WO2010021227A1

WO2010021227A1 - Organism condition analyzer, computer program, and recording medium

Info

Publication number: WO2010021227A1
Application number: PCT/JP2009/063390
Authority: WO
Inventors: 悦則藤田; 由美小倉; 慎一郎前田; 重行小島
Original assignee: 株式会社デルタツーリング
Priority date: 2008-08-20
Filing date: 2009-07-28
Publication date: 2010-02-25
Also published as: JP5327584B2; JP2010046236A

Abstract

A significant biometric signal can be detected without giving a feeling of strangeness, and the condition of a person can be correctly analyzed. An organism condition analyzer includes a second-order differentiating means (61) for twice differentiating time-series signal data about an air pressure variation detected by an air cushion. The second-order differentiation waveform obtained by the second-order differentiating means (61) is processed to analyze the condition of a person. The time series signal data represents the pulse of an aorta in the back of the person, and the second-order differentiation emphasizes the variation of the waveform of the time-series signal data. Information representing the organism condition included in the waveform obtained by the second-order differentiation is more significant than that representing the organism condition included in the waveform before the second-order differentiation. Therefore, analysis of the time-series waveform obtained by the second-order differentiation bring about more accurate result of the analysis than that by conventional means for analyzing the organism condition by using the pulse of the aorta.

Description

Biological condition analyzer, computer program, and recording medium

The present invention relates to a technique for analyzing a state of a living body by detecting a biological signal, and more particularly, to a biological state analyzing apparatus, a computer program, and a recording medium using an air cushion capable of non-invasively detecting a biological signal.

In recent years, monitoring the biological state of a driver during driving has attracted attention as an accident prevention measure. For example, as disclosed in Patent Document 1, the present applicant also includes an air bag in which a restoring force imparting member is inserted. The air bag is disposed at a portion corresponding to, for example, a human waist, and the air pressure variation of the air bag is measured. A system for detecting a human biological signal from the obtained time-series data of air pressure fluctuation and analyzing the state of the human biological body is disclosed.

Non-Patent Documents

1 and 2 also report attempts to detect human biological signals by arranging an air pack sensor along the lumbar gluteal muscles.
JP 2007-90032 A "Application of biological fluctuation signals measured by non-invasive sensors to fatigue and sleep prediction", Naoki Ochiai (6 others), 39th Annual Meeting of the Japan Ergonomics Society, Chugoku-Shikoku Branch, 2006 Issued on May 25, Publisher: Japan Ergonomics Society Chugoku-Shikoku Branch Office "Prototype of vehicle seat with non-invasive biological signal sensing function", Shinichiro Maeda (4 others), 39th Japan Ergonomics Society China-Shikoku Branch Conference, Proceedings, November 25, 2006 Place: Japan Ergonomics Society Chugoku / Shikoku Branch Office

According to Patent Document 1 and

Non-Patent Documents

1 and 2, a pulse wave of an artery (aorta) near the lumbar region is detected, and time series signal data of the obtained pulse wave is used. The biological state is analyzed by detecting a sleep symptom signal by the method proposed in -344612. More specifically, the detection of the onset of sleep signal is performed by obtaining the maximum value and the minimum value of the time-series signal data of the pulse wave by the smoothing differentiation method using Savitzky and Golay, respectively. Then, the maximum value and the minimum value are divided every 5 seconds, and the average value of each is obtained. The square of the difference between the average values of the obtained local maximum and local minimum is used as a power value, and this power value is plotted every 5 seconds to create a time series waveform of the power value. In order to read the global change of the power value from this time series waveform, the slope of the power value is obtained by the least square method for a certain time width Tw (180 seconds). Next, the next time width Tw is similarly calculated at the overlap time Tl (162 seconds), and the result is plotted. This calculation (slide calculation) is sequentially repeated to obtain a time series waveform of the gradient of the power value. On the other hand, chaos analysis is performed on pulse wave time series signal data to obtain the maximum Lyapunov exponent, and as above, the maximum and minimum values are obtained by smoothing differentiation, and the time series of the slope of the maximum Lyapunov exponent is obtained by slide calculation. Get the waveform.

In these two time series waveforms, the time series waveform of the power value slope and the time series waveform of the power value slope and the time series waveform of the maximum Lyapunov exponent slope are in opposite phases, and further, the power value A waveform having a low frequency and a large amplitude waveform in the time series waveform of the slope is a characteristic signal indicating a sleep onset symptom, and the point at which the amplitude subsequently decreases is the sleep onset point.

However, when comparing the technique for detecting a sleep onset symptom signal using the pulse wave of the above-mentioned artery with the technique for detecting a sleep onset symptom signal using the fingertip volume pulse wave, when using the fingertip volume pulse wave, In spite of being detected as a sleep onset signal, it has been found that when an arterial pulse wave is used, the onset signal cannot be detected at that timing, or vice versa. In order to reliably detect the onset of sleep signal, detection using arterial pulse waves and detection using fingertip volume pulse waves may be used in combination. For example, when trying to detect a sleep signal of a car driver, the driver does not always wear a fingertip plethysmograph in daily driving. is not.

The present invention has been made in view of the above, and by using time-series signal data of air pressure fluctuations detected by an air cushion disposed at a portion corresponding to a back portion including at least a waist portion of a person, the state of the person than before It is an object of the present invention to provide a biological state analysis apparatus, a computer program, and a recording medium that can accurately analyze the condition.

