JP2005342188A - System for deciding mental and physical condition - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a system for surely and easily deciding the mental and physical conditions of a person based on the degree of the fluctuation of respiration and body movement of the respiration by not utilizing the vibration of the body surface with heart beats but detecting the variation of the body movement by the respiration which can be detected clearly. <P>SOLUTION: A seat 100 as a human body support means is provided with a respiration displacement detection sensor 10 where a detected value is varied with the respiration of the person sitting in the seat 100. The time series variation of a respiration frequency is obtained by slide calculation with measured data obtained from the detection sensor 10. A peak frequency obtained by frequency analysis is set as a threshold to decide the person to be in a sleeping state when the time series data of the respiration frequency is lower than the threshold. When the time series data of the degree of the body movement by respiration and that of the respiration frequency are considered together and a time when the conditions of both of them appears in an opposite phase is detected, sleeping sign before falling into sleep is allowed to be decided. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention is a vehicle seat for use in transportation equipment such as automobiles, trains, airplanes, office seats, various seats such as seats for people seated at the time of inspection or diagnosis in hospitals, etc., or a futon, A person supporting means used for supporting a person, such as a mattress, a bed, or the like, and a person who is actually supported by the human body supporting means, whether it is a sleeping state or not, or leads to sleep The present invention relates to a psychosomatic state determination system that can automatically determine whether or not it is the previous stage (at the time of sleep onset), and is particularly suitable for determining the state of a person supported by a vehicle seat.

  In order to detect a person's mind-body state, for example, an active state (awake state) or a sleep state, conventionally, an electroencephalogram is measured and an electroencephalogram pattern is analyzed. However, in order to measure an electroencephalogram, it is necessary to attach an electroencephalogram electrode or an electrooculogram electrode to the subject's head, for example, in an environment that restricts the normal operation of a person. It is difficult to evaluate the biological state at the time of driving various transport devices such as the above at a high academic level without placing a burden on the driver.

On the other hand, in recent years, monitoring the biological state (mental state of mind) of a driving driver has been attracting attention as an accident prevention measure. For example, Patent Document 1 and Patent Document 2 use a heartbeat or a pulse. A technique for monitoring the biological state by analyzing chaos is proposed. According to the techniques disclosed in Patent Documents 1 and 2, it is not necessary to attach a large-scale device for measuring an electroencephalogram to the head, and the biological state of the driver can be easily evaluated.
JP-A-9-308614 JP-A-10-146321

  Each of the devices disclosed in Patent Documents 1 and 2 senses vibrations of the body surface accompanying the pulsation of the heart with a pressure sensor attached to the seat portion of the seat. However, in actuality, it is extremely difficult to detect only the vibration of the body surface accompanying the pulsation of the person seated on the seat by the pressure sensor due to the vibration of the vehicle body while traveling. That is, even if an attempt is made to detect the vibration of the body surface due to the pulsation by such a pressure sensor, the pressure sensor sensitively detects the pressure change due to the vibration of the vehicle body. It is difficult to clearly distinguish Therefore, the above-described technique does not function correctly unless the environment is not easily affected by vibrations caused by external factors, and there is a problem in terms of practicality.

  The present invention has been made in view of the above points, and does not use the vibration of the body surface accompanying the pulsation of the heart, but detects a change in body movement due to respiration that can be detected more clearly. It is an object of the present invention to provide a system for determining the state of mind and body of a person based on the size of body movement caused by.

