on the kurtosis of each of the plurality of signals; and a heart rate information acquisition unit that acquires the subject's heart rate information based on the information acquisition signal. 【0010】 A second aspect of the present disclosure provides a heart rate information acquisition method for acquiring heart rate information of a subject lying in bed, the method comprising: a signal acquisition unit acquiring a plurality of signals, each containing a component that varies according to the subject's heart rate; a signal selection unit selecting an information acquisition signal from the plurality of signals based on the kurtosis of each of the plurality of signals to be used for acquiring the heart rate information; and a heart rate information acquisition unit acquiring the subject's heart rate information based on the information acquisition signal. 【0011】 A third aspect of the present disclosure is provided, which includes a body movement determination unit that determines whether or not body movement is occurring in a subject based on a comparison between a threshold value and a threshold value among a plurality of normalized standard deviations calculated for a plurality of signals, each containing a component that fluctuates in accordance with the body movement of the subject on a bed. 【0012】 According to the heart rate information acquisition device and heart rate information acquisition method of this disclosure, it is possible to select an appropriate signal from multiple signals and acquire highly accurate heart rate information. 【0013】 The body movement detection device of this disclosure can determine with high accuracy whether or not a subject is moving. 【0014】Figure 1 is a block diagram showing the configuration of a biological information acquisition system, which is one embodiment of the system. Figure 2 is a plan view showing the arrangement of load detectors on a bed. Figure 3 is a flowchart of the process for acquiring heart rate information. Figure 4 is a graph showing an example of a load signal. The four graphs on the left side of Figure 4 show signals obtained by filtering each of the four load signals based on the output values ​​of the four load detectors. The four graphs on the right side of Figure 4 each show signals obtained by normalizing the four graphs on the left side of Figure 4. Figure 5 shows the waveform of one cycle of the BCG signal. Figure 6(a) is a graph showing an example of the waveform of the load signal before normalization when no body movement occurs in the subject during the target interval, and an example of the waveform of the load signal after normalization. Figure 6(b) is a graph showing an example of the waveform of the load signal before normalization when body movement occurs in the subject during the target interval, and an example of the waveform of the load signal after normalization. The left side of Figure 7(a) is a graph showing an example of a time-domain waveform exhibiting periodic peaks, and the right side of Figure 7(a) is a graph showing an example of a frequency spectrum obtained by applying a Fourier transform to the signal exhibiting the same time-domain waveform. The left side of Figure 7(b) is a graph showing an example of a time-domain waveform that does not exhibit periodic peaks, and the right side of Figure 7(b) is a graph showing an example of a frequency spectrum obtained by applying a Fourier transform to the signal exhibiting the same time-domain waveform. Figure 8(a) is a graph showing an example of a normalized weight signal waveform. Figure 8(b) is a graph showing the frequency spectrum obtained by applying a Fourier transform to the weight signal exhibiting the waveform in Figure 8(a). Figure 9(a) is a graph showing an example of a normalized weight signal waveform. Figure 9(b) is a graph showing the frequency spectrum obtained by applying a Fourier transform to the weight signal exhibiting the waveform in Figure 9(a). Figure 10(a) is a graph showing a BCG waveform. Figure 10(b) is a graph showing a Gaussian noise waveform. Figure 10(c) is a graph showing a sine wave waveform. Figure 10(d) is a graph showing the waveform of a sawtooth wave. Figure 11 is a flowchart showing the specific steps of the signal selection process. Figures 12(a) and 12(b) are graphs showing examples of normalized load signal waveforms, respectively.Figure 12(c) is a diagram showing the data distribution obtained by performing principal component analysis on the load signal showing the waveform shown in Figure 12(a) and the load signal showing the waveform shown in Figure 12(b). Figure 13(a) is the waveform of the load signal generated based on the data distribution shown in Figure 13(b). Figure 13(b) is a diagram showing the data distribution obtained by projecting the data points in the data distribution shown in Figure 12(c) onto the PCA axis in a direction perpendicular to the PCA axis. Figure 14 is a flowchart showing the specific procedure of the heart rate information acquisition process. Figure 15(a) is an explanatory diagram for explaining the process of extracting multiple BCG signals from the information acquisition signal. Figure 15(b) is a graph showing the waveform of the template signal created by calculating the average of multiple BCG signals extracted from the information acquisition signal. Figure 16(a) is an explanatory diagram showing the state in which the subject is lying supine on the bed with the body axis aligned with the longitudinal direction of the bed. The table in Figure 16(b) shows the relationship between the direction of movement of the subject's center of gravity shown in Figure 16(a) and the direction of fluctuation of the output value of the load detector. 【0015】 <Embodiment> The biological information acquisition system 100 (Figure 1) of the embodiment of this disclosure will be described as an example in which the system is used together with a bed BD (Figure 2) to acquire heart rate information of a subject S on the bed BD. 【0016】 [Configuration of the Biometric Information Acquisition System 100] As shown in Figure 1, the Biometric Information Acquisition System 100 of this embodiment mainly comprises a load detection unit 10 and a control unit 30 (an example of a "heart rate information acquisition device"). The load detection unit 10 and the control unit 30 are connected via an A / D conversion unit 20. A storage unit 40, a display unit 50, a notification unit 60, and an input unit 70 are connected to the control unit 30 by wiring or wirelessly. 【0017】 The load detection unit 10 includes four load detectors 11, 12, 13, and 14. Each of the load detectors 11, 12, 13, and 14 is a load detector that detects load using, for example, a beam-type load cell. Each of the load detectors 11, 12, 13, and 14 is connected to the A / D conversion unit 20 by wiring or wirelessly. 【0018】As shown in Figure 2, the four load detectors 11 to 14 are positioned under the casters C1, C2, C3, and C4 attached to the lower ends of the legs BL1, BL2, BL3, and BL4 at the four corners of the bed BD used by the subject S. 【0019】 In Figure 2, the X-axis extends in the width direction of the bed BD from the center of the bed BD's longitudinal direction, and the Y-axis extends in the longitudinal direction of the bed BD from the center of the bed BD's width direction. The intersection of the X-axis and the Y-axis is the center O of the bed BD. The positive and negative sides of the X-axis are the right and left sides of the bed BD, respectively, and the positive and negative sides of the Y-axis are the head and foot sides of the bed BD, respectively. Load detector 11 is located at the right and foot corner of the bed BD, load detector 12 is located at the left and foot corner of the bed BD, load detector 13 is located at the head and left corner of the bed BD, and load detector 14 is located at the head and right corner of the bed BD. 【0020】 The A / D conversion unit 20 is connected to the load detection unit 10 and the control unit 30 by wiring or wirelessly, respectively. The A / D conversion unit 20 includes an A / D converter that converts the analog signal input from the load detection unit 10 into a digital signal. 【0021】 The control unit 30 mainly comprises a load signal acquisition unit 31 (an example of a "signal acquisition unit"), a body movement determination unit 32, a noise determination unit 33, a signal selection unit 34, a noise reduction unit 35, and a heart rate information acquisition unit 36. Details of the operation of each unit will be described later. 【0022】 The storage unit 40 is a storage device that stores data used by the control unit 30. The storage unit 40 may be, for example, a hard disk, a non-volatile memory, or the like. 【0023】 The display unit 50 visually displays information such as heart rate information output from the control unit 30 to the user of the biometric information acquisition system 100. The display unit 50 can be a monitor such as an LCD monitor. 【0024】 The notification unit 60 provides a predetermined notification audibly based on information such as heart rate information output from the control unit 30. The notification unit 60 may be, for example, a speaker. 【0025】The input unit 70 is an interface for inputting information to the biometric information acquisition system 100. The input unit 70 may be, for example, a keyboard and a mouse. 【0026】 [Operation of the Biometric Information Acquisition System 100] The operation of acquiring heart rate information of a subject S on the bed BD using the Biometric Information Acquisition System 100 having the above configuration will be described. 【0027】 The acquisition of heart rate information of subject S using the biometric information acquisition system 100 includes a load signal acquisition step S1, a body movement determination step S2, a noise determination step S3, a signal selection step S4, a noise reduction step S5, a heart rate information acquisition step S6, and a display step S7, as shown in the flowchart of Figure 3. 【0028】 [Load signal acquisition process S1] In load signal acquisition process S1, the load signal acquisition unit 31 acquires multiple load signals via the load detection unit 10, each containing a component that fluctuates according to the subject's heart rate. 【0029】 The load signal acquisition unit 31 detects the load of the subject S on the bed BD using load detectors 11, 12, 13, and 14. The load of the subject S on the bed BD is distributed and applied to the load detectors 11 to 14, which are located under the legs BL1 to BL4 at the four corners of the bed BD, and is detected in a distributed manner by these detectors. 【0030】 Each of the load detectors 11 to 14 detects a load (load change) and outputs it as an analog signal to the A / D conversion unit 20. The A / D conversion unit 20 converts the analog signal into a digital signal with a sampling period of, for example, 5 milliseconds (0.005 seconds), and outputs the digital signal (hereinafter referred to as "load signal") to the load signal acquisition unit 31. Hereinafter, the load signals obtained by digitally converting the analog signals output from load detectors 11, 12, 13, and 14 in the A / D conversion unit 20 will be referred to as load signals LS1, LS2, LS3, and LS4, respectively. 