In order to solve the above-described problems, a living body state analyzing apparatus according to the present invention includes a human body support unit that includes a skin member that supports at least the vicinity of a human waist and a cushion support member that is disposed on the back side of the skin member. Time-series signal data of the sensor due to air pressure fluctuation from a biological signal measuring device comprising an air cushion having an air bag incorporated in between and a sensor for detecting air pressure fluctuation of the air bag accompanying an arterial pulse wave A biological state analyzer that processes the time-series signal data and analyzes the state of the person supported by the human body support means,
The apparatus has a second-order differential operation means for second-order differentiation of the time series signal data, and processes a second-order differential waveform obtained by the second-order differential operation means to analyze a human state.
The biological state analyzer of the present invention further includes an upper limit peak value and a lower limit peak value for each predetermined time range from the peak value of each cycle of the second derivative waveform obtained by the second derivative calculation means. The difference is calculated, and the difference is used as a power value, time series data of the power value is obtained, and the slope of the second-order differential waveform power value obtained by sliding the power value with respect to the time axis in a predetermined time range by a predetermined number of times is calculated. Means,
Second-order differentiation obtained by obtaining time-series data of the maximum Lyapunov exponent from the second-order differential waveform obtained by the second-order differentiation calculation means and slidingly calculating the slope of the maximum Lyapunov exponent with respect to the time axis in a predetermined time range by a predetermined number of times. Waveform maximum Lyapunov exponent slope calculation means,
When the slope time-series waveforms obtained by the second-order differential waveform power value slope calculating means and the second-order differential waveform maximum Lyapunov exponent slope calculating means are overlapped, the two slope time-series waveforms have an antiphase relationship. It is preferable to comprise a second-order differential waveform sleep onset determination means for determining a waveform as a sleep onset signal.
The biological state analyzer of the present invention further includes an upper limit peak value and a lower limit peak value for each predetermined time range from the peak value of each period of time series signal data of air pressure fluctuation detected by the air cushion. The original waveform power value slope calculation means for obtaining the time series data of the power value and calculating the slope of the power value with respect to the time axis in a predetermined time range by performing slide calculation a predetermined number of times. When,
From the time series signal data of the air pressure fluctuation detected by the air cushion, the time series data of the maximum Lyapunov exponent is obtained, and the original waveform obtained by sliding the slope of the maximum Lyapunov exponent with respect to the time axis in a given time range a predetermined number of times Means for calculating the maximum Lyapunov exponent slope;
When the respective slope time series waveforms obtained by the original waveform power value slope calculating means and the original waveform maximum Lyapunov exponent slope calculating means are overlapped, a waveform in which the two slope time series waveforms are in an antiphase relationship is displayed. An original waveform sleep onset predictor determining means for determining a signal;
Whether a sleep onset sign signal has been determined only by the second-order differential waveform sleep onset determination means, or whether both of the second-order differential waveform sleep onset determination means and the original waveform onset sign determination means have determined a sleep onset sign signal in the same time period A comparison judgment means for making a comparison judgment;
It is preferable to comprise warning control means for operating a warning for being awakened according to the comparison results of the ratio determining means.
The warning control means of the biological state analyzer of the present invention includes a case in which the comparison judgment means determines a sleep onset symptom signal only by the second order differential waveform sleep onset determination means, and the second order differential waveform sleep onset determination means. In addition, it is preferable that different types of warnings function depending on whether the sleep onset sign signal is determined in the same time period in both the original waveform sleep onset determination means.
Further, the computer program of the present invention includes an air bag incorporated between a skin member of a part of the human body support means that supports at least the vicinity of a human waist and a cushion support member disposed on the back side of the skin member. Receiving time-series signal data of the sensor due to air pressure fluctuation from a biological signal measuring device comprising an air cushion provided and a sensor for detecting air pressure fluctuation of the air bag accompanying a pulse wave of an artery; A computer program introduced into a biological state analyzer that processes signal data and analyzes the state of a person supported by the human body support means,
The apparatus has a second-order differential operation means for second-order differentiation of the time series signal data, and processes a second-order differential waveform obtained by the second-order differential operation means to analyze a human state.
The computer program of the present invention further calculates a difference between the peak value on the upper limit side and the peak value on the lower limit side for each predetermined time range from the peak value of each cycle of the second-order differential waveform obtained by the second-order differential calculation means. A second-order differential waveform power value slope calculating means for calculating and calculating time series data of the power value by using this difference as a power value, and calculating a slope of the power value with respect to the time axis in a predetermined time range by sliding a predetermined number of times; ,
Second-order differentiation obtained by obtaining time-series data of the maximum Lyapunov exponent from the second-order differential waveform obtained by the second-order differentiation calculation means and slidingly calculating the slope of the maximum Lyapunov exponent with respect to the time axis in a predetermined time range by a predetermined number of times. Waveform maximum Lyapunov exponent slope calculation means,
When the slope time-series waveforms obtained by the second-order differential waveform power value slope calculating means and the second-order differential waveform maximum Lyapunov exponent slope calculating means are overlapped, the two slope time-series waveforms have an antiphase relationship. It is preferable to comprise a second-order differential waveform sleep onset determination means for determining a waveform as a sleep onset signal.
The computer program of the present invention further includes a difference between a peak value on an upper limit side and a peak value on a lower limit side for each predetermined time range from a peak value of each period of time series signal data of air pressure fluctuation detected by the air cushion. An original waveform power value slope calculating means for obtaining a time value data of the power value and calculating a slope of the power value with respect to the time axis in a predetermined time range by performing slide calculation a predetermined number of times,
From the time series signal data of the air pressure fluctuation detected by the air cushion, the time series data of the maximum Lyapunov exponent is obtained, and the original waveform obtained by sliding the slope of the maximum Lyapunov exponent with respect to the time axis in a given time range a predetermined number of times Means for calculating the maximum Lyapunov exponent slope;
When the respective slope time series waveforms obtained by the original waveform power value slope calculating means and the original waveform maximum Lyapunov exponent slope calculating means are overlapped, a waveform in which the two slope time series waveforms are in an antiphase relationship is displayed. An original waveform sleep onset predictor determining means for determining a signal;
Whether a sleep onset sign signal has been determined only by the second-order differential waveform sleep onset determination means, or whether both of the second-order differential waveform sleep onset determination means and the original waveform onset sign determination means have determined a sleep onset sign signal in the same time period A comparison judgment means for making a comparison judgment;
It is preferable to comprise warning control means for operating a warning for being awakened according to the comparison results of the ratio determining means.
The warning control means of the computer program according to the present invention includes a case where, in the comparison and determination means, a sleep onset sign signal is determined only by the second-order differential waveform sleep onset determination means, and the second-order differential waveform sleep onset determination means and the original It is preferable that different types of warnings are made to function depending on whether the sleep onset sign signal is determined in the same time period in both of the waveform sleep onset determination means.
The present invention also provides a computer-readable recording medium on which the computer program is recorded.

In the present invention, there is a second-order differential calculation means for performing second-order differentiation on the time-series signal data of the air pressure fluctuation detected by the air cushion, and the second-order differential waveform obtained by the second-order differential calculation means is processed to produce a human Analyze the condition. The time-series signal data of air pressure fluctuation detected from the air cushion is due to the arterial pulse wave. By second-order differentiation of this, changes in the waveform of the time-series signal data, especially changes in high-frequency components, are emphasized. Is done. As described later, according to the tests of the present inventors, the time series waveform obtained by second-order differentiation emphasizes the high-frequency component, so that the time series of the fingertip volume pulse wave, which is a peripheral pulse wave, is used. By sampling the arterial pulse wave, the arterial pulse wave itself and the second-order differential waveform approximating the fingertip volume pulse wave can be used together, and only the arterial pulse wave is obtained. However, it is possible to analyze the biological state with higher accuracy.

In addition, the detection of the sleep signal by the fingertip volume pulse wave is more sensitive than the sleep signal by the arterial pulse wave. Therefore, when a sleep onset predictor signal is detected only from the time-series waveform of the arterial pulse wave obtained by second-order differentiation corresponding to the fingertip volume pulse wave, the sleep onset predictor signal is a sleep signal from the awakening stage. It seems that the signs of the level closer to the awakening stage were captured until reaching stage 1. On the other hand, not only from a time-series waveform obtained by second-order differentiation, but also when using a data that is not second-order differentiated, when the sleep onset predictor signal is detected, the detection sensitivity of the sleep onset predictor signal is relative. Therefore, it is considered that a sign of a level closer to the sleep stage 1 is captured from the awakening stage to the sleep stage 1. That is, by capturing the sleep onset sign signal using two time-series waveforms in this way, it is possible to capture a two-stage sleep onset sign signal. Therefore, by linking the warning control means to the two-stage sleep signal, for example, when a sleep signal is detected only from the time-series waveform obtained by second-order differentiation, a weak warning is issued. Even when data that is controlled and is not second-order differentiated is used, if a sleep onset sign signal is detected, control can be performed so that a strong warning is issued. In addition, when a waveform having a relatively small amplitude is observed between the two sleep onset signals, the first sleep onset signal is shown to indicate that the sleep state 1 is not reached but the state of disorder is continued. A warning may be issued even when such a small waveform is seen after appearing.