In the first aspect of the present invention, a respiratory displacement detection sensor attached to the human body support means, the detection value of which changes with the breathing of the person supported by the human body support means,
Based on the signal data obtained from the respiratory displacement detection sensor, time series fluctuation calculating means for calculating the time series fluctuation of the respiratory state,
Threshold analysis means for analyzing the frequency of the signal data obtained from the respiratory displacement detection sensor and obtaining a threshold using the peak frequency that is the maximum amplitude;
A respiratory psychosomatic state determining means for comparing the time series fluctuation of the respiratory state obtained by the time series fluctuation calculating means with the threshold obtained by the threshold calculating means to determine the person's psychosomatic state. Provided is a characteristic psychosomatic state determination system.
In the invention of claim 2, the time-series fluctuation calculating means includes a bandpass filter for extracting a predetermined frequency band including a peak frequency obtained by the threshold value calculating means,
The average value of the respiratory frequency in a predetermined time range of the maximum value of the waveform obtained by applying the band pass filter is obtained, and the obtained average value is calculated by sliding at a predetermined slide lap ratio to calculate the time series fluctuation of the respiratory frequency. 2. The psychosomatic state determination system according to claim 1, further comprising a respiration frequency time series data calculating means to be obtained.
According to a third aspect of the present invention, the respiratory psychosomatic state determining means compares the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculating means and falls below the threshold obtained by the threshold calculating means. 3. The psychosomatic state determination system according to claim 2, wherein the state is determined as a sleep state.
The invention according to claim 4 provides the psychosomatic state determination system according to claim 3, wherein the threshold value is the same as the peak frequency obtained by the threshold value calculation means.
In the invention of claim 5, the time-series fluctuation calculating means includes a bandpass filter that extracts a predetermined frequency band including a peak frequency obtained by the threshold value calculating means,
The number of maximum values of the waveform obtained by applying the bandpass filter in a predetermined time range is obtained as the number of breaths, and the obtained breathing number is calculated by sliding at a predetermined slide lap ratio to obtain the time series fluctuation of the number of breaths. The system according to claim 1, further comprising: a respiratory frequency time-series data calculation unit.
In the invention according to claim 6, the respiratory mind-and-body state determining means compares with the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculating means, and is below the threshold obtained by the threshold calculating means. 6. The psychosomatic state determination system according to claim 5, wherein the state is determined as a sleep state.
The invention according to claim 7 is characterized in that the threshold value is a value obtained by multiplying the peak frequency obtained by the threshold value calculation means by the time width of the predetermined time range obtained for the number of breaths. A psychosomatic state determination system according to claim 6 is provided.
In the invention according to claim 8, further, an average value in a predetermined time range of the maximum value of the waveform obtained by applying the band pass filter is obtained, and the obtained average value is slide-calculated at a predetermined slide lap ratio. Body movement magnitude time series data calculating means for obtaining time series fluctuations in the magnitude of body movement due to breathing,
In the respiratory psychosomatic state determining means, the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculating means and the body movement magnitude due to breathing obtained by the body movement magnitude time-series data calculating means. 3. The psychosomatic state determination system according to claim 2, wherein the time series data is compared with the series data and the timing at which the two time series data are in an antiphase state is determined as a sleep onset sign.
The invention according to claim 9 provides the psychosomatic state determination system according to any one of claims 1 to 8, wherein the respiratory displacement detection sensor is a sensor that detects pressure fluctuation due to human breathing. To do.
In the invention of claim 10, further comprising a pulse wave measuring device for measuring the pulse wave of the person supported by the human body support means,
A power value inclination calculating means for calculating a time series variation of the inclination of the power value of the pulse wave obtained from the pulse wave measuring device;
Maximum Lyapunov exponent inclination calculating means for calculating time series fluctuations of the gradient of the maximum Lyapunov exponent of the pulse wave obtained from the pulse wave measuring device;
The time series fluctuation of the power value slope obtained by the power value slope computing means and the time series fluctuation of the slope of the maximum Lyapunov exponent slope obtained by the maximum Lyapunov exponent slope computing means are compared, and two time series data Comprising a pulse wave psychosomatic state determination means that determines that the timing when the phase is in an antiphase state is a sleep onset sign,
9. The psychosomatic state determination system according to claim 8, wherein the respiratory psychosomatic state determination unit and the pulse wave psychosomatic state determination unit are used in combination to determine a sleep onset sign.
The invention according to claim 11 is characterized in that the pulse wave measuring device is a device for measuring at least one of a fingertip volume pulse wave or an earlobe pulse wave. provide.