【0031】 The load signal acquisition unit 31 applies a bandpass filter to each of the load signals LS1 to LS4 to remove noise from each of the load signals LS1 to LS4. 【0032】Human respiration is typically 12 to 20 breaths per minute (approximately 0.2 Hz to 0.33 Hz) at rest, and human heart rate is typically 60 to 100 beats per minute (approximately 1 Hz to 1.7 Hz) at rest. Therefore, for example, by removing low-frequency components of the load signal (for example, components below 0.5 Hz) using a bandpass filter, it is possible to reduce components in the load signal that are different from the component showing temporal variation corresponding to the subject's heart rate (for example, components showing temporal variation corresponding to the subject's body movement, components showing temporal variation corresponding to the subject's respiration, and various types of noise such as noise caused by the surrounding environment). 【0033】 In this embodiment, the load signal acquisition unit 31 applies a bandpass filter to each of the load signals LS1 to LS4, allowing only components in the frequency band of 1 Hz to 8.5 Hz to pass through. The passband of the bandpass filter can be arbitrarily set so that frequency components not involved in the acquisition of heart rate information are removed. 【0034】 Next, the load signal acquisition unit 31 performs normalization processing on each of the load signals LS1 to LS4. The load signal acquisition unit 31 can normalize the load signals LS1 to LS4 using any known method. Specifically, for example, normalization is performed by adjusting the amplitude of each load signal LS1 to LS4 so that the average value of the amplitude in the target period PR1 (Figure 4) is 0 and the standard deviation of the signal in the target period PR1 is 1. By performing normalization processing on the load signals LS1 to LS4, normalized signals NS1 to NS4 are obtained. 【0035】 As shown in the graphs on the left side of Figure 4, the amplitudes of the load signals LS1 to LS4 may differ from each other. This is because the amplitude changes depending on several factors, such as the position and posture of the subject S on the bed BD, the method of setting up the load detectors, and the magnitude of the passband frequency components of the bandpass filter corresponding to the subject's breathing. The graphs on the left side of Figure 4 show a situation where the subject S is located in the positive region in the X direction (width direction) of the bed BD, and the amplitudes of the load signals LS1 and LS4 based on load detectors 11 and 14 are larger than the amplitudes of the load signals LS2 and LS3 based on load detectors 12 and 13. 【0036】By normalization processing, the variations in the amplitudes of such load signals LS1 to LS4 are eliminated. As shown in each graph on the right side of FIG. 4, the amplitudes of the normalized signals NS1 to NS4 become similar to each other. 【0037】 Each of the load signals LS1 to LS4 and the normalized signals NS1 to NS4 acquired in the load signal acquisition step S1 shows a BCG (BallistoCardioGram, ballistocardiogram) waveform as shown in FIG. 5 when the noise is small (in other words, when the SNR is large). The BCG waveform is a waveform generated in response to slight body movements corresponding to the heartbeat of the heart. For each heartbeat, the J peak PK with the largest amplitude Ｊ and the J peak PK Ｊ occurring before and having an amplitude smaller than that of the J peak PK Ｊ the H peak PK, Ｈ and the I peak PK Ｉ and the J peak PK Ｊ occurring after and having an amplitude smaller than that of the J peak PK Ｊ the K peak PK, Ｋ and the L peak PK Ｌ are shown. 【0038】 [Body movement determination step S2] In the body movement determination step S2, the body movement determination unit 32 determines whether or not body movement has occurred in the subject S within the target period PR1. The principle of body movement determination executed by the body movement determination unit 32 is as follows. 【0039】 When no body movement has occurred in the subject S within the target period PR1, as shown in the left graph of FIG. 6(a), the amplitude of the load signal LS (any one of the load signals LS1 to LS4) is generally the same throughout the entire period within the target period PR1. In this case, as shown in the right graph of FIG. 6(a), the amplitude of the normalized signal NS (any one of the normalized heartbeat signals NS1 to NS4) is also generally the same throughout the entire period of the target period PR1. 【0040】 On the other hand, when body movement has occurred in the subject S within the target period PR1, as shown in the left graph of FIG. 6(b), the amplitude of the load signal LS becomes larger during the body movement occurrence period PR ＭＶ than in other periods. In this case, the normalization of the load signal LS during the body movement occurrence period PRＭＶ Because it is affected by the amplitude in the period PR, as shown in the graph on the right side of Figure 6(b), the amplitude of the normalized signal NS is affected by the period PR during which body motion occurs. ＭＶ It decreases over a different period. 【0041】 Thus, the amplitude of the normalized signal NS changes depending on whether or not the subject S moves. Therefore, if we divide the target period PR1 into multiple periods and calculate the standard deviation for each period obtained (for example, periods PR11, PR12, and PR13 in Figure 6), if no body movement occurs in the subject S during the target period PR1, the standard deviations of the normalized signal NS in each period will be approximately the same (for example, about 1 each). On the other hand, if body movement occurs in the subject S during the target period PR1, then among each period, the period in which body movement occurs PR ＭＶ The standard deviation of the normalized signal NS in periods that do not include this period (for example, period PR11 in Figure 6) is relatively small (for example, less than 1). 【0042】 Therefore, based on the magnitude of the standard deviation for each period obtained by dividing the normalized signal NS, it is possible to determine whether or not the subject S experienced body movement. 【0043】 Based on the above principle, the body motion determination unit 32 first divides each of the normalized signals NS1 to NS4 of the target period PR1 acquired by the load signal acquisition unit 31 into N periods at regular intervals, and then calculates 4N standard deviations σ ＮＳ１－ｎ ~σ ＮＳ４－ｎ Calculate (n = 1, 2, 3, ... N). Then, calculate the standard deviation σ. ＮＳ１－ｎ ~σ ＮＳ４－ｎ The minimum value among them σ ＭＩＮ threshold TH σ Compare it to this. 【0044】 The body movement determination unit 32 determines the minimum value σ ＭＩＮ is threshold TH σ If it is smaller than this, it is determined that there was body movement during the target period PR1, and the minimum value σ ＭＩＮ is threshold TH σ If the above conditions are met, it is determined that there was no physical movement during the target period PR1. 【0045】If the body movement determination unit 32 determines that there was body movement during the target period PR1, the control unit 30 may not perform the noise determination process S3 and subsequent steps using the normalized signals NS1 to NS4 within the target period PR1, and may wait until the normalized signals NS1 to NS4 for the next period are obtained. This is because if the normalized signals NS1 to NS4 for a certain period are affected by the body movement of the subject S, it is difficult to accurately obtain the heart rate information of the subject S using those normalized signals NS1 to NS4. 【0046】 [Noise Determination Process S3] In the noise determination process S3, the noise determination unit 33 determines the amount of noise contained in each of the normalized signals NS1 to NS4. Specifically, it determines whether each of the normalized signals NS1 to NS4 is a low-noise signal with a high signal-to-noise ratio suitable for acquiring heart rate information. The principle of the determination performed by the noise determination unit 33 is as follows. 【0047】 When a Fourier transform is applied to a periodic time-domain waveform TW, which shows a peak every T [s] as shown on the left side of Figure 7(a), a frequency spectrum FS is obtained, which shows a spectral peak every 1 / T [Hz] as shown on the right side of Figure 7(a). The spectral peak of the frequency spectrum FS is largest at 0 [Hz] and decreases as the frequency moves away from 0 [Hz] by 1 / T [Hz]. Alternatively, any frequency analysis method that yields a frequency spectrum may be used instead of the Fourier transform. 【0048】 On the other hand, when a Fourier transform is applied to a time-domain waveform TW that does not have periodicity, as shown on the left side of Figure 7(b), the resulting frequency spectrum FS does not show any prominent spectral peaks that appear at predetermined intervals, as shown on the right side of Figure 7(b). 【0049】Thus, when the time-domain waveform exhibits periodic peaks, i.e., when the noise in the time-domain waveform is small, the frequency spectrum obtained by the Fourier transform also exhibits periodic spectral peaks. On the other hand, when the time-domain waveform does not exhibit periodic peaks, i.e., when the noise in the time-domain waveform is large, the frequency spectrum obtained by the Fourier transform does not exhibit periodic spectral peaks. Therefore, by determining how prominent the periodic spectral peaks are in the frequency spectrum, it is possible to determine the amount of noise contained in the time-domain waveform. 【0050】 The noise determination unit 33 determines the amount of noise for each of the normalized signals NS1 to NS4 based on the above principle. The specific determination process will be explained using the processing for normalized signal NS1 as an example. The noise determination unit 33 performs the same processing as described below for each of the normalized signals NS2 to NS4. 【0051】 The noise determination unit 33 first determines the positive or negative sign of the waveform of the normalized signal NS1 (Figures 8(a) and 9(a)) for the target period PR2. The target period PR2 may be the same period as the target period PR1, a period obtained by combining multiple target periods PR1 into one, or one of several periods obtained by dividing a single target period PR1 into multiple parts. In addition, the target period PR2 can be set arbitrarily. Specifically, the noise determination unit 33 determines the positive or negative sign of the waveform by, for example, the following method. 【0052】 The waveform of one period of the BCG signal, as shown in Figure 5, is asymmetrical, with a first region where the amplitude value is greater than the average amplitude (for example, the region with positive amplitude values ​​in Figure 5) and a second region where the amplitude value is smaller than the average amplitude (for example, the region with negative amplitude values ​​in Figure 5). When the standard deviation σ1 in the first region and the standard deviation σ2 in the second region are calculated for the waveform of one period of the BCG signal as shown in Figure 5, the J-peak PK Ｊ The standard deviation is large in regions where there are few large waves, such as waves with a peak (J-wave). This trend is also observed in signals that periodically contain BCG signals. 