FIG. 1 is a diagram illustrating a state in which a biological signal measuring device that is an analysis target of a biological state analyzer according to an embodiment of the present invention is incorporated in a sheet. FIG. 2 is a diagram showing the biological signal measuring apparatus according to the embodiment in more detail. 3A and 3B are views showing the air cushion unit, where FIG. 3A is a cross-sectional view seen from the front direction, FIG. 3B is a side view, FIG. 3C is a bottom view, and FIG. It is A sectional view. FIG. 4 is an exploded perspective view of the air cushion unit. 5A and 5B are views for explaining the size of the air cushion unit used in the test example. FIG. 6 is a diagram for explaining the structure of the biological state analyzer of the embodiment. FIG. 7 is a diagram for explaining a method of measuring load-deflection characteristics in Test Example 1. FIG. 8 is a diagram showing the measurement results of FIG. FIG. 9 is a diagram for explaining a test method for vibration absorption characteristics of Test Example 2. FIG. FIGS. 10A to 10D are diagrams showing sensor outputs when vibration is applied at 1.0 Hz to 2.5 Hz in Test Example 2. FIG. FIGS. 11A to 11D are diagrams showing sensor outputs when vibration is applied at 3.0 Hz to 4.5 Hz in Test Example 2. FIG. 12A to 12D are diagrams showing sensor outputs when vibration is applied at 5.0 Hz to 6.5 Hz in Test Example 2. FIG. FIGS. 13A to 13D are diagrams showing sensor outputs when vibration is applied at 7.0 Hz to 8.5 Hz in Test Example 2. FIG. 14A to 14C are diagrams showing sensor outputs when vibration is applied at 9.0 Hz to 10.0 Hz in Test Example 2. FIG. 15A and 15B are diagrams for explaining the test method of Test Example 3. FIG. FIG. 16 is a diagram showing the output of the sensor when vibrating at 1.0 Hz in Test Example 3. FIG. 17 is a diagram showing the output of the sensor when vibrating at 1.5 Hz in Test Example 3. FIG. 18 is a diagram showing the output of the sensor when vibrating at 2.0 Hz in Test Example 3. FIG. 19 is a diagram showing an original waveform of a subject's aortic pulse wave (air pack pulse wave) collected by the biological signal measuring apparatus in Test Example 4. FIG. 20 is a diagram showing a second-order differential waveform (air pack pulse wave second-order differential waveform) obtained by the second-order differential calculation means. FIG. 21 is a diagram illustrating an original waveform of a fingertip volume pulse wave. FIG. 22 is a diagram showing a part of the air pack pulse wave and fingertip volume pulse wave shown in FIGS. 19 and 21 partially enlarged. FIG. 23 is a diagram showing another part of the air pack pulse wave and fingertip volume pulse wave shown in FIGS. 19 and 21 partially enlarged. FIG. 24A is a frequency analysis result of the air pack pulse wave of FIG. 19 (“Air-pack” in the figure) and the fingertip volume pulse wave of FIG. 21 (“Plethysmogram” in the figure). (B) is a frequency analysis of the second-order differential waveform of the air pack pulse wave in FIG. 20 (“Air-pack (2nd differential calculus”) in FIG. 20 and the fingertip volume pulse wave in FIG. 21 (“Plethysmogram” in the figure)). It is a figure which shows a result. FIG. 25A is a time-series waveform of the original waveform power value of the air pack pulse wave and the gradient of the original waveform maximum Lyapunov exponent, and FIG. 25B is the second derivative of the air pack pulse wave second-order differential waveform. FIG. 25C is a time series waveform of the waveform power value and the slope of the second-order differential waveform maximum Lyapunov exponent, and FIG. 25C shows the power value of the fingertip volume pulse wave and the slope of the maximum Lyapunov exponent determined from the fingertip volume pulse wave. It is a time series waveform. FIG. 25 (d) is a diagram showing a time-series waveform of the distribution rate of the electroencephalogram. 26 (a) and 26 (b) show three types of time series waveforms (air pack pulse wave (“Air-pack” in the figure)), air pack pulse wave second-order differential waveform (in the figure, FIG. 25) of FIG. It is a figure which shows the frequency analysis result of "Air-pack (2nd) differential (calculus) wave") and fingertip plethysmogram ("Plethysmogram" in the figure). FIG. 27A shows an air pack pulse wave (“Air-pack sensor” in the figure), and FIG. 27B shows an air pack pulse wave second-order differential waveform (“Air-pack 2nd differential calculus wave” in the figure). 27 (c) is a fingertip volume pulse wave (“Plethysmogram” in the figure), and FIG. 27 (d) is a wavelet analysis of heart rate variability obtained from an electrocardiogram (“The electrocardiongram” in the figure). It is a figure which shows the result of having performed. FIG. 28A is a diagram showing the value of the acceleration pulse wave aging index (SDPTGAI) obtained from the fingertip volume pulse wave and the air pack pulse wave, and FIG. It is a figure which shows the value of the acceleration pulse wave aging index (SDPTGAI) obtained from the air pack pulse wave second-order differential waveform.

Hereinafter, the present invention will be described in more detail based on the embodiments of the present invention shown in the drawings. FIG. 1 is an external view of a vehicle seat 500 incorporating a biological signal measuring apparatus 1 that collects a pulse wave of a back aorta, which is a biological signal to be analyzed by the biological state analyzing apparatus 60 according to the present embodiment. It is. As shown in this figure, the biological signal measuring apparatus 1 is used by being incorporated in a seat back portion 510. The biological state analyzer 60 of the present embodiment can analyze biological information more accurately than in the past, but of course it can be analyzed more accurately as there is no noise in the biological signal to be analyzed. Therefore, first, in the following, the configuration of the biological signal measuring apparatus 1 with less noise mixing will be described.

The biological signal measuring apparatus 1 includes an air cushion unit 100, a first bead foam resin elastic member 20, and a second bead foam resin elastic member 30. The air cushion unit 100 includes a housing 15 and two air cushions 10 housed in the housing 15. As shown in FIGS. 3 and 4, each air cushion 10 is configured by laminating a front side air cushion 11 and a back side air cushion 12, and is arranged on the left and right sides of the container 15. The front-side air cushion 11 is formed such that three small air bags 111 are connected in the vertical direction, and each of them does not allow air to flow. In each small air bag 111, a three-dimensional solid knitted fabric 112 is disposed as a restoring force applying member.
The back side air cushion 12 has a large air bag 121 having the same length as the full length of the front side air cushion 11 formed by connecting three small air bags 111 and a tertiary as a restoring force applying member accommodated in the large air bag 121. It comprises an original three-dimensional knitted fabric 122 (see FIG. 4). The front side air cushion 11 and the back side air cushion 12 are used in such a manner that one side edge along the longitudinal direction is joined, folded into two around the joined side edge, and overlapped with each other ( (Refer FIG.3 (d) and FIG. 4).