According to the present invention, the respiratory state obtained by the time-series fluctuation calculating means is provided with the respiratory displacement detection sensor attached to the human body support means and whose detection value changes with the breathing of the person supported by the human body support means. This time-series variation is compared with the threshold value obtained by the threshold value calculation means, and the breathing psychosomatic state determination means for determining the mental and physical state of the person is provided. Changes in body movement due to human breathing can be detected more clearly compared to changes in the body surface associated with heart beat disclosed in Patent Document 1 and Patent Document 2. For this reason, a person's mind-body state, ie, whether it is a sleep state or an awake state, can be caught more reliably.
Further, the time series fluctuation calculating means slides the data obtained from the respiratory displacement detection sensor every predetermined time range time to obtain the time series data of the breathing frequency or the number of breaths. The effect of noise due to vibrations transmitted from the motor is small.
Further, by considering the time series data of the magnitude of body movement due to respiration and the time series data of the respiration frequency, it is possible to detect the timing of the onset of sleep before reaching sleep. Therefore, when it is attached to a vehicle seat such as an automobile, if it detects the timing of such a sleep symptom, it is configured to operate a warning device that tilts the seat back or emits a warning sound or the like. Then, the driver's consciousness can be returned to the awake state.
Further, a pulse wave psychosomatic state determining means for detecting a sleep onset sign by a change in the slope of the power value measured based on the fingertip volume pulse wave or the earlobe pulse wave, and the maximum Lyapunov exponent, and the respiratory mind and body described above By using together with the state determination means, any of them can be used as a backup system for determination of the mind and body state, and the reliability of the mind and body state determination system according to the present invention can be improved.

  Hereinafter, the present invention will be described in more detail based on embodiments shown in the drawings. FIGS. 1-3 is a schematic block diagram of the state which attached the mind-body state determination system 1 which concerns on one Embodiment of this invention to the vehicle seat 100 which is a load body support means. The psychosomatic state determination system 1 includes a calculation unit 20 that receives and analyzes signal data collected by the respiratory displacement detection sensor 10.

  As the respiratory displacement detection sensor 10, a pressure sensor can be used. However, since it is used by being attached to at least one of a seat cushion, a seat back, or a headrest, it is necessary to prevent a person from feeling a foreign object when sitting, and for example, a film-like piezoelectric element may be used as a pressure sensor. preferable. As the film-like piezoelectric element, for example, Tokyo Sensor Co., Ltd., product name: PIEZO FILM LDT series, model number: LDT4-028K / L can be used.

  In addition, when the respiratory displacement detection sensor 10 is attached to the seat cushion, only one piece may be disposed near the sciatic tubercle, but for example, a posture (sacrum) in which the buttocks are shifted forward by sitting for a long time. In addition to the sensor placed near the sciatic tubercle, one or more sensors may be placed in front of or behind the sensor. Can also be arranged. In addition, when detecting a body motion change due to respiration by using a seat 100 of an automobile or the like, it is preferable to use a pressure sensor as described above because a person can take a sample without sitting on a special measurement device.

  Here, the structure of the seat 100 is not limited, but each cushion structure of the seat cushion 120 and the seat back 140 can effectively transmit the muscle pressure fluctuation caused by human breathing to the respiratory displacement detection sensor 10. At the same time, it is preferable to have a high performance of floor vibration isolation function. 1 to 3 show an example of a preferable sheet 100 having such performance.

  That is, the seat cushion 120 of the seat 100 includes a torsion bar 122 at the rear part of the cushion frame 121, and supports the rear support frame 124 on the arm 123 urged backward by the torsion bar 122, thereby supporting the front part. A base cushion material 126 stretched between the frame 125 and the rear support frame 124 is provided. As shown by an imaginary line in FIG. 2, a surface cushion material 127 that is stretched on the cushion frame 121 with a low tension is provided on the base cushion material 126. Each of the base cushion material 126 and the surface cushion material 127 can be formed of a single cushion material, or can be formed by stacking a plurality of cushion materials as necessary.

  The respiratory displacement detection sensor 10 is provided between the base cushion material 126 and the surface cushion material 127. Since the base cushion material 126 has a structure in which tension is applied by the elastic force of the torsion bar 122, the base cushion material 126 dampens floor vibration. Thereby, the vibration transmission to the surface cushion material 127 is reduced. On the other hand, since the surface cushion material 127 is stretched to the cushion frame 121 with low tension, the human muscle (especially the buttocks muscle) is less compressed when seated, and blood vessel expansion / contraction exercise, breathing or body movement, etc. Does not interfere with muscle movement. Thereby, the mixing of the external vibration noise into the signal data collected by the respiratory displacement detection sensor 10 is reduced, and it becomes possible to collect the pressure fluctuation signal resulting from respiration more accurately.