【0053】Therefore, the noise determination unit 33 calculates the average value of the amplitude for the normalized signal NS1 for the target period PR2, and calculates the standard deviation σ1 in the first region where the amplitude value is greater than the average amplitude, and the standard deviation σ2 in the second region where the amplitude value is smaller than the average amplitude. Then, if the standard deviation σ1 in the first region is greater than the standard deviation σ2 in the second region (i.e., J-peak PK Ｊ If (is present on the positive side), the normalized signal NS1 is used as is for subsequent processing. On the other hand, if the standard deviation σ2 in the second region is greater than the standard deviation σ1 in the first region (i.e., J-peak PK Ｊ If the J peak PK is present on the negative side, the normalized signal NS1 is multiplied by "-1" to invert the sign of the normalized signal NS1, and then the subsequent processing is performed. That is, the noise determination unit 33 determines the J peak PK Ｊ The normalized signal NS1 is adjusted so that it appears on the positive side, and then subsequent processing is performed. 【0054】 The noise determination unit 33 may also calculate the standard deviation σ1 of the first region and the standard deviation σ2 of the second region for each of the L regions obtained by dividing the target period PR2 into L periods (where L is any natural number of 2 or more). In this case, the noise determination unit 33 calculates the median σ1 of the L standard deviations σ1. Ｍ The median σ² of L standard deviations σ² calculated Ｍ You can also compare it with the median σ1. Ｍ The median σ² Ｍ If it is greater than the median σ², the normalized signal NS1 may be used as is for subsequent processing, and the median σ² Ｍ The median σ1 Ｍ If it is greater than this, the normalized signal NS1 may be multiplied by "-1" to invert the sign of the normalized signal NS1. By using the median in this way, the effects of partial waveform abnormalities can be suppressed. 【0055】 Next, the noise determination unit 33 determines the J peak PK of the BCG waveform. Ｊ Estimated J-peak EPK that is estimated to show Ｊ It detects the following. As described above, the noise determination unit 33 performs a positive / negative waveform determination in advance and detects the J peak PK. ＪThe normalized signal NS1 is adjusted so that the positive peak appears on the positive side. Therefore, the noise determination unit 33 estimates the J peak EPK based on the detection of only the positive peak of the normalized signal NS1. Ｊ It can be detected. 【0056】 The noise determination unit 33 estimates the J-peak EPK as a peak with a large amplitude using any known peak detection method. Ｊ It detects, for example, the estimated J-peak EPK. Ｊ This is detected by performing the following steps (1) to (3). 【0057】 (1) Peak detection processing is performed on the normalized signal NS1 for the target period PR2, and all positive peaks included in the normalized signal NS1 for the target period PR2 are detected. 【0058】 (2) Calculate the average value of the amplitude of all peaks detected in (1), and set this average value as the threshold. 【0059】 (3) The normalized signal NS1 for the target period PR2 is subjected to peak detection processing again, and the peaks whose amplitude is larger than the threshold set in (2) and which are located at intervals greater than or equal to the maximum heart rate set in the specifications are estimated as J-peak EPKs. Ｊ It is detected as such. 【0060】 In this way, the noise determination unit 33 estimates the peaks with relatively large amplitudes among the peaks included in the normalized signal NS1 for the target period PR2 as the J-peak EPK. Ｊ It is detected as such. 【0061】 Next, the noise determination unit 33 determines the two adjacent estimated J peaks EPK included in the normalized signal NS1 for the target period PR2. Ｊ The mean heart rate interval IN is the average value during the period PR2 of the interval IN. ＡＶ The noise determination unit 33 calculates, for example, the first estimated J peak EPK indicated by the normalized signal NS1 within the target period PR2. Ｊ From the last estimated J-peak EPK Ｊ Time until T ２ [s] Estimated J-peak EPK included in the normalized signal NS1 for the target period PR2. ＪLet M be the number of , and the mean heart rate interval IN is calculated using the following formula (1). ＡＶ Calculate IN ＡＶ = T ２ / (M-1) ...(1) 【0062】 Next, the noise detection unit 33 applies a Fourier transform to the normalized signal NS1 for the target period PR2 to obtain the frequency spectrum FS1 (Figures 8(b) and 9(b)). 【0063】 As shown in Figure 8(a), the periodicity of the normalized signal NS1 in the target period PR2 is high, and any two adjacent estimated J peaks EPK Ｊ The interval between IN is the average heart rate interval IN. ＡＶ If they roughly match, as shown in Figure 8(b), the frequency spectrum FS1 obtained by the Fourier transform will be 1 / IN in the frequency direction from the position of frequency 0 [Hz]. ＡＶ Spectral peak SP appears periodically at [Hz] intervals. ｍ This shows that (m is a natural number greater than or equal to 1). 【0064】 On the other hand, as shown in Figure 9(a), the periodicity of the normalized signal NS1 in the target period PR2 is low, and any two adjacent estimated J peaks EPK Ｊ The interval between these is the average heart rate interval IN ＡＶ If it deviates from this, as shown in Figure 9(b), the frequency spectrum FS1 obtained by the Fourier transform is 1 / IN ＡＶ Spectral peaks do not appear at intervals of [Hz]. 【0065】 The noise determination unit 33 determines whether the normalized signal NS1 is a low-noise signal based on the acquired frequency spectrum FS1, according to the following procedure. 【0066】 The noise detection unit 33 first starts from the position of frequency 0 [Hz] and measures 1 / IN in the frequency direction. ＡＶ [Frequency] Region for each frequency (hereinafter referred to as "Peak Region PA") ｍ The term "peak region amplitude sum" is used to sum the amplitudes of the frequency spectrum (where m is a natural number greater than or equal to 1). ＰＡ Calculate the peak region PA. ｍ The width is, for example, 1 / IN ＡＶThe frequency range can be approximately 15-25% of [Hz]. 【0067】 The noise determination unit 33 then determines the peak region PA ｍ From the frequency direction, 1 / (2 × IN ＡＶ ) A region shifted by only [Hz] (hereinafter referred to as "shift region SA") ｍ This is called the "shift region amplitude summation (SUM)". (m is a natural number greater than or equal to 1) The amplitudes of the frequency spectrum FS1 in this region are summed up to form the shift region amplitude summation (SUM). ＳＡ Calculate the shift area SA. ｍ The width is, for example, 1 / IN ＡＶ The frequency range can be approximately 15-25% of [Hz]. 【0068】 The noise detection unit 33 then calculates the peak ratio R using the following equation (2): R = SUM ＰＡ / SUM ＳＡ ... (2) 【0069】 As shown in equation (2), the peak ratio R is the sum of peak region amplitudes SUM ＰＡ SUM of the shift region amplitude ＳＡ This is the ratio to the peak region PA. ｍ The sum of the amplitudes of the frequency spectrum FS1 in the shift region SA ｍ The peak ratio R increases as the sum of the amplitudes of the frequency spectrum FS1 increases. That is, the peak ratio R is the sum of the spectral peaks SP in the frequency spectrum FS1. ｍ Peak region PA ｍ It will be a large value when it is clearly expressed in [the diagram / framework]. 【0070】 The noise determination unit 33 determines the peak ratio R and the threshold TH Ｒ Based on the comparison, it is determined whether the normalized signal NS1 is a low-noise signal. Specifically, for example, if the peak ratio R is the threshold TH Ｒ If the above conditions are met, the normalized signal NS1 is determined to be a low-noise signal, and the peak ratio R is equal to the threshold TH. Ｒ If it is smaller than this, the normalized signal NS1 is determined not to be a low-noise signal. 【0071】The noise determination unit 33 performs the above determination process for each of the normalized signals NS1 to NS4 and determines whether each of the normalized signals NS1 to NS4 is a low-noise signal. 【0072】 Furthermore, if the noise determination unit 33 determines that none of the normalized signals NS1 to NS4 for the target period PR2 are low-noise signals, the control unit 30 may not perform the signal selection process S4 and subsequent steps using the normalized signals NS1 to NS4 within the target period PR2, and may wait until the normalized signals NS1 to NS4 for the next period are obtained. This is because if none of the normalized signals NS1 to NS4 for a given period are low-noise signals, it is difficult to accurately acquire the heart rate information of the subject S using those normalized signals NS1 to NS4. 【0073】 [Signal Selection Process S4] In the signal selection process S4, the signal selection unit 34 selects a signal to be used for acquiring heart rate information from among the signals that were determined to be low-noise signals in the noise determination process S3. 【0074】 Any signal determined to be a low-noise signal in the noise determination step S3 is suitable for acquiring heart rate information. However, if multiple low-noise signals exist, it may be possible to acquire heart rate information with higher accuracy by performing noise reduction processing (details described later) using these multiple low-noise signals. Alternatively, if noise reduction processing is not performed, it is desirable to select the one signal that is most suitable for acquiring heart rate information from among the multiple low-noise signals. 【0075】 Therefore, if the signal selection unit 34 determines in the noise determination step S3 that any of the normalized signals NS1 to NS4 is a low-noise signal, it selects a signal from that signal to be used for acquiring heart rate information. If the signal selection unit 34 selects multiple signals to be used for acquiring heart rate information, the control unit 30 executes the noise reduction step S5 using the multiple signals. If the signal selection unit 34 selects a single signal to be used for acquiring heart rate information, the control unit 30 executes the heart rate information acquisition step S6 using the single signal. 【0076】The signal selection unit 34 selects the signal to be used for acquiring heart rate information based on the kurtosis of the normalized signals NS1 to NS4. Kurtosis is a concept that indicates the characteristics of a probability distribution; a probability distribution with high kurtosis shows a sharp peak near the mean, and the lower the kurtosis, the gentler the peak becomes. 