In this embodiment, the air cushion 10 in which the front side air cushion 11 and the back side air cushion 12 are overlapped with each other is arranged on the left and right sides. By arranging them on the left and right, the back of the seated person becomes even on the left and right sides, making it difficult to feel uncomfortable. In addition, a sensor mounting tube 111a is provided in any one of the small air bags 111 constituting either one of the left and right

front air cushions

11 and 11, and a sensor 111b for measuring air pressure fluctuation is fixed inside thereof. The sensor mounting tube 111a is sealed. Although the sensor can be disposed in the large air bag 121 constituting the back side air cushion 12, if the air bag is provided with a large capacity, the air pressure fluctuation due to the pulse wave may be absorbed. It is preferable to provide in. However, as shown in FIG. 4, a mounting tube 121a is provided in the large air bag 121 in advance, and a sensor is disposed at that portion, and the air pressure fluctuation of the large air bag 121 is measured as necessary. Alternatively, the measurement result of the small air bag 111 may be used for verification. The small air bag 111 preferably has a size in the range of 40 to 100 mm in width and 120 to 200 mm in length in order to react sensitively to such a variation in air pressure due to a biological signal. The material of the small air bag 111 is not limited. For example, the small air bag 111 can be formed using a sheet made of polyurethane elastomer (for example, product number “DUS605-CDR” manufactured by Seadam Co., Ltd.). Any sensor 111b may be used as long as it can measure the air pressure in the small air bag 111. For example, a condenser microphone sensor can be used.

The size of the large air bag 121 and the total size when three small air bags 111 are connected are a range of 40 to 100 mm in width and 400 to 600 mm in total length when used for the seat back portion 510 of the automobile seat 500. It is preferable that When the length is short, the seat occupant feels a foreign body sensation only at a portion near the waist in the seat back portion 510. Therefore, it is preferable that the length is 400 mm or more to correspond to the entire back of the seat occupant as much as possible.

In this embodiment, the sensor 111b that detects air pressure fluctuation is provided in the small air bag 111 at the center of the front air cushion 11 that constitutes the air cushion 10 that is disposed on the left side of the seated person. The position of the small air bag 111 corresponds to a region where a pulse wave of the aorta near the waist of the seated person can be detected. The region in which the aortic pulse wave in the vicinity of the lumbar region can be detected is not uniform depending on the physique of the seated person, but when measured by 20 subjects with various physiques from a Japanese woman with a height of 158 cm to a Japanese man with a height of 185 cm, The small air bag 111 (width: 60 mm, length: 160 mm) is formed so that the intersection P (see FIGS. 2 and 3) between the side edge and the lower edge near the center of the seat back portion 510 is from the upper surface of the seat cushion portion 520. When the length L along the surface of the portion 510 was set to 220 mm and the distance M from the center of the seat back portion 510 was set to 80 mm, the pulse wave of the aorta could be detected in all the above subjects. When the size of the small air bag 111 is in the range of 40 to 100 mm in width and 120 to 200 mm in length, the position of the intersecting portion P is a length along the surface of the seat back portion 510 from the upper surface of the seat cushion portion 520. It is preferable to set the distance within the range of 150 to 280 mm and 60 to 120 mm from the center of the seat back portion 510.

It is preferable to unitize the two air cushions 10 so that the seat back portion 510 can be easily set at a predetermined position. Therefore, it is preferable to configure the air cushion unit 100 in which the air cushion 10 is loaded in the housing 15 as shown in FIGS. The container 15 has a bag-shaped air cushion accommodating part 151 that accommodates the air cushion 10 on both sides, and a connecting part 152 between the two air cushion accommodating parts 151.

The air cushion 10 is inserted into each of the two air cushion accommodating portions 151. In addition, it is preferable to insert a three-dimensional solid knitted fabric 40 having substantially the same size as the air cushion 10 into the air cushion accommodating portion 151 so as to overlap the back side of the back side air cushion 12 of the air cushion 10 (FIG. 3D). )reference). By arranging the three-dimensional solid knitted fabric 40, the effect of removing vibrations input to the human body side through the seat back portion 510 is further enhanced.

The connecting portion 152 only needs to be able to support the two air cushion portions 151 at a predetermined interval, and is formed with a width of about 60 to 120 mm. It is preferable that the connecting portion 152 is also formed in a bag shape, and the three-dimensional solid knitted fabric 45 is inserted therein (see FIGS. 3D and 4). Thereby, the vibration input through the connection portion 152 can be effectively removed by inserting the three-dimensional solid knitted fabric 45.

As described above, the small air bag 111 is formed using, for example, a sheet made of polyurethane elastomer (for example, product number “DUS605-CDR” manufactured by Seadam Co., Ltd.), and forms the back cushion material 12. The large air bag 121 and the container 15 are also preferably formed using the same material. Further, each three-dimensional solid knitted fabric loaded in the small air bag 111, the large air bag 121, the air cushion accommodating portion 151, and the connection portion 152 is disclosed in, for example, Japanese Patent Application Laid-Open No. 2002-331603. This is a knitted fabric having a three-dimensional three-dimensional structure having a pair of ground knitted fabrics spaced apart from each other and a large number of connecting yarns that reciprocate between the pair of ground knitted fabrics to couple them together.

One ground knitted fabric is formed by, for example, a flat knitted fabric structure (fine stitches) that is continuous in both the wale direction and the course direction from a yarn obtained by twisting a single fiber. For example, a knitted structure having a honeycomb-shaped (hexagonal) mesh is formed from a yarn obtained by twisting short fibers. Of course, this knitted fabric structure is arbitrary, and it is also possible to adopt a knitted fabric structure other than a fine structure or a honeycomb shape, and a combination thereof is also arbitrary, such as adopting a fine structure for both. The connecting yarn is knitted between two ground knitted fabrics so that one ground knitted fabric and the other ground knitted fabric maintain a predetermined distance. As such a three-dimensional solid knitted fabric, for example, the following can be used. Each three-dimensional solid knitted fabric can be used by stacking a plurality of pieces as necessary.

(1) Product number: 49076D (manufactured by Sumie Textile Co., Ltd.)
Material:
Front side ground knitted fabric: twisted yarn of 300 dtex / 288 f polyethylene terephthalate fiber false twisted yarn and 700 dtex / 192 f polyethylene terephthalate fiber false twisted yarn Back side ground knitted fabric: 450 dtex / 108 f polyethylene Combination of terephthalate fiber false twisted yarn and 350 decitex / 1f polytrimethylene terephthalate monofilament Linked yarn ... 350 decitex / 1f polytrimethylene terephthalate monofilament

(2) Product number: 49013D (manufactured by Sumie Textile Co., Ltd.)
Material:
Front side ground knitted fabric: two twisted yarns of 450 dtex / 108f polyethylene terephthalate fiber false twisted yarn Backside ground knitted fabric ... 450 twists of polyethylene terephthalate fiber false twisted yarn of 108 dtex / 108f Connecting thread: 350 dtex / 1f polytrimethylene terephthalate monofilament

(3) Product number: 69030D (manufactured by Sumie Textile Co., Ltd.)
Material:
Front side ground knitted fabric: two twisted yarns of 450 dtex / 144 f polyethylene terephthalate fiber false twisted yarn Back side ground knitted fabric: 450 dtex / 144 f polyethylene terephthalate fiber false twisted yarn and 350 dtex / 1 f Combined with polytrimethylene terephthalate monofilaments of linking yarns ... 350 dtex / 1f polytrimethylene terephthalate monofilaments

(4) Product number manufactured by Asahi Kasei Fibers Co., Ltd .: T24053AY5-1S

The first bead foam resin elastic member 20 and the second bead foam resin elastic member 30 are arranged between the skin member of the seat back portion 510 and the container 15 (air cushion unit 100) containing the air cushion 10. And has a length corresponding to the entire length of the two air cushions 10 and a width corresponding to the length between the tops of the two air cushions 10. Accordingly, it is preferable to use a material having a length of about 400 to 600 mm and a width of about 250 to 350 mm. Thereby, since the two air cushions 10 are covered together, it becomes difficult to feel the unevenness of the two air cushions 10.