  The surface cushion material 127 can be formed of a two-dimensional net material, a thin urethane material, or the like. However, in order to reduce the pressure on the buttocks muscle, the surface cushion material 127 is stretched on the cushion frame 121. It is preferable that the spring characteristics at this time be as close as possible to the spring characteristics of the buttocks muscle or the like. As the surface cushion material 127 having such characteristics, it is preferable that the applicant uses, for example, a three-dimensional knitted fabric with a small reaction force disclosed in JP-A-2002-336076. The three-dimensional knitted fabric is formed using, for example, a double raschel knitting machine or the like, and is knitted by reciprocating a connecting yarn between a pair of ground knitted fabrics positioned at a predetermined interval. As the base cushion material 126, a two-dimensional net material, a three-dimensional knitted fabric, or the like can be used as with the surface cushion material 127.

  The vibration isolation mechanism having a range of zero spring constant is not limited to the one made from the combination of the torsion bar 122 and the base cushion material 126 as in the present embodiment, and the Japanese Patent Application As disclosed in Japanese Patent Application Laid-Open No. 2003-139192 and Japanese Patent Application Laid-Open No. 2002-206594, it is configured by combining a repulsive force or attractive force of a permanent magnet and an elastic member such as a metal spring, and a spring at an equilibrium point of load mass. It can also be constituted by a seat suspension using a vibration isolation mechanism having a region where the constant is substantially zero.

  Further, as described above, the base cushion material 126 is stretched between the rear support frame 124 and the front support frame 125, and the surface cushion material 127 is stretched on the cushion frame 121 so as to cover the base cushion material 126. Since the structure is provided, it is preferable to provide a structure that supplements the restoring force during unloading. In FIG. 1 to FIG. 3, as such a structure, a hard auxiliary plate 128 made of a metal plate, synthetic resin, or the like is disposed on the lower side of the base cushion material 126 and near the front end from the substantially central portion, and this is attached to the side frame 121a. In addition, the member is elastically supported via a coil spring 129 and one wire 130 supported at one end thereof, and a member in which a buffer material 131 such as a urethane material or a three-dimensional knitted fabric is laminated on the upper surface of the auxiliary plate material 128 is provided. Further, an elastic band member 132 made of rubber or the like is disposed in the width direction between the buffer material 131 and the base cushion material 126, and this is supported by a coil spring 133 having one end supported by the side frame 121a. Furthermore, one end of the coil spring 134 is hung on a portion of the base cushion material 126 that is located near both sides of the rear support frame 124, and the other end of the coil spring 134 is positioned in a direction that extends rearward and obliquely outward. The auxiliary frame member 135 is engaged. The coil spring 134 disposed at the rear generates a longitudinal tension in the base cushion material 126, and a tension that intersects the tension direction is generated by the belt-like elastic member 132 or the like, thereby assisting the restoring force. Further, since the auxiliary plate material 128 is disposed closer to the front of the base cushion material 126, the holdability and stability in the vicinity of the heel portion are enhanced, and the posture support function is improved.

  The seat back 140 includes a base cushion material 141 and a surface cushion material (not shown) stretched on the back frame 142 so as to cover the base cushion material 141. The base cushion material 141 and the surface cushion material are configured using a three-dimensional knitted fabric or the like, similar to that used for the seat cushion 120 described above. The base cushion material 141 has an upper end supported on the upper part of the back frame 142 by a coil spring 144 and a lower end supported on the cushion frame 121 by a coil spring 145 so that a predetermined tension is applied to the base cushion material 141. Is ensured.

  The calculation unit 20 is connected to the above-described respiratory displacement detection sensor 10 via a wireless or signal cable, and includes a threshold value calculation unit, a time-series variation calculation unit, and a respiratory mind-body state determination unit. As shown in the flowchart of FIG. 4, the measurement data (signal data) obtained by the respiratory displacement detection sensor 10 is first subjected to frequency analysis in the threshold value calculation means (S100) and then has its maximum amplitude. The peak frequency is determined as a threshold value.