【0077】 Here, the kurtosis K of a waveform signal represented by n sample data xi (where i is a natural number between 1 and n) is calculated by the following equation (3), where xa is the average value of the n sample data xi. 【0078】 The kurtosis of the BCG waveforms shown in Figures 5 and 10(a) is 4.6 (the difference in waveforms between the two figures is due to the difference in the amplitude axis scale). Furthermore, the kurtosis of the Gaussian noise shown in Figure 10(b) is 3.0, the kurtosis of the sine wave shown in Figure 10(c) is 1.5, and the kurtosis of the sawtooth wave shown in Figure 10(d) is 1.8. The BCG waveform is a J-peak PK Ｊ Because this peak is more prominent than others and has a higher degree of anomaly, the kurtosis of a typical BCG waveform without noise will be greater than 4.0. As the amount of Gaussian noise mixed in with the BCG waveform increases, the kurtosis of the BCG waveform approaches 3.0. 【0079】 The smaller the difference in kurtosis between multiple signals, the better the noise reduction process (details described later) using those multiple signals can be performed. Also, if noise reduction processing is not performed, the higher the kurtosis of the signal used for information acquisition, the better the acquisition of heart rate information (details described later) can be performed. Therefore, the signal selection unit 34 selects the signal with the highest kurtosis from among the low-noise signals. If there is at least one other signal suitable for performing noise reduction processing together with that signal, the unit selects that signal and the other at least one signal as the signals to be used for acquiring heart rate information. If there is no other signal suitable for performing noise reduction processing together with that signal, the unit selects only that signal as the signal to be used for acquiring heart rate information. 【0080】 As an example, the signal selection unit 34 executes the signal selection process S4 according to the flowchart in Figure 11. 【0081】 ​The signal selection unit 34 first calculates the kurtosis K of each of the normalized signals NS1 to NS4 in the target period PR2 (step S41). The signal selection unit 34 divides the target period PR2 into multiple periods for each of the normalized signals NS1 to NS4 and calculates the kurtosis K for each of the multiple periods after division. Then, the median value of the multiple kurtosis K calculated for each of the multiple periods is taken as the value of kurtosis K for the target period PR2. In this way, by dividing the target period PR2 into multiple periods and taking the median value of the multiple kurtosis K corresponding to each of these multiple periods as the kurtosis K for the target period PR2, the influence of abnormal values ​​that may be included in the signal of the target period PR2 due to the body movements of the subject S, etc., can be reduced, and kurtosis K can be calculated more accurately. 【0082】 Next, the signal selection unit 34 sets the difference K(1) - K(2) between the kurtosis K(1) of the signal S(1) with the largest kurtosis K among the normalized signals NS1 to NS4 and the kurtosis K(2) of the signal S(2) with the second largest kurtosis K to be the threshold TH Ｋ The signal selection unit 34 compares the difference K(1) - K(2) with the threshold TH. Ｋ That is the case (or threshold TH Ｋ If it is determined that the threshold TH is greater than (S42: NO), then signal S(1) is selected as the information acquisition signal IS to be used to acquire heart rate information (step S48). Ｋ This can be set arbitrarily, but as an example, it can be set to around 0.2 to 0.4. 【0083】 The signal selection unit 34 determines that the difference K(1) - K(2) is the threshold TH Ｋ Smaller (or threshold TH Ｋ If it is determined that the following is true (S42: YES), the difference K(1) - K(3) between the kurtosis K(1) of the signal S(1) with the largest kurtosis K among the normalized signals NS1 to NS4 and the kurtosis K(3) of the signal S(3) with the third largest kurtosis K is set as the threshold TH Ｋ The signal selection unit 34 compares the difference K(1)-K(3) with the threshold TH. Ｋ That is the case (or threshold TH Ｋ If it is determined that the value is greater than (S43: NO), then signals S(1) and S(2) are selected as information acquisition signals IS (step S47). 【0084】 When the signal selection unit 34 determines that the difference K(1) - K(3) is less than the threshold TH Ｋ (or equal to or less than the threshold TH Ｋ ), in the case of determination (S43: YES), the difference K(1) - K(4) between the sharpness K(1) of the signal S(1) with the largest sharpness K among the normalized signals NS1 to NS4 and the sharpness K(4) of the signal S(4) with the fourth largest sharpness K is compared with the threshold TH Ｋ . When the signal selection unit 34 determines that the difference K(1) - K(4) is greater than or equal to the threshold TH Ｋ (or greater than the threshold TH Ｋ ), in the case of determination (S44: NO), the signals S(1), S(2), and S(3) are selected as the information acquisition signals IS (step S46). 【0085】 When the signal selection unit 34 determines that the difference K(1) - K(4) is less than the threshold TH Ｋ (or equal to or less than the threshold TH Ｋ ), in the case of determination (S44: YES), all of the signals S(1) to S(4), that is, all of the normalized heartbeat signals NS1 to NS4, are selected as the information acquisition signals IS (step S45). 【0086】 In addition, when there is only one signal determined as a low-noise signal in the noise determination step S3 and the value of the sharpness K(2) of the signal S(2) with the second largest sharpness K does not exist in step S42, the signal selection unit 34 determines that the difference K(1) - K(2) is greater than or equal to the threshold TH Ｋ (or greater than the threshold TH Ｋ ). The same applies when there are only two signals determined as low-noise signals in the noise determination step S3 and the value of the sharpness K(3) of the signal S(3) with the third largest sharpness K does not exist in step S43, and when there are only three signals determined as low-noise signals in the noise determination step S3 and the value of the sharpness K(4) of the signal S(4) with the fourth largest sharpness K does not exist in step S44. 【0087】 [Noise reduction step S5] The noise reduction step S5 is executed by the noise reduction unit 35 when a plurality of information acquisition signals IS are selected in steps S45 to S47 of the signal selection step S4. 【0088】 In the noise reduction process S5, the noise reduction unit 35 performs noise reduction using at least two information acquisition signals IS selected in the signal selection process S4. 【0089】 The noise reduction unit 35 reduces noise using Principal Component Analysis (PCA). Principal Component Analysis is a method that reduces the dimensionality of multidimensional data by finding the direction (principal component direction) in which the multidimensional data has a large variance and projecting the data points in the principal component direction. 【0090】 As an example of a specific process, we will explain the case where noise reduction processing is performed using the normalized signal NS1 shown in Figure 12(a) and the normalized signal NS2 shown in Figure 12(b). The normalized signal NS1 in Figure 12(a) and the normalized signal NS2 in Figure 12(b) each contain noise (for example, Gaussian noise). 【0091】 The noise reduction unit 35 first performs principal component analysis on the normalized signal NS1 and the normalized signal NS2 (i.e., on the two-dimensional data) to obtain the distribution of data points. 【0092】 The distribution of data points obtained by principal component analysis is shown in Figure 12(c). In Figure 12(c), each circle represents a single data point. Each data point represents a combination of the amplitude values ​​of the normalized signal NS1 and the amplitude values ​​of the normalized signal NS2 at a given time. 【0093】 Next, the noise reduction unit 35 determines the direction of the greatest variance (i.e., the principal component direction) of the normalized signal NS1 and the normalized signal NS2 (i.e., the two-dimensional data). In Figure 12(c), the PCA axis X ＰＣＡ The direction indicated by this signifies the principal component direction. 【0094】 As can be seen from Figure 12(c), the data points are on the PCA axis X. ＰＣＡ Although it spreads most widely in the direction of the PCA axis X ＰＣＡ Instead of aligning upwards, PCA axis X ＰＣＡThe noise is also distributed in directions orthogonal to the given direction. This is because both the normalized signal NS1 in Figure 12(a) and the normalized signal NS2 in Figure 12(b) contain noise. 【0095】 Therefore, the noise reduction unit 35 uses the data points shown in Figure 12(c) along the PCA axis X ＰＣＡ The PCA axis X is aligned in a direction perpendicular to it. ＰＣＡ The data is projected upwards. This reduces the dimensionality of the multidimensional data and mitigates the effects of noise. The distribution of data points after dimensionality reduction is shown in Figure 13(b). 【0096】 Subsequently, the noise reduction unit 35 generates a single information acquisition signal IS (Figure 13(a)) with reduced noise based on the distribution of data points after dimensionality reduction. 【0097】 In this way, the noise reduction unit 35 performs principal component analysis on multiple normalized signals (i.e., multidimensional data) to obtain the distribution of data points. Then, the PCA axis X ＰＣＡ Determine the direction (principal component direction) and set the data points to the PCA axis X ＰＣＡ The PCA axis X is aligned in a direction perpendicular to it. ＰＣＡ The data is projected onto the image. Then, a single signal (i.e., one-dimensional data) is generated based on the distribution of the projected data points. The generated signal is a single information acquisition signal IS in which the noise of multiple information acquisition signals IS is reduced, while maintaining the tendency for multiple information acquisition signals IS to be shared. 【0098】 Furthermore, the noise reduction unit 35 performs noise reduction processing in the same procedure as described above, regardless of the number of information acquisition signals IS used in noise reduction process S5 (i.e., the number of dimensions of the multidimensional data). The larger the number of information acquisition signals IS used in noise reduction process S5 (i.e., the number of dimensions of the multidimensional data), the greater the noise reduction effect. 【0099】 [Heart rate information acquisition process S6] In the heart rate information acquisition process S6, the heart rate information acquisition unit 36 ​​of the control unit 30 acquires the heart rate information of the subject S using the information acquisition signal IS selected in step S48 of the signal selection process S4, or the information acquisition signal IS generated in the noise reduction process S5. 【0100】An example of the specific operation of the heart rate information acquisition process S6 will be explained using the case where the heart rate interval is acquired as the heart rate information of subject S. 