The first bead foamed resin elastic member 20 is composed of a bead foam formed in a flat plate shape and a covering material adhered to the outer surface thereof. As the bead foam, a foam molded body by a resin bead method containing at least one of polystyrene, polypropylene and polyethylene is used. The expansion ratio is arbitrary and is not limited. The covering material is a material having a high elongation and a recovery rate, which is adhered to the outer surface of the bead foam by adhesion, and preferably has a recovery rate of 80% or more at the elongation of 200% or more and 100%. An elastic fiber nonwoven fabric is used. For example, a nonwoven fabric in which thermoplastic elastomer elastic fibers disclosed in Japanese Patent Application Laid-Open No. 2007-92217 are melt-bonded to each other can be used. Specifically, trade name “Espancione” manufactured by KB Seiren Co., Ltd. can be used.

The second bead foamed resin elastic member 30 includes a bead foam as in the first bead foamed resin elastic member 20, and the first bead foamed resin elastic member covers the outer surface thereof. A material that is less stretchable than the elastic fiber nonwoven fabric used in the member 20, for example, a nonwoven fabric made of thermoplastic polyester is used. Specifically, a biaxial woven fabric (length: 20 / inch, width: 20 / inch) formed from polyethylene naphthalate (PEN) fibers (1100 dtex) manufactured by Teijin Limited can be used.

The order in which the first bead foam resin elastic member 20 and the second bead foam resin elastic member 30 are stacked is not limited, but the first elastic member on the seat back portion 510 close to the skin member 511 has a high elasticity. It is preferable to dispose one bead foamed resin elastic member 20. The bead foam constituting the first and second bead foam resin

elastic members

20 and 30 has a thickness of about 5 to 6 mm, and the outer surface thereof has a thickness of about 1 mm or less and the above-described elastic fiber nonwoven fabric or heat. It is formed by sticking a nonwoven fabric made of plastic polyester. In the present embodiment, the surface of the first bead foamed resin elastic member 20 facing the skin member 511 and the surface of the second bead foamed resin elastic member 30 facing the air cushion unit 100 are each made of a PEN film or the like. A polyester film is attached. Thereby, the transmissibility of a biological signal improves.

In the present embodiment, the seat back portion 510 of the seat 500 constituting the human body support means includes a skin member 511 and a cushion support member 512 disposed on the back side of the skin member 511, and the skin member 511 A container 15 (air cushion unit 100) holding the air cushion 10 and the first and second bead foamed resin

elastic members

20 and 30 are incorporated between the cushion support member 512 and the cushion support member 512. At this time, the container 15 (air cushion unit 100) holding the air cushion 10 is first disposed on the cushion support member 512 side, the second bead foam resin elastic member 30 is further on the surface side, and the second bead foam resin elastic member 30 is further on the surface side. One bead foamed resin elastic member 20 is disposed and then covered with a skin member 511. The cushion support member 512 can be formed, for example, by stretching a three-dimensional solid knitted fabric between the rear end edges of the pair of left and right side frames of the seat back portion 510, or can be formed from a synthetic resin plate. The skin member 511 can be provided, for example, by stretching a three-dimensional solid knitted fabric, synthetic leather, leather, or a laminate thereof between the front edges of a pair of left and right side frames.

As described above, in the present embodiment, the first bead foamed resin elastic member 20 and the second bead foam resin elastic member 30 having a predetermined size are laminated and arranged on the back surface side of the skin member 511, and thereafter Since the container 15 (air cushion unit 100) holding the pair of left and right air cushions 10 is arranged on the side, the seated person does not feel the unevenness of the air cushion 10 on the back, and measures a biological signal. Although it is the structure which has this air cushion 10, sitting comfort improves.

Next, the configuration of the biological state analyzer 60 will be described with reference to FIG.
The biological state analyzer 60 is configured by a computer, and as a computer program, second-order differential calculation means 61, second-order differential waveform power value slope calculation means 62, second-order differential waveform maximum Lyapunov exponent slope calculation means 63, second-order differential waveform. A sleep onset predictor determining unit 64, an original waveform power value inclination calculating unit 65, an original waveform maximum Lyapunov exponent inclination calculating unit 66, an original waveform entering sleep predictor determining unit 67, a comparison determining unit 68, and a warning control unit 69 are installed. Note that the computer program can be provided by being stored in a recording medium. A “recording medium” is a medium that can carry a program that cannot occupy space by itself, such as a flexible disk, hard disk, CD-ROM, MO (magneto-optical disk), DVD-ROM, etc. is there. It is also possible to transmit from a computer installed with the program according to the present invention to another computer through a communication line. Moreover, it is also possible to form a biological state analyzer by preinstalling or downloading the above-mentioned program to a general-purpose terminal device.

The second-order differential calculation means 61 second-order differentiates the original waveform of the time-series signal data of the air cushion 10 that is an electrical signal of the sensor 111b provided in the biological signal measuring device 1. Since the second-order differential waveform obtained by second-order differentiation of the original waveform of the time series signal data of the air cushion 10 can emphasize the change of the original waveform, the information indicating the biological state included in the original waveform is markedly shown. become.

The second-order differential waveform power value slope calculation means 62 calculates the difference between the peak value on the upper limit side and the peak value on the lower limit side for each predetermined time range from the peak value of each cycle of the second-order differential waveform obtained by the second-order differential calculation means 61. The difference is calculated, the difference is used as a power value, time series data of the power value is obtained, and the inclination of the power value with respect to the time axis in a predetermined time range is calculated by sliding a predetermined number of times.

The second-order differential waveform maximum Lyapunov exponent slope calculating means 63 performs chaos analysis on the second-order differential waveform obtained by the second-order differential calculation means 61, obtains the time series data of the maximum Lyapunov exponent obtained from the chaos analysis, and the maximum Lyapunov exponent. The inclination with respect to the time axis in a predetermined time range is calculated by sliding a predetermined number of times.

The second-order differential waveform onset symptom predicting means 64 has two slopes when the slope time series waveforms obtained by the second-order differential waveform power value slope calculating means 62 and the second-order differential waveform maximum Lyapunov exponent slope calculating means 63 are overlapped. A waveform in which the time series waveform has an antiphase relationship is determined as a sleep onset signal. More specifically, the time-series waveform of the slope of the second-order differential waveform power value and the time-series waveform of the slope of the second-order differential waveform maximum Lyapunov exponent are in opposite phases, and the slope of the second-order differential waveform power value A waveform in which a low-frequency and large-amplitude waveform is generated in the time-series waveform is determined as a sleep onset signal.