  The time series fluctuation calculating means includes a band pass filter and a respiratory frequency time series data calculating means. The band pass filter extracts a predetermined frequency band including the peak frequency obtained by the threshold value calculation means from the measurement data (S101). Next, the maximum value calculation means set in the time series fluctuation calculation means detects the maximum value of the waveform using the waveform of the data extracted by the band pass filter (S102). Next, the average value of the respiration frequency in a predetermined time range is obtained by the respiration frequency time series data calculating means (S103). The obtained average value is slide-calculated at a predetermined slide lap ratio (S104), and when the measurement end time is reached, the time-series fluctuation of the respiratory frequency is displayed (S105).

Here, the average value of the respiratory frequency is obtained by averaging each time interval (difference) in which the maximum value exists in the time width (for example, 180 seconds) used in the slide calculation, and 1 / (average value of each time interval) ). When calculating the average value of the respiratory frequency during T seconds (s) with a slide lap ratio of 90%,
Slide calculation (1): Between T / 10 (s) and T + T / 10 (s),
Slide calculation (2): Between 2 × T / 10 (s) and T + 2 × T / 10 (s),
Slide calculation (n): It is performed between n × T / 10 (s) and T + n × T / 10 (s), and the average value of the respiratory frequency is obtained.

  Then, for example, the average value of the respiratory frequency obtained first is plotted at the time T (s), and the average value of the respiratory frequency obtained in the next slide calculation is plotted at the time T + T / 10 (s). The average value of the respiratory frequency obtained in the nth slide calculation is plotted at the time point T + n × T / 10 (s), and time series data of the respiratory frequency is obtained. When various experiments were repeated, the optimal time width (T seconds) for performing the slide calculation was 180 seconds, and the optimal slide lap ratio was 90%.

  On the other hand, the time series fluctuation calculating means of this embodiment further includes body movement magnitude time series data calculating means for obtaining time series fluctuations in the magnitude of body movement due to respiration. As shown in the flowchart of FIG. 4, the body movement magnitude time-series data calculation means obtains the average value of the local maximum values existing in the time range for performing the slide calculation from the local maximum value detected in S102 (S106). Then, slide calculation is performed at a predetermined slide lap rate in the same manner as described above (S104), and when the measurement end time is reached, it is displayed as a time-series variation in the magnitude of body movement due to respiration (S105). By comparing and displaying the time-series data of the body movement magnitude and the time-series data of the respiratory frequency, it is possible to determine the sleep onset sign as will be described later.

  The respiratory mind / body state determination means compares the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculation means with the threshold obtained by the threshold calculation means, and determines that the state is a sleep state if it is below the threshold (FIG. 4 S107). Also, the respiratory frequency time series data obtained by the respiratory frequency time series data computing means and the body motion magnitude time series data by breathing obtained by the body motion magnitude time series data computing means are compared, and two times are obtained. The timing at which the series data is in an antiphase state is determined as a sleep onset sign (S107 in FIG. 4).

(Test example)
Two subjects, subjects A and B, were seated on the seats shown in FIGS. During the experiment, the state of each subject was observed by an observer. FIG. 5A shows the measurement original waveform of the subject A detected by the respiratory displacement detection sensor 10, and FIG. 5B shows the frequency analysis result. FIG. 6A shows the measurement original waveform of the subject B detected by the respiratory displacement detection sensor 10, and FIG. 6B shows the frequency analysis result. Subject A has the maximum amplitude (peak frequency) at 0.28 Hz, and subject B has the maximum amplitude (peak frequency) at 0.27 Hz. Therefore, these values are set as threshold values. FIG. 7A is an enlarged view of the measurement original waveform of the subject A from 1000 to 1100 seconds, and FIG. 7B shows the waveform extracted by the band-pass filter for the same time. FIG. 8A is an enlarged view of the measurement original waveform of the subject B from 1000 to 1100 seconds, and FIG. 8B shows the waveform extracted by the band-pass filter for the same time. In the case of the subject A, 0.01 to 0.33 Hz is extracted, and in the case of the subject B, 0.01 to 0.32 Hz is extracted.

  Here, the frequency band to which the bandpass filter is applied is extracted from a frequency band as small as 0.01 Hz to the peak frequency of each subject. This is to prevent loss of information on respiratory fluctuation components existing in the low frequency region.

  FIGS. 9 and 10 show the time-series data of the respiratory frequency obtained by the slide calculation and the time-series data of the magnitude of body movement (respiration magnitude). FIG. The results are shown in FIG.

  For subject A, it can be determined that the subject is in the sleep state about 1300 sec when the respiratory frequency is below the threshold. On the other hand, the observer determined that the sleep state was about 1500 seconds, but it was observed that the subject A showed a calm breathing state with his eyes closed from about 2 minutes before the observer determined that it was sleeping. It is estimated that the actual timing of sleep was around 1300 seconds. Further, when comparing the respiration magnitude (body movement magnitude) and the respiration frequency, the increase / decrease is greatly reversed in the vicinity of 1000 seconds. This indicates that the speed and magnitude of breathing are disturbed, and can be determined as a sign of sleep onset.

  In the case of the subject B, it was about 1500 seconds that the respiration frequency fell below the threshold value. This almost coincided with the timing when the observer determined to be sleeping. In addition, there was a phenomenon that appeared to be a sign of sleep onset in which the magnitude of breathing (the magnitude of body movement) and the breathing frequency were reversed in the vicinity of 400 seconds.

  From the above, according to the above test example, it is possible to effectively determine whether it is a sleeping state or a wakeful state, and also by determining the magnitude of breathing (size of body movement) together It was found that it is possible to determine the sign of sleep onset before sleep.

  In the above embodiment, the maximum value is obtained and the respiration frequency is calculated based on the measurement data detected by the respiratory displacement detection sensor 10, but the predetermined time range of the maximum value of the waveform obtained by applying the band-pass filter The number of breaths is calculated as the number of breaths, and the number of breaths obtained is slide-calculated at a predetermined slide lap rate to obtain a time series data calculating means for calculating the number of breaths. In this case, the threshold is set to a value obtained by multiplying the peak frequency obtained by frequency analysis by a time width (for example, 180 seconds) for performing slide calculation.

  FIG. 11 shows the results of a sleep experiment in which the subject C is seated on the seat shown in FIGS. FIG. 11A is obtained by processing data obtained from the piezoelectric sensor set on the seat cushion (under the bottom), and FIG. 11B is obtained from the piezoelectric sensor set on the seat back (back). Processed data. In this test, the peak frequency was 0.3 Hz. The frequency band to which the bandpass filter was applied was 0.1 to 0.45 Hz, the slide calculation time width was 180 seconds, and the slide lap rate was 90%. Therefore, the threshold value was set to 54 times from the peak frequency (0.3 Hz) × slide calculation time width (180 seconds).

  As shown in FIG. 11, according to the observer's record, a low wakefulness state was observed from 600 to 1400 seconds, a wandering state from 1400 to 2200 seconds, and a sleep state from 2200 seconds to the end of measurement. When this is seen in the waveform of the respiratory rate, both of FIGS. 11 (a) and 11 (b) show a downward trend around 500 seconds, and are determined to be sleeping from around 1700 seconds when the respiratory rate falls below 54 times. it can. This is an intermediate period between the time when the observer determines that he is in a heel state and the time when he / she determines that he / she is sleeping, and it can be said that the observation results are almost the same. In this study, the back data is easier to judge than the lower butt data, which indicates that in this subject, the back is easier to detect respiratory components. Is. However, it is more preferable that the sensor for detecting the pressure fluctuation is provided in both the seat cushion and the seat back, and the determination is made in each of them because it is different depending on the person whether the bottom of the back or the back can be detected prominently. .

  Further, in the above description, only the respiratory component is detected to determine a person's mind and body state. However, a pulse wave measuring device for measuring a person's pulse wave is provided and a pulse wave obtained from the pulse wave measuring device is provided. Power value inclination calculating means for calculating the time series fluctuation of the slope of the power value and maximum Lyapunov exponent inclination calculating means for calculating the time series fluctuation of the slope of the maximum Lyapunov exponent of the pulse wave obtained from the pulse wave measuring device. The time series fluctuation of the power value slope obtained by the power value slope computing means and the time series fluctuation of the slope of the maximum Lyapunov exponent slope obtained by the maximum Lyapunov exponent slope computing means are compared, and two time series data It is also possible to have a configuration equipped with a pulse wave mind / body state determining means for determining that the timing when the phase is in the opposite phase state is the onset of sleep onset.

  Here, the maximum Lyapunov exponent is one of the chaos indicators, which can grasp the state of the mental burden of the person, the power value is obtained from the peak value of the period of the pulse wave signal data, and the state of the physical burden Can be grasped. Then, when these are calculated by sliding calculation in the same manner as described above, the time series of the power value time series and the maximum Lyapunov exponent slope in the sleep onset transition period when the sleep onset symptom signal and transition state signal appear It is disclosed in Japanese Patent Application No. 2003-180294 previously proposed by the present applicant that there is a relationship in which the phase is reversed by 180 degrees with respect to the change. Therefore, it is more preferable that the determination unit for the sleep onset transition period is used in combination with the above-described determination method based on the respiratory component so that the warning device is operated when it is determined by any one of the sleep transition transition periods. In this case, for example, the determination of the sleep transition period based on the respiratory component can be used as a main, and the determination based on the pulse wave can be used as a backup.

  As the pulse wave, a fingertip volume pulse wave and an earlobe pulse wave can be collected. As a pulse wave measuring device, for example, a known fingertip volume pulse wave or an optical fingertip pulse wave meter or an ear vein pulse is collected. A measuring instrument that collects waves can be used. In collecting pulse waves from the driver, for example, if an optical finger plethysmograph is attached to the driving glove and the measurement result is wirelessly transmitted to the calculation unit, it will hinder driving. Never come.

  FIG. 12 shows the finger plethysmogram, ear plethysmogram, and respiration (using the pressure sensor of the buttocks) processed for the person seated on the seat shown in FIGS. Time series data. From this result, it can be seen that the timing at which it can be determined as a sleep onset sign and the timing at which it can be determined as a sleep onset point are substantially the same, and by using these together, it is possible to more reliably determine a sleep onset sign.

FIG. 1 is a perspective view showing a schematic configuration of a seat to which a mind and body state determination system according to an embodiment of the present invention is attached. FIG. 2 is a side view showing a schematic configuration of the sheet. FIG. 3 is a plan view showing a schematic configuration of the sheet. FIG. 4 is a flowchart of measurement data processing of the mind-body state determination system according to the embodiment. Fig.5 (a) shows the measurement original waveform of the test subject A detected by the respiratory displacement detection sensor, and FIG.5 (b) is a figure which shows the frequency analysis result. FIG. 6A shows the measurement original waveform of the subject B detected by the respiratory displacement detection sensor, and FIG. 6B shows the frequency analysis result. FIG. 7A is an enlarged view of the measurement original waveform of the subject A from 1000 to 1100 seconds, and FIG. 7B is a diagram showing the waveform extracted by the band-pass filter for the same time. FIG. 8A is an enlarged view of the measurement original waveform of the subject B from 1000 to 1100 seconds, and FIG. 8B is a diagram showing the waveform extracted by the band-pass filter for the same time. FIG. 9 is a diagram in which the time-series data of the respiratory frequency obtained by the slide calculation and the time-series data of the magnitude of body movement (the magnitude of respiration) are displayed together with respect to the measurement data of the subject A. FIG. 10 is a diagram in which the time-series data of the respiratory frequency obtained by the slide calculation and the time-series data of the magnitude of body movement (respiration magnitude) are displayed together with respect to the measurement data of the subject B. FIG. 11A is a diagram in which data obtained from the piezoelectric sensor set on the seat cushion (under the butt) is processed to obtain time-series data of the number of breaths, and FIG. It is the figure which calculated | required the time series data of the frequency | count of respiration by processing the data obtained from the piezoelectric sensor set to the back. FIG. 12 is a diagram showing time-series data obtained by processing data of fingertip volume pulse wave, ear pleural pulse wave, and respiration (using the pressure sensor of the buttocks).

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Psychosomatic state determination system 10 Respiration displacement sensor 20 Calculation part 100 Seat 120 Seat cushion 122 Torsion bar 126 Base cushion material 127 Surface layer cushion material 140 Seat back 141 Base cushion material

Claims (11)

A respiratory displacement detection sensor attached to the human body support means, the detection value of which varies with the breathing of the person supported by the human body support means;
Based on the signal data obtained from the respiratory displacement detection sensor, time series fluctuation calculating means for calculating the time series fluctuation of the respiratory state,
Threshold analysis means for analyzing the frequency of the signal data obtained from the respiratory displacement detection sensor and obtaining a threshold using the peak frequency that is the maximum amplitude;
A respiratory psychosomatic state determining means for comparing the time series fluctuation of the respiratory state obtained by the time series fluctuation calculating means with the threshold obtained by the threshold calculating means to determine the person's psychosomatic state. A characteristic physical and mental state determination system. The time series fluctuation calculating means includes a bandpass filter for extracting a predetermined frequency band including a peak frequency obtained by the threshold value calculating means,
The average value of the respiratory frequency in a predetermined time range of the maximum value of the waveform obtained by applying the band pass filter is obtained, and the obtained average value is calculated by sliding at a predetermined slide lap ratio to calculate the time series fluctuation of the respiratory frequency. 2. The psychosomatic state determination system according to claim 1, further comprising a respiration frequency time-series data calculation means to be obtained.   The respiratory mind-and-body state determining means compares with the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculating means, and determines that it is a sleep state when the respiratory frequency time-series data is below the threshold obtained by the threshold calculating means. The mental and physical state determination system according to claim 2, wherein   4. The psychosomatic state determination system according to claim 3, wherein the threshold value is the same as a peak frequency obtained by the threshold value calculation means. The time series fluctuation calculating means includes a bandpass filter for extracting a predetermined frequency band including a peak frequency obtained by the threshold value calculating means,
The number of maximum values of the waveform obtained by applying the bandpass filter in a predetermined time range is obtained as the number of breaths, and the obtained breathing number is calculated by sliding at a predetermined slide lap ratio to obtain the time series fluctuation of the number of breaths. 2. The psychosomatic state determination system according to claim 1, further comprising a respiratory frequency time-series data calculation means.   The respiratory mind-and-body state determining means compares with the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculating means, and determines that it is a sleep state when it is below the threshold obtained by the threshold calculating means. The psychosomatic state determination system according to claim 5, wherein:   The psychosomatic state determination according to claim 6, wherein the threshold value is a value obtained by multiplying the peak frequency obtained by the threshold value calculation means by the time width of the predetermined time range obtained for the number of breaths. system. Further, the average value of the maximum value of the waveform obtained by applying the bandpass filter in a predetermined time range is obtained, and the obtained average value is calculated by sliding with a predetermined slide lap ratio to calculate the magnitude of body movement due to respiration. Body movement magnitude time series data calculating means for obtaining time series fluctuations,
In the respiratory psychosomatic state determining means, the respiratory frequency time-series data obtained by the respiratory frequency time-series data calculating means and the body movement magnitude due to breathing obtained by the body movement magnitude time-series data calculating means. 3. The psychosomatic state determination system according to claim 2, wherein the time series data is compared with the series data, and the timing at which the two time series data are in an antiphase state is determined as a sleep onset sign.   The psychosomatic state determination system according to any one of claims 1 to 8, wherein the respiratory displacement detection sensor is a sensor that detects pressure fluctuation due to human breathing. Furthermore, a pulse wave measuring device for measuring the pulse wave of the person supported by the human body support means is provided,
A power value inclination calculating means for calculating a time series variation of the inclination of the power value of the pulse wave obtained from the pulse wave measuring device;
Maximum Lyapunov exponent inclination calculating means for calculating the time series variation of the gradient of the maximum Lyapunov exponent of the pulse wave obtained from the pulse wave measuring device;
The time series fluctuation of the power value slope obtained by the power value slope computing means and the time series fluctuation of the slope of the maximum Lyapunov exponent slope obtained by the maximum Lyapunov exponent slope computing means are compared, and two time series data Comprising a psychosomatic state determination means for pulse wave, which determines that the timing when is in an antiphase state is a sleep onset sign,
9. The psychosomatic state determination system according to claim 8, wherein the respiratory psychosomatic state determination unit and the pulse wave psychosomatic state determination unit are used in combination to determine a sleep onset sign.   The psychosomatic state determination system according to claim 10, wherein the pulse wave measuring device is a device that measures at least one of a fingertip volume pulse wave or an earlobe pulse wave.

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