【0101】 The heart rate information acquisition process S6 includes the BCG signal extraction process S61, the template creation process S62, and the heart rate interval acquisition process S63, as shown in the flowchart in Figure 14. 【0102】 In the BCG signal extraction step S61, the heart rate information acquisition unit 36 ​​extracts the BCG signal BS, which represents the BCG waveform, from the information acquisition signal IS (Figure 15(a)) for the target period PR3. The target period PR3 may be the same period as the target period PR2, a period obtained by combining multiple target periods PR2 into one, or one of several periods obtained by dividing a single target period PR2 into multiple parts. In addition, the target period PR3 can be set arbitrarily. 【0103】 The heart rate information acquisition unit 36 ​​first performs peak detection and identifies the J peak PK of the BCG waveform included in the information acquisition signal IS. Ｊ Estimated J-peak EPK that is estimated to show Ｊ The heart rate information acquisition unit 36, similar to the noise determination unit 33 in the noise determination process S3, performs the following (1) to (3) to estimate the J-peak EPK. Ｊ Detects. 【0104】 (1) Peak detection processing is performed on the information acquisition signal IS for the target period PR3, and all peaks included in the information acquisition signal IS for the target period PR3 are detected. 【0105】 (2) Calculate the average value of the amplitude of all peaks detected in (1), and set a threshold value that is twice the average value. 【0106】 (3) The peak detection process is performed again on the information acquisition signal IS for the target period PR3, and the peak with an amplitude larger than the threshold set in (2) is estimated as the J-peak EPK. Ｊ It is detected as such. 【0107】 Next, the heart rate information acquisition unit 36 ​​detects the estimated J-peak EPK Ｊ A predetermined period centered around (for example, EPK) Ｊ The average heart rate interval IN calculated from the median of the intervals.ＡＶ The signal during the period (0.7 seconds to 2.5 seconds) is extracted as the BCG signal BS. The heart rate information acquisition unit 36 ​​then extracts one estimated J peak EPK from the detected Ｊ One BCG signal BS is extracted for each. Therefore, the heart rate information acquisition unit 36 ​​extracts the estimated J peak EPK detected within the target period PR3. Ｊ Multiple BCG signals BS are extracted according to the number. 【0108】 In the template creation process S62, the heart rate information acquisition unit 36 ​​creates a template signal TS based on the multiple BCG signals BS extracted in the BCG signal extraction process S61. Specifically, the heart rate information acquisition unit 36 ​​uses the multiple BCG signals BS extracted in the BCG signal extraction process S61 to calculate the average value of the amplitudes of the multiple BCG signals BS for each time point, and sets the signal represented by the average value of the amplitudes of the multiple BCG signals BS at each time point as the template signal TS. The waveform shape represented by the template signal TS is the average shape of the multiple waveforms represented by the multiple BCG signals BS extracted in the BCG signal extraction process S61. 【0109】 In the heart rate interval acquisition process S63, the heart rate information acquisition unit 36 ​​performs template matching between the template signal TS created in the template creation process S62 and the information acquisition signal IS for the target period PR3. Specifically, for example, it calculates the cross-correlation function between the template signal TS and the information acquisition signal IS. The calculated cross-correlation function shows a peak at the timing when the degree of agreement between the template signal TS and the information acquisition signal IS is highest. Therefore, based on the peak of the calculated cross-correlation function, the heart rate information acquisition unit 36 ​​selects the J peak PK included in the information acquisition signal IS. Ｊ Identify the J-peak PK. Ｊ Obtain the heart rate interval based on this. 【0110】 The peak interval of the calculated cross-correlation function is the J peak PK of the IS signal used for information acquisition. ＪThis becomes equal to the interval. Therefore, the heart rate information acquisition unit 36 ​​may acquire the peak interval of the calculated cross-correlation function as the heart rate interval of subject S. The heart rate information acquisition unit 36 ​​may also calculate the average heart rate interval based on the multiple acquired heart rate intervals. 【0111】 [Display Step S7] In display step S7, the control unit 30 displays the heart rate interval acquired by the heart rate information acquisition unit 36 ​​on the display unit 40. In addition to displaying using the display unit 40, or instead, notification may be provided using the notification unit 50 in display step S7. In this case, for example, the control unit 30 emits a notification sound when the heart rate interval of the subject S deviates from a predetermined range, and notifies the user of the biological information acquisition system 100, such as a doctor, nurse, or caregiver, of the abnormal heart rate condition. 【0112】 The effects of the biological information acquisition system 100 of this embodiment are summarized below. 【0113】 In this embodiment, the biological information acquisition system 100 has a signal selection unit 34 that selects from normalized signals NS1 to NS4, based on the kurtosis of the signals, a plurality of information acquisition signals IS to be used in the noise reduction process S5, or a single information acquisition signal IS to be used in the heart rate information acquisition process S6. This makes it possible to select a plurality of information acquisition signals IS suitable for the execution of the noise reduction process S5, or a single information acquisition signal IS suitable for the execution of the heart rate information acquisition process S6. Therefore, the biological information acquisition system 100 of this embodiment can acquire highly accurate heart rate information of the subject S. 【0114】 In this embodiment of the biological information acquisition system 100, the noise determination unit 33 determines whether each of the normalized signals NS1 to NS4 is a low-noise signal. The signal selection unit 34 then selects from among the signals determined to be low-noise signals from the normalized signals NS1 to NS4 either a plurality of information acquisition signals IS to be used in the noise reduction process S5 or a single information acquisition signal IS to be used in the heart rate information acquisition process S6. Therefore, the biological information acquisition system 100 of this embodiment can acquire more accurate heart rate information of the subject S. 【0115】In this embodiment of the biological information acquisition system 100, when the signal selection unit 34 selects a plurality of information acquisition signals IS from normalized signals NS1 to NS4 whose signal kurtosis values ​​are close to each other, the noise reduction unit 35 performs a noise reduction process S5 using the plurality of information acquisition signals IS. Therefore, the biological information acquisition system 100 of this embodiment can acquire more accurate heart rate information of the subject S. 【0116】 In this embodiment, the biological information acquisition system 100 has a body movement determination unit 32 that determines the standard deviation σ of each divided period of the normalized signals NS1 to NS4. ＮＳ１－ｎ ~σ ＮＳ４－ｎ The minimum value σ for (n = 1, 2, 3, ..., N) ＭＩＮ and threshold TH σ Based on this comparison, it is determined whether or not subject S is experiencing body movement. This allows for highly accurate detection of subject S's body movement. 【0117】 <Modification> In the biological information acquisition system 100 of the above embodiment, the following modified forms can also be used. 【0118】 [Modified Version of Load Signal Acquisition Process S1] In the load signal acquisition process S1 of the above embodiment, the load signal acquisition unit 31 does not need to perform frequency filtering and / or normalization processing on at least one of the load signals LS1 to LS4. 【0119】 In the load signal acquisition step S1 of the above embodiment, the load signal acquisition unit 31 may separate the signals by performing component analysis on at least one of the load signals LS1 to LS4, and identify the heart rate component from the separated signals. The component analysis can be performed, for example, by independent component analysis (ICA). The heart rate component can be identified from the separated signals, for example, by identifying the one with the maximum kurtosis as the heart rate component. By performing the body movement determination step S2 and subsequent steps using the identified heart rate component, heart rate information can be acquired with higher accuracy. 【0120】In the load signal acquisition step S1 of the above embodiment, the load signal acquisition unit 31 may invert the phase of at least one of the load signals LS1 to LS4. The load signals LS1 and LS3 from the load detectors 11 and 13, which are positioned with the subject S in between, are often signals with opposite phases to each other. Similarly, the load signals LS2 and LS4 from the load detectors 12 and 14, which are positioned with the subject S in between, are often signals with opposite phases to each other. Therefore, for example, by inverting the phases of the load signals LS3 and LS4 to match the phases of the load signals LS1 and LS2 and performing the processing from the body movement determination step S2 onward, heart rate information can be acquired with higher accuracy. Note that both phase inversion in the load signal acquisition unit 31 and phase inversion based on the positive / negative determination of the waveform in the noise determination unit 33 may be performed, or only one of them may be performed. 【0121】 In the above embodiment, the load signal acquisition unit 31 acquires normalized signals NS1 to NS4, and the control unit 30 uses the normalized signals NS1 to NS4 to perform the processes from the body motion determination step S2 onward. However, this is not the only embodiment. 【0122】 The load signal acquisition unit 31 may generate a composite normalized signal by adding any two of the normalized signals NS1 to NS4. Specifically, for example, the load signal acquisition unit 31 may calculate a composite normalized signal CNS12 by adding normalized signal NS1 and normalized signal NS2, and a composite normalized signal CNS34 by adding normalized signal NS3 and normalized signal NS4. In this case, the control unit 30 may use the normalized signals NS1 to NS4 and the composite normalized signals CNS12 and CNS34 to perform the processes from the body motion determination step S2 onward. 【0123】Alternatively, the load signal acquisition unit 31 may calculate at least one of the six composite normalization signals: composite normalization signal CNS12, composite normalization signal CNS34, composite normalization signal CNS14 obtained by adding normalization signal NS1 and normalization signal NS4, composite normalization signal CNS23 obtained by adding normalization signal NS2 and normalization signal NS3, composite normalization signal CNS13 obtained by adding normalization signal NS1 and normalization signal NS3, and composite normalization signal CNS24 obtained by adding normalization signal NS2 and normalization signal NS4. In this case, the control unit 30 may use the normalization signals NS1 to NS4 and the calculated at least one composite normalization signal to perform the processes from the body motion determination step S2 onward. 【0124】 Calculating such a composite normalized signal and using it in the processing from the body motion detection step S2 onward has the following significance. 【0125】 The waveform of one cycle of the BCG signal, as shown in Figure 5, reflects the movement of the subject S's center of gravity toward the head in response to the contraction of the subject S's heart and the pumping of blood toward the head, and then the subsequent movement of the subject S's center of gravity toward the legs in response to the circulation of blood throughout the subject S's body. In other words, the BCG signal is a signal based on the fluctuation component of the subject S's center of gravity G in the direction along the subject S's body axis (i.e., the direction along the subject S's spine), as shown in Figure 16(a). 【0126】 In contrast, the center of gravity G of subject S fluctuates in directions different from the direction of subject S's body axis, in accordance with the movement of the chest, abdomen, etc., based on subject S's breathing, etc. The fluctuation component of subject S's center of gravity G that is in a direction different from subject S's body axis is noise in the normalized signals NS1 to NS4, which include the BCG signal, and may remain even after filtering. 【0127】Here, as shown in Figure 16(a), the fluctuations in the output values ​​of load detectors 11 to 14 when subject S is lying supine on bed BD with his body axis aligned with the longitudinal direction of bed BD are as shown in the table in Figure 16(b). Specifically, when subject S's center of gravity G moves towards the leg side in the longitudinal direction of bed BD, the output values ​​of load detectors 11 and 12 shift to the positive side, and the output values ​​of load detectors 13 and 14 shift to the negative side. Conversely, when subject S's center of gravity G moves towards the head side in the longitudinal direction of bed BD, the output values ​​of load detectors 11 and 12 shift to the negative side, and the output values ​​of load detectors 13 and 14 shift to the positive side. 【0128】 Similarly, if the center of gravity G of subject S moves to the right side in the width direction of bed BD, the output values ​​of load detectors 11 and 14 will shift to the positive side, and the output values ​​of load detectors 12 and 13 will shift to the negative side. Conversely, if the center of gravity G of subject S moves to the left side in the width direction of bed BD, the output values ​​of load detectors 11 and 14 will shift to the negative side, and the output values ​​of load detectors 12 and 13 will shift to the positive side. Also, if the center of gravity G of subject S moves downward in the vertical direction, the output values ​​of load detectors 11 to 14 will shift to the positive side, and if the center of gravity G of subject S moves upward in the vertical direction, the output values ​​of load detectors 11 to 14 will shift to the negative side. 【0129】 Thus, the output values ​​of load detectors 11 and 12, which are positioned symmetrically with respect to the Y-axis (i.e., the central axis of bed BD extending in the longitudinal direction of bed BD), increase or decrease when the center of gravity G of subject S moves in the longitudinal direction, and increase and decrease when the center of gravity G of subject S moves in the width direction. A similar relationship holds for the output values ​​of load detectors 13 and 14, which are positioned symmetrically with respect to the Y-axis. Therefore, in the composite normalized signal CNS12, which is the sum of the normalized signal NS1 based on the output value of load detector 11 and the normalized signal NS2 based on the output value of load detector 12, the longitudinal fluctuation component of the center of gravity G of subject S is added together and becomes larger, while the width fluctuation component is canceled out and becomes smaller. The same applies to the composite normalized signal CNS34, which is the sum of the normalized signal NS3 based on the output value of load detector 13 and the normalized signal NS4 based on the output value of load detector 14. 【0130】In other words, when subject S is lying supine on bed BD with their body axis aligned with the longitudinal direction of bed BD, the composite normalized signals CNS12 and CNS34 each better reflect the BCG signal based on the fluctuation component of the center of gravity G in the direction along the body axis than the normalized signals NS1 to NS4. Therefore, when the control unit 30 processes a total of six signals, including the normalized signals NS1 to NS4 plus the composite normalized signals CNS12 and CNS34, from the body movement determination step S2 onward, for example, the signal with a higher signal-to-noise ratio is selected in the signal selection step S4, and more accurate heart rate information is obtained in the heart rate information acquisition step S6. 【0131】 Furthermore, if the orientation of the subject S's body axis coincides with the width direction of the bed BD, the composite normalized signals CNS14 and CNS23 better reflect the BCG signal. Also, if the orientation of the subject S's body axis coincides with the diagonal direction of the bed BD, the composite normalized signal CNS13 or composite normalized signal CNS24 better reflects the BCG signal. Therefore, by performing subsequent processing including these signals, regardless of the orientation of the subject S on the bed BD, the signal with a higher signal-to-noise ratio is selected in the signal selection step S4, and more accurate heart rate information is obtained in the heart rate information acquisition step S6. 【0132】 In the above method, a composite normalized signal is obtained by adding two of the normalized signals NS1 to NS4, but this is not the only method. Alternatively, a composite signal may be calculated by adding two of the weight signals LS1 to LS4 before or after filtering, and the calculated composite signal may be subjected to normalization processing to obtain a composite normalized signal. 【0133】 [Modified Version of Body Movement Determination Step S2] In the body movement determination step S2 of the above embodiment, the body movement determination unit 32 can determine whether or not body movement is occurring in the subject S in any manner. 【0134】The body movement determination unit 32 may divide one of the normalized signals NS1 to NS4 of the target period PR1 into multiple signals along the time axis, and calculate the standard deviation for each of these multiple signals. Then, it may compare the minimum value of the multiple standard deviations corresponding to each of these multiple signals with a threshold value, and determine, for example, that body movement occurred in the subject S during the target period PP1 if the minimum value is smaller than the threshold value. 【0135】 The normalized signals NS1 to NS4 for the target period PR1 are each divided into multiple signals by time, and the presence or absence of subject S movement is determined by comparing the minimum value of the multiple standard deviations of these multiple signals with a threshold, thereby determining whether the signal is affected by subject S's movement for each signal. If the influence of subject S's movement is small in at least one of the normalized signals NS1 to NS4 (for example, if the minimum value of the multiple standard deviations in that signal is greater than or equal to the threshold), the control unit 30 may use that at least one signal to perform the noise determination process S3 and subsequent processes. Multiple signals obtained by dividing any one of the normalized signals NS1 to NS4 along the time axis are an example of "multiple signals that each contain a component that fluctuates according to the movement of the subject on the bed and are normalized." 【0136】 The load signals LS1 to LS4 in the load signal acquisition unit 31 can be normalized by various known methods. If the normalization method does not include setting the standard deviation in the target period PR1 to 1, the body movement determination unit 32 may calculate the standard deviation of each of the normalized signals NS1 to NS4 in the target period PR1, and determine that body movement has occurred in subject S if at least one of the calculated standard deviations (e.g., the minimum value) is greater than or equal to a threshold. 【0137】 In the above embodiment, the body motion determination unit 32 and the body motion determination step S2 may be omitted. 【0138】[Modified Version of Noise Determination Step S3] In the noise determination step S3 of the above embodiment, the noise determination unit 33 can determine the amount of noise contained in each of the normalized signals NS1 to NS4 by any step based on frequency analysis. The frequency analysis may include a method that includes analysis of the frequency component ratio. 【0139】 In the above embodiment, the noise determination unit 33 determines the two adjacent estimated J-peak EPKs for each of the normalized signals NS1 to NS4 of the target period PR2. Ｊ Average heart rate interval between IN ＡＶ The noise determination unit 33 calculates, but is not limited to, two estimated J-peak EPKs. Ｊ The peak region of the frequency spectrum PA is determined based on the interval IN between the intervals IN. ｍ You may specify it. 【0140】 In the above embodiment, the noise determination unit 33 calculates the peak region amplitude sum SUM. ＰＡ SUM of the shift region amplitude ＳＡ The peak ratio R, which is the ratio to the noise, is calculated, but is not limited to this. The noise determination unit 33 calculates, for example, the peak region amplitude sum SUM. ＰＡ Instead, 1 / IN in the frequency direction from the position where the frequency is 0 [Hz]. ＡＶ The peak position amplitude sum may be calculated by summing the amplitudes of the frequency spectrum at each position [Hz]. In addition, the noise determination unit 33 calculates the shift region amplitude sum SUM ＳＡ Instead, an arbitrary distance in the frequency direction from the peak position (for example, 1 / (2 × IN) ＡＶ Alternatively, the sum of the frequency spectrum amplitudes at the shifted position, shifted by ) [Hz], may be calculated to obtain the sum of shifted position amplitudes. The ratio of the sum of peak position amplitudes to the sum of shifted position amplitudes may then be calculated as the peak ratio R. 【0141】 In the above embodiment, the peak region PA ｍ From 1 / (2 × IN) ＡＶ The region including the shifted position shifted by [Hz] is called the shift region SA. ｍ However, this is not the only option. Shift area SA ｍ This is the peak region PA ｍThis can be a region shifted by an arbitrary distance in the frequency direction from the original region. 【0142】 In the above embodiments and modifications, a power spectrum may be used instead of an amplitude spectrum. In this case, the sum of powers is calculated instead of the sum of amplitudes. In this disclosure, the amplitude of the spectrum and the power of the spectrum are collectively referred to as the intensity of the spectrum vector. 【0143】 In the above embodiment, the noise determination unit 33 and the noise determination step S3 may be omitted. 【0144】 [Modified Version of Signal Selection Process S4] In the signal selection process S4 of the above embodiment, the signal selection unit 34 can select the information acquisition signal IS in any manner based on the kurtosis of each of the normalized signals NS1 to NS4. 【0145】 The signal selection unit 34 compares the kurtosis of each of the normalized signals NS1 to NS4 with a threshold and may select the signal among the normalized signals NS1 to NS4 whose kurtosis is greater than or equal to the threshold as the information acquisition signal IS. If there are multiple selected signals, the unit may determine whether or not to perform the noise reduction process S5 using the multiple signals. If it is determined that the noise reduction process S5 should not be performed, the signal with the largest kurtosis among the selected signals may be selected as the information acquisition signal IS. On the other hand, if the kurtosis of each of the normalized signals NS1 to NS4 is less than the threshold, the unit may wait until the next timing without selecting any signal, or it may select the signal among the normalized signals NS1 to NS4 with the largest kurtosis as the information acquisition signal IS. Not selecting any signal as the information selection signal is also one way of selecting an information selection signal. 【0146】 The signal selection unit 34 may simply select the signal with the largest kurtosis among the normalized signals NS1 to NS4, or the signal with the largest kurtosis among the normalized signals NS1 to NS4 that is also above a threshold, as the information acquisition signal IS. In this embodiment, the control unit 30 does not perform the noise reduction process S5. 【0147】In the above embodiment, the signal selection unit 34 uses the median value of the kurtosis K of each of the multiple periods obtained by dividing the target period PR2 for each of the normalized signals NS1 to NS4 as the value of kurtosis K in the target period PR2, but is not limited to this. The signal selection unit 34 may calculate a single kurtosis K in the target period PR2 and select the information acquisition signal IS based on that kurtosis K. 【0148】 [Modified Version of Noise Reduction Process S5] In the noise reduction process S5 of the above embodiment, the noise reduction unit 35 can generate a single information acquisition signal IS from a plurality of information acquisition signals IS with reduced noise by any manner using principal component analysis. 【0149】 In the above embodiment, the noise reduction unit 35 and the noise reduction process S5 may be omitted. 【0150】 [Modified Version of Heart Rate Information Acquisition Process S6] In the heart rate information acquisition process S6 of the above embodiment, the heart rate information acquisition unit 36 ​​can acquire the heart rate information of the subject S in any manner based on the information acquisition signal IS. 【0151】 In the heart rate information acquisition step S6 of the above embodiment, the heart rate information acquisition unit 36 ​​may extract feature quantities related to the waveform of the peak of the information acquisition signal IS, identify a peak corresponding to the heart rate of the subject S from the peak of the information acquisition signal IS based on the classification of said feature quantities, and acquire the heart rate information of the subject S based on the identified peak. 【0152】 Specifically, the extraction of feature quantities related to the waveform of the peaks of the information acquisition signal IS is performed, for example, by extracting the following feature quantities I to V for each peak indicated by the information acquisition signal IS. 【0153】 (1) Feature I: The difference in amplitude between the negative maximum value just before the peak and the peak itself. (2) Feature II: The amplitude of the negative maximum value just before the peak. (3) Feature III: The amplitude of the peak. (4) Feature IV: The time from the negative maximum value just before the peak to the peak. (5) Feature V: The time from the peak to the negative maximum value just after the peak. 【0154】Feature classification can be performed, for example, by k-means clustering. The heart rate information acquisition unit 36, for example, classifies a dataset containing feature quantities I to V for each peak into three classes by k-means clustering, and J Peak PK Ｊ The heart rate information acquisition unit 36 ​​then identifies a dataset that shows the J peak PK of the information acquisition signal IS based on the identified dataset. Ｊ Identify the identified Jpeak PK Ｊ The heart rate interval of subject S is obtained based on this. 【0155】 In the heart rate information acquisition step S6 of the above embodiment, the heart rate information acquisition unit 36 ​​may acquire heart rate information by extracting the section to be analyzed from the information acquisition signal IS and estimating the interval between the appearance of similar waveforms. 【0156】 Specifically, for example, the heart rate information acquisition unit 36 ​​first extracts analysis intervals from the waveform of the information acquisition signal IS. At this time, the heart rate information acquisition unit 36 ​​extracts multiple analysis intervals, each containing two cycles of waveform, with the centers of the intervals shifted from each other by Δt [s] (for example, 0.2 [s]). Then, the heart rate information acquisition unit 36 ​​estimates the intervals at which similar waveform patterns appear, so-called pitch tracking. 【0157】 The heart rate information acquisition unit 36 ​​may acquire heart rate information by calculating a plurality of correlation indices for estimating the intervals between similar waveforms, and calculating the probability that the estimated intervals represent heart rate intervals based on the calculated plurality of correlation indices. 【0158】Specifically, for example, the heart rate information acquisition unit 36 ​​calculates several correlation indices for estimating the interval between similar waveforms: the autocorrelation function in the information acquisition signal IS for the analysis interval, the average of the amplitude differences in the information acquisition signal IS for the analysis interval, and the maximum value of the sum of the amplitudes in the information acquisition signal IS for the analysis interval. The heart rate information acquisition unit 36 ​​calculates these three correlation indices, namely the autocorrelation function, the average of the amplitude differences, and the maximum value of the sum of the amplitudes, by estimating a predetermined period N. Then, based on the three calculated correlation indices, the heart rate information acquisition unit 36 ​​calculates the probability that the estimated predetermined period N is a heart rate interval. Specifically, this calculation is performed, for example, by calculating the most likely N using Bayes' theorem. 【0159】 The heart rate information acquisition unit 36 ​​may further perform the estimation of similar waveform occurrence intervals for intervals where the probability that the estimated predetermined period N (occurrence interval) represents a heart rate interval is lower than a set threshold. 【0160】 Specifically, for example, the heart rate information acquisition unit 36 ​​performs independent component analysis and separates the signals of multidimensional data represented by conditional probabilities, with the autocorrelation function, the mean of the amplitude difference, and the maximum value of the sum of the amplitudes as elements. This separates the low-accuracy intervals from the high-accuracy intervals in the multidimensional data. The heart rate information acquisition unit 36 ​​then performs heart rate interval estimation again in the low-accuracy intervals. 【0161】 In addition, the heart rate information acquisition unit 36 ​​may acquire information other than the heart rate interval as the heart rate information of the subject S. Specifically, for example, the heart rate information acquisition unit 36 ​​can acquire the heart rate of the subject S based on the heart rate interval. Alternatively, the heart rate information acquisition unit 36 ​​can acquire a value indicating the magnitude of the heartbeat based on the amplitude of the information acquisition signal IS. 【0162】 [Other Modifications] In the above embodiment, the control unit 30 can also be considered as a body motion detection device equipped with a body motion detection unit 32. When the control unit 30 is considered as a body motion detection device, the noise detection unit 33, signal selection unit 34, noise reduction unit 35, heart rate information acquisition unit 36, etc., can be omitted. 【0163】The biological information acquisition system 100 of the above embodiment does not necessarily need to include all of the load detectors 11 to 14, and may include only at least one of the load detectors 11 to 14. Furthermore, the load detectors do not necessarily need to be placed at the four corners of the bed, and can be placed at any position so as to be able to detect the load of the subject on the bed and its fluctuations. In addition, the load detectors 11 to 14 are not limited to load sensors using beam-type load cells, but can also be force sensors, for example. 【0164】 In the biological information acquisition system 100 of the above embodiment, the load detection unit 1 may be a plurality of pressure sensors arranged in a matrix shape under the sheet. 【0165】 The load detectors 11 to 14 may be integrated with the bed BD or detachably combined to form a bed system consisting of the bed BD and the biological information acquisition system 100 of this embodiment. 【0166】 As long as the features of the present invention are maintained, the present invention is not limited to the embodiments described above, and other forms conceivable within the scope of the technical idea of ​​the present invention are also included within the scope of the present invention. 【0167】 10: Load detection unit, 30: Control unit, 31: Load signal acquisition unit, 32: Body movement determination unit, 33: Noise determination unit, 34: Signal selection unit, 35: Noise reduction unit, 36: Heart rate information acquisition unit, 40: Memory unit, 50: Display unit, 60: Notification unit, 70: Input unit, BD: Bed, S: Subject

Claims

1. A heart rate information acquisition device for acquiring heart rate information of a subject lying in bed, comprising: a signal acquisition unit that acquires a plurality of signals, each containing a component that fluctuates according to the subject's heart rate; a signal selection unit that selects an information acquisition signal from the plurality of signals to be used for acquiring the heart rate information based on the kurtosis of each of the plurality of signals; and a heart rate information acquisition unit that acquires the subject's heart rate information based on the information acquisition signal.

2. The heart rate information acquisition device according to claim 1, further comprising a noise determination unit that determines the amount of noise contained in each of the plurality of signals based on the frequency analysis of each of the plurality of signals, wherein the signal selection unit selects the information acquisition signal from the signal determined to have a low amount of noise based on the kurtosis of the signal determined to have a low amount of noise among the plurality of signals.

3. The heart rate information acquisition device according to claim 2, wherein the noise determination unit determines the amount of noise in a target signal which is one of the plurality of signals by: calculating an estimated value of the subject's average heart rate interval based on the target signal; applying a Fourier transform to the target signal to obtain a frequency spectrum; obtaining the ratio of the sum of the intensities of a plurality of positions separated in the frequency direction by a distance corresponding to the calculated estimated value of the average heart rate interval in the obtained frequency spectrum to the sum of the amplitudes of a plurality of positions shifted in the frequency direction from the plurality of positions; and comparing the ratio with a threshold.

4. The heart rate information acquisition device according to claim 3, wherein the noise determination unit determines whether or not phase inversion of the target signal is necessary based on a comparison of the standard deviation of the target signal in a region where the amplitude of the target signal is greater than the average value of the amplitude of the target signal and the standard deviation of the target signal in a region where the amplitude of the target signal is smaller than the average value of the amplitude of the target signal, and calculates an estimated value of the average heart rate interval of the subject based on the detection of the peak of the phase-inverted target signal.

5. The heart rate information acquisition device according to any one of claims 1 to 4, further comprising a noise reduction unit that, when the signal selection unit selects two or more signals as the information acquisition signal, generates a single information acquisition signal from the two or more signals with reduced noise by principal component analysis.

6. The heart rate information acquisition device according to any one of claims 1 to 5, wherein the signal selection unit calculates a plurality of kurtosis values ​​corresponding to a plurality of periods for at least one of the plurality of signals, and sets the median value of the plurality of kurtosis values ​​as the kurtosis of the signal.

7. The heart rate information acquisition device according to any one of claims 1 to 6, wherein the signal acquisition unit applies frequency filtering to each of the plurality of signals.

8. The heart rate information acquisition device according to any one of claims 1 to 7, wherein the signal acquisition unit normalizes each of the plurality of signals.

9. The heart rate information acquisition device according to claim 8, further comprising a body movement determination unit that determines whether or not body movement is occurring in the subject based on a comparison between the minimum value of a plurality of standard deviations calculated for each of the plurality of normalized signals or a plurality of signals obtained by time-dividing any one of the plurality of normalized signals and a threshold, wherein if the body movement determination unit determines that body movement is occurring in the subject, the device does not acquire the heart rate information based on the signals for the period during which it was determined that body movement was occurring in the subject.

10. The heart rate information acquisition device according to any one of claims 1 to 9, wherein the signal acquisition unit acquires at least one composite signal, each of which is obtained by adding up two of the plurality of signals, and the signal selection unit selects the information acquisition signal from the plurality of signals and the at least one composite signal based on the kurtosis of each of the plurality of signals and the at least one composite signal.

11. The heart rate information acquisition device according to any one of claims 1 to 10, wherein the signal acquisition unit separates signals by performing component analysis on at least one of the plurality of signals and identifies a heart rate component from the separated signals.

12. The heart rate information acquisition device according to any one of claims 1 to 11, wherein the signal acquisition unit inverts the phase of at least one of the plurality of signals.

13. The heart rate information acquisition device according to any one of claims 1 to 12, wherein the heart rate information acquisition unit extracts feature quantities relating to the waveform of the peak of the information acquisition signal, identifies a peak corresponding to the subject's heart rate from the peak of the information acquisition signal based on the classification of the feature quantities, and acquires the heart rate information based on the identified peak.

14. The heart rate information acquisition device according to any one of claims 1 to 13, wherein the heart rate information acquisition unit acquires the heart rate information based on matching a template generated based on the peak of the information acquisition signal with the information acquisition signal.

15. The heart rate information acquisition device according to claim 14, wherein the heart rate information acquisition unit acquires the heart rate information based on the peak of the correlation function between the template and the information acquisition signal.

16. The heart rate information acquisition device according to any one of claims 1 to 15, wherein the heart rate information acquisition unit acquires the heart rate information by extracting a section to be analyzed from the information acquisition signal and estimating the interval between the appearance of similar waveforms.

17. The heart rate information acquisition device according to claim 16, wherein the heart rate information acquisition unit calculates a plurality of correlation indicators for estimating the occurrence interval of similar waveforms, and acquires the heart rate information by calculating the probability that the estimated occurrence interval represents a heart rate interval based on the calculated plurality of correlation indicators.

18. The heart rate information acquisition device according to claim 17, wherein the heart rate information acquisition unit performs the estimation of the occurrence interval of similar waveforms again for intervals in which the estimated occurrence interval is lower than a set threshold in which the probability of representing a heart rate interval is lower.

19. A method for acquiring heart rate information of a subject lying in bed, comprising: a signal acquisition unit acquiring a plurality of signals, each containing a component that fluctuates according to the subject's heart rate; a signal selection unit selecting an information acquisition signal from the plurality of signals based on the kurtosis of each of the plurality of signals to be used for acquiring the heart rate information; and a heart rate information acquisition unit acquiring the subject's heart rate information based on the information acquisition signal.

20. A body movement determination device comprising a body movement determination unit that determines whether or not body movement is occurring in a subject based on a comparison between the minimum value of a plurality of standard deviations calculated for a plurality of signals, each containing a component that fluctuates in accordance with the body movement of the subject on the bed and which are normalized, and a threshold value.

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