The original waveform power value slope calculating means 65, the original waveform maximum Lyapunov exponent slope calculating means 66, and the original waveform sleep onset predictor judging means 67 are not the second-order differential waveform but the biological signal measuring device 1 Is the original waveform of the time-series signal data of the air cushion 10 which is an electric signal of the sensor 111b provided in the sensor 111b, and the calculation method thereof is the above-described second-order differential waveform power value slope calculating means 62, second-order differential waveform maximum Lyapunov exponent. This is exactly the same as the slope calculating means 63 and the second-order differential waveform sleep onset determination means 64.

The comparison judgment means 68 is determined to have a sleep onset sign signal only by the second-order differential waveform onset sign determination means 64, or both the second-order differential waveform onset sign determination means 64 and the original waveform onset sign determination means 67 fall asleep at the same time period. A comparison is made to determine whether or not the predictive signal has been determined. Comparing the second-order differential waveform sleep onset predicting means 64 and the original waveform sleep onset predicting means 67, the former is considered to have a low threshold (high sensitivity) for detecting a change in the living body. Until then, it is possible to catch signs at a level closer to the awakening stage, whereas the latter is considered to have a high threshold (low sensitivity) to detect changes in the living body, so the awakening stage to the sleeping stage. Until it reaches 1, it can only catch signs of a level closer to sleep stage 1. Therefore, by comparing and determining which of the sleep onset sign signals is determined, it can be used for warning by the warning control means 69. For example, when a sleep onset symptom signal is determined only by the second-order differential waveform onset symptom determination means 64, a warning control means 69, which is a computer program, gives a command for operating a weak warning to a warning device (not shown). When both the second-order differential waveform onset symptom determination unit 64 and the original waveform onset symptom determination unit 67 determine that a sleep onset symptom signal is determined in the same time period, a command to activate a strong warning is set. Can do. Note that various devices such as a device that generates sound, a device that blinks light, and a device that changes the inclination angle of the seat back portion can be used as the warning device. Of course, two or more of these may be appropriately combined. For example, it emits a relatively small sound for a weak warning, a loud sound for a strong warning, or only a sound for a weak warning, and a sound for a strong warning. The light can also be controlled to blink.

(Test Example 1)
(Static load characteristics)
As shown in FIG. 7, when only the container 15 (air cushion unit 100) holding the air cushion 10 is placed alone on the measurement panel (data of “air pack” in FIG. 8), the air cushion 10 When the first bead foamed resin elastic member 20 is laminated on the container 15 (air cushion unit 100) that holds the air (the data of “A + air pack” in FIG. 8), the container 15 ( When the second bead foamed resin elastic member 30 is laminated on the air cushion unit 100) (data of “B + air pack” in FIG. 8), and the container 15 holding the air cushion 10 (air cushion unit 100) When the second bead foamed resin elastic member 30 is laminated thereon and the first bead foam resin elastic member 20 is further laminated thereon (“A + B in FIG. 8). + Air pack data), the position corresponding to the small air bag 111 provided with the sensor 111b was pressurized to a deflection of 1 mm with a pressure plate having a diameter of 30 mm, and the load-deflection characteristics were measured. The three-dimensional solid knitted fabric housed in the

air bags

111 and 121 of the air cushion 10 is a product number 49013D manufactured by Sumie Textile Co., Ltd. The main dimensions of each part are shown in FIGS. 5 (a) and 5 (b). It was as shown in.

The result is shown in FIG. The air pressure fluctuation generated by the pulse wave of the aorta at the back of the air cushion 10 held by the container 15 is the case when only the container 15 holding the air cushion 10 is placed alone (“air pack” in FIG. 8). It follows the load-deflection characteristics. Therefore, when the spring constant becomes higher than this load-deflection characteristic, the sensitivity of the pulse wave of the aorta becomes duller than when the container 15 holding the air cushion 10 is arranged directly on the back side of the skin member 511. It will be. Therefore, when comparing the spring constants obtained from the respective load-deflection characteristics, when only one of the first or second bead foamed resin

elastic members

20 and 30 is laminated with the container 15 holding the air cushion 10 ( When both the first and second bead foamed resin

elastic members

20 and 30 are laminated (“A + B + air pack” in FIG. 8) rather than “A + air pack” and “B + air pack” in FIG. However, it can be seen that it is close to the spring constant when measured by placing only the container 15 holding the air cushion 10 alone. Therefore, when both the first and second bead foamed resin

elastic members

20 and 30 are laminated, the aortic pulse wave is almost attenuated even though they are laminated on the air cushion 10. It is possible to transmit without using the air cushion 10, and it is less likely to feel a foreign object as when the air cushion 10 is used alone.

When only a plurality of first bead foam resin elastic members 20 are laminated or when only a plurality of second bead resin foam elastic members 30 are laminated, “A + air pack” in FIG. 8 respectively. Therefore, the first bead foam resin elastic member 20 and the second bead foam resin elastic member 30 have different spring constants. Are preferably overlapped. From the experimental results of FIG. 8, it is preferable that the spring constant of the second bead foamed resin elastic member 30 is in a range of 1.1 to 1.4 times the spring constant of the first bead foamed resin elastic member 20. As described above, the first bead foamed resin elastic member 20 is covered with a relatively elastic elastic nonwoven fabric, and the second beaded foam elastic member 30 is relatively stretchable as described above. It is given by coating with a small non-woven fabric. Further, when the second bead foamed resin elastic member 30 is laminated on the container 15 holding the air cushion 10 and the first bead foam resin elastic member 20 is further laminated thereon (“A + B + air in FIG. 8). The spring constant of the “pack”) is preferably in the range of 0.8 to 1.2 times the spring constant indicated by the “air pack” of FIG. 8 corresponding to the spring constant of the air cushion 10 only. .

(Test Example 2)
(Influence of disturbance vibration)
As shown in FIG. 9, the container 15 (the same structure and size as in Test Example 1) holding the air cushion 10 is placed on the vibration table of the vibration exciter, and on the upper surface thereof, The second bead foam resin elastic member 30 and the first bead foam resin elastic member 20 are sequentially laminated (in FIG. 9, the second bead foam resin elastic member 30 and the first bead foam resin elastic member 20 are stacked. The combined material is displayed as “buffer material” (the same form as “A + B + air pack” in FIG. 8), and a 2 kg weight is placed on the first beaded foamed elastic resin member 20 and the amplitude is 1 mm and 1.0 Hz to The vibration was applied in increments of 0.5 Hz up to 10 Hz (Test Example 2-A). Moreover, it replaces with the container 15 holding the air cushion 10, and arrange | positions one small air bag 111 on a vibration stand, on it, the 2nd bead foam resin elastic member 30 and the 1st bead foam resin The elastic members 20 were laminated in order, and further a 2 kg weight was placed thereon and vibrated in the same manner (Comparative Example 2-A). In addition, the PEN film is affixed on the surface facing the skin member 511 of the first bead foamed resin elastic member 20 and the surface facing the air cushion unit 100 of the second bead foamed resin elastic member 30. . And the output voltage of the sensor 111b (capacitor-type microphone sensor) provided in the small air bag 111 was measured for each. The results are shown in FIGS.

From FIG. 10 to FIG. 14, in the case of Test Example 2-A, there is almost no change in the output voltage at any frequency from 1.0 Hz to 10 Hz, whereas in the case of Comparative Example 2-A. The change in the output voltage is relatively larger than in Test Example 2-A. Therefore, by adopting the configuration of Test Example 2-A, the influence of the external vibration from the seat back portion 510 on the output voltage becomes extremely small. On the other hand, the biological signal input from the first and second bead foamed resin

elastic members

20 and 30 side can be regarded as a change in output voltage by the sensor 111b as in Test Example 3 described later.

(Test Example 3)
(Influence of disturbance vibration and detection of biological signals)
As shown in FIG. 15 (a), a three-dimensional solid knitted fabric (3D net) corresponding to the cushion support member 512 in the seat back portion 510 and a container holding the air cushion 10 on the vibration exciter of the vibration exciter. 15 (air cushion unit 100, the same structure and size as those of Test Example 1), the second bead foam resin elastic member 30, the first bead foam resin elastic member 20, and the skin member 511 in the seat back portion 510. Three-dimensional three-dimensional knitted fabric (3D net) was laminated in order, a 2 kg weight was placed thereon, and vibration was applied at 1.0 Hz, 1.5 Hz, and 2.0 Hz with an amplitude of 1 mm and close to the frequency of the aortic pulse wave. FIG. 15A shows the influence of disturbance vibration received when the biological signal measuring device 1 of this embodiment is actually incorporated into the seat back portion 510 by inputting vibration from the cushion support member 512 side. It is.

On the other hand, FIG. 15 (b) is arranged in the reverse order to FIG. 15 (a). That is, a container holding a three-dimensional solid knitted fabric (3D net) corresponding to the skin member 511 in the seat back portion 510, the first bead foam resin elastic member 20, the second bead foam resin elastic member 30, and the air cushion 10. 15 (air cushion unit 100, the same structure and size as those of Test Example 1) and a three-dimensional solid knitted fabric (3D net) corresponding to the cushion support member 512 are laminated in this order. By oscillating in this state, it is possible to examine the detection sensitivity of the pulse wave of the back aorta inputted from the skin member 511 side. The reason why the weight is set to 2 kg is that when a person is seated, the load applied to the seat back portion 510 where the air cushion unit 100 is disposed from the waist corresponds to 2 kg in an area of 98 mm in diameter. .

The results are shown in FIGS. In the figure, “air pack disturbance simulation” is the result of FIG. 15A, and “air pack biological signal simulation” is the result of FIG. 15B. In addition, the input waveform of the vibrator is also shown. From these figures, it can be seen that the “air pack disturbance simulation” is in a state close to a straight line with almost no amplitude, and disturbance vibrations are almost eliminated. On the contrary, it can be seen that the “air pack biosignal simulation” is amplified more than the input waveform. Therefore, according to the configuration of the present embodiment, the pulse wave of the aorta in the vicinity of 1.0 Hz to 2.0 Hz is buried in the disturbance vibration even under dynamic conditions in which disturbance vibration is input such as when riding. It can be said that it can be reliably detected without causing it to occur.

(Test Example 4)
(Measurement of biological signals)
As shown in FIG. 2, a container 15 (the same structure and size as those of the air cushion unit 100, Test Example 1) holding the air cushion 10 described in the above embodiment on the seat back portion 510 of the seat 500, The second bead foam resin elastic member 30 and the first bead foam resin elastic member 20 were accommodated in this order. The skin member 511 used for the seat back portion 510 is a three-dimensional solid knitted fabric (manufactured by Sumie Textile Co., Ltd., product number 49013D). Further, the intersection of the side edge and the lower edge near the center of the seat back part 510 of the central small air bag 111 (width 60 mm, length 160 mm) constituting the air cushion 10 on the left side of the occupant provided with the sensor 111b. The seat back portion 510 was incorporated such that P was 220 mm from the upper surface of the seat cushion portion 520 along the surface of the seat back

portion

510 and 80 mm from the center of the seat back portion 510. Then, a biological signal analyzing means 60 comprising a computer for analyzing the state of the person based on the air pressure fluctuation obtained by measuring the electric signal from the sensor 111b of the small air bag 111 is arranged (see FIG. 1). A Japanese male of the age group was seated on the seat 500 and the pulse wave of the aorta near the lumbar region was collected. Each subject was also equipped with a fingertip plethysmograph (manufactured by Amco, finger clip probe SR-5C) to measure the fingertip plethysmogram and a simple electroencephalograph (Futech Electronics ( EEG measurement was also performed by wearing FM-515A). In addition, an electrocardiograph (NEC Kogyo Co., Ltd. ECG-9122) was also attached.

FIG. 19 shows the original waveform of the aortic pulse wave (air pack pulse wave) near the lumbar region of the subject, and FIG. 20 shows the second-order differential waveform (the air pack pulse wave second-order differential) obtained by the second-order differential calculation means 61. Waveform). FIG. 21 shows the original waveform of the fingertip volume pulse wave. 22 and 23 are partially enlarged views of the air pack pulse wave shown in FIG. 19 and the fingertip volume pulse wave shown in FIG. 22 and 23, it can be seen that the air pack pulse wave captures a notch (a signal indicating that the aorta suddenly closes at the end of the stroke period) included in the pressure waveform of the aorta. The ability to capture this notch is a proof that the biological information is reliably captured. Therefore, the air pack pulse wave has substantially the same phase difference and cycle as the fingertip volume pulse wave. FIG. 24A shows the frequency analysis results of the air pack pulse wave and fingertip volume pulse wave of FIGS. 19 and 21, and FIG. 24B shows the air pack pulse wave second-order differential waveform of FIG. It is a figure which shows the frequency analysis result of a cusp volume pulse wave. From this figure, it can be seen that both the air pack pulse wave and its second-order differential waveform have peaks at the same frequency as the fingertip volume pulse wave.

FIG. 25A shows a time-series waveform of the original waveform power value of the airpack pulse wave and the inclination of the original maximum Lyapunov exponent determined by the original waveform power value inclination calculator 65 and the original waveform maximum Lyapunov exponent slope calculator 66. It is. FIG. 25B shows the second-order differential waveform power value of the second-order differential waveform power value slope calculating means 62, the second-order differential waveform power value obtained by the second-order differential waveform maximum Lyapunov exponent slope calculating means 63, and 2 This is a time-series waveform of the gradient of the highest differential Lyapunov exponent. FIG. 25C is a time-series waveform of the power value of the fingertip volume pulse wave and the slope of the maximum Lyapunov exponent obtained from the fingertip volume pulse wave for verification.

From FIG. 25 (b), in the case of the airpack pulse wave second-order differential waveform, the time-series waveform of the slope of the second-order differential waveform power value and the time series of the slope of the second-order differential waveform maximum Lyapunov exponent are around 300 seconds. Since the waveform is in antiphase and the time-series waveform of the slope of the second-order differential waveform power value is low frequency and large amplitude, the first sleep predictive signal may appear at this point Recognize. In the fingertip volume pulse wave of FIG. 25 (c) used for the verification, a similar sleep prediction signal appears. Then, in the distribution rate of the electroencephalogram in FIG. 25 (d), a state of approaching from the awakening phase to the sleep phase 1 appears that the appearance rate of the α wave decreases and the θ wave increases from 600 seconds to 1000 seconds. Therefore, the sleep onset sign signal in the vicinity of 300 seconds is considered to be a sign of this state. However, in the case of the air pack pulse wave of FIG. 25 (a), such a sleep prediction signal does not appear.

On the other hand, in the range of 1200 seconds to 1400 seconds, it can be seen that a sleep onset sign signal appears in any of the air pack pulse wave, the air pack pulse wave second-order differential waveform, and the fingertip volume pulse wave. In the electroencephalogram distribution rate, a decrease in α-wave and an increase in θ-wave are observed in the vicinity of 1400 seconds.

From the above test example, when the analysis using the second-order differential waveform of the air pack pulse wave is performed, a result closer to the case where the analysis is performed using the fingertip volume pulse wave than when the air pack pulse wave is used It can be seen that Therefore, if the biological signal measuring device 1 having the air cushion 10 described above is disposed on a vehicle seat or the like by using the air pack pulse wave second-order differential waveform, the driving operation is not hindered. It is possible to make the same determination as when a person's biological state (such as sleepiness) is analyzed by measuring fingertip volume pulse waves. That is, the air pack pulse wave second-order differential waveform corresponds to the fingertip volume pulse wave.

Further, since the difference as described above occurs between the case where the air pack pulse wave is used and the case where the air pack pulse wave second-order differential waveform is used, it is determined by the comparison determination means 68 which of the sleep onset predictor signals has been determined. As described above, it can be used for warning control by the warning control means 69.

26 (a) and 26 (b) show the frequency analysis results of the three types of gradient time-series waveforms in FIG. From this figure, as for the power value, the fingertip volume pulse wave and the air pack pulse wave second-order differential waveform have a large power spectrum, and their absolute values are also at the same level. On the other hand, the power spectrum of the air pack pulse wave becomes extremely small. The time series waveforms of the maximum Lyapunov exponent slopes of the fingertip volume pulse wave and the air pack pulse wave second-order differential waveform are at the same level, but the maximum Lyapunov exponent of the air pack pulse wave is extremely high. Therefore, the fingertip volume pulse wave and the air pack pulse wave second-order differential waveform have a good balance between the power value and the power spectrum of the maximum Lyapunov exponent, and the two antagonizing actions smoothly mesh and balance each other. Recognize. On the other hand, the air pack pulse wave has two unstable actions, and changes greatly with a small stress. This difference influences the sensitivity between the detection of the sleep onset signal using the air pack pulse wave and the detection of the sleep onset signal using the air pack pulse wave second-order differential waveform.

FIGS. 27A to 27D show the results of wavelet analysis of heart rate fluctuations obtained from an air pack pulse wave, air pack pulse wave second-order differential waveform, fingertip volume pulse wave, and electrocardiograph. Show. The LF / HF component is an index indicating the state of sympathetic nerve activity, and the HF component is an index of parasympathetic nerve activity. In the process of transitioning from the awakening stage to the sleep stage 1, the physiological significance of the change of the HF component is small compared to the LF / HF component, and therefore, the analysis was conducted focusing on the LF / HF component. In the case of the LF / HF component obtained from the fingertip volume pulse wave, the air pack pulse wave second-order differential waveform, and the heart rate fluctuation, an increase is observed in one shot in the vicinity of 150 seconds. This is probably because the sympathetic nerve was activated before the onset of sleep signal in the vicinity of 300 seconds in the gradient time-series waveform of FIG. Furthermore, in the heart rate variability analysis, the frequency of LF / HF bursts decreased at 450 to 900 seconds and 1200 to 1400 seconds, and it is considered that the level of sympathetic nerve activity in the autonomic nerve decreased during these periods. These periods coincided with the appearance period of the sleep onset sign signal detected from the tilt time-series waveform of FIG. 25 and the period of change in the sleep stage of the electroencephalogram.

FIG. 28A shows the acceleration pulse wave aging index (SDPTGAI) obtained from the acceleration pulse wave of the fingertip volume pulse wave and the acceleration pulse wave aging index obtained from the acceleration pulse wave of the air pack pulse wave. FIG. 28B shows the values of the series, and FIG. 28B shows the acceleration obtained from the acceleration pulse wave aging index obtained from the acceleration pulse wave of the fingertip volume pulse wave and the acceleration pulse wave of the air pack pulse wave second-order differential waveform. The time series value of the pulse wave aging index is shown. It is known that SDPTGAI changes under the influence of both organic vascular wall sclerosis (arteriosclerosis in lifestyle-related diseases) and functional vascular wall tension. The SDPTGAI obtained from the air pack pulse wave second-order differential waveform shows a value close to the SDPTGAI obtained from the fingertip volume pulse wave, although there is some variation. On the other hand, the SDPTGAI obtained from the air pack pulse wave has a large variation compared to the case of the air pack pulse wave second-order differential waveform, and the air pack pulse wave second-order differential waveform shows organic vascular wall hardening and functional vascular tone. It can be seen that the blood vessel state can be captured in the same manner as the fingertip plethysmogram.

In the above embodiment, the air cushion 10 and the first and second bead foamed resin

elastic members

20 and 30 are incorporated in the automobile seat as the human body support means, but the human body support means may be a bed or the like. It can also be incorporated into bedding, diagnostic chairs in hospital equipment, and the like. Further, in the above embodiment, the pulse wave of the back aorta is detected using an air cushion incorporated in the seat back part.For example, by installing this air cushion around a person's wrist, the transverse artery, Arterial pulse waves can also be collected from the ulnar artery. Other locations where arterial pulse waves can be collected include superficial temporal artery, carotid artery, subclavian artery, brachial artery, abdominal aorta, femoral artery, popliteal artery, posterior tibial artery, and dorsal artery It is also possible to collect these pulse waves and analyze the human condition using a second-order differential technique. However, when analyzing the driver's condition, it can be measured without restraining the driver's hand, arm, neck, foot, etc., so the pulse wave of the aorta near the waist is detected as in the above embodiment. It is desirable.

DESCRIPTION OF SYMBOLS 1 Biosignal measuring apparatus 10 Air cushion 11 Front side air cushion 111 Small air bag 112 Three-dimensional solid knitted fabric 12 Back side air cushion 121 Large air bag 122 Three-dimensional solid knitted fabric 15 Housing 100 Air cushion unit 20 1st bead foam resin elastic member 30 Second bead foamed resin

elastic member

40, 45 Three-dimensional solid knitted fabric 60 Biological state analyzer 61 Second-order differential calculation means 62 Second-order differential waveform power value inclination calculation means 63 Second-order differential waveform maximum Lyapunov exponent inclination calculation means 64 2 Step differential waveform sleep onset determination means 65 Original waveform power value inclination calculation means 66 Original waveform maximum Lyapunov exponent inclination calculation means 67 Original waveform sleep onset sign determination means 68 Comparison determination means 69 Warning control means 500 Seat 510 Seat back portion 511 Skin member 512 Cushion Support member 52 Seat cushion

Claims

In the human body support means, an air cushion provided with an air bag incorporated between an epidermis member of a part supporting at least the vicinity of the human lumbar region and a cushion support member disposed on the back side of the epidermis member, and an arterial pulse Receiving a time series signal data of the sensor due to air pressure fluctuation from a biological signal measuring device comprising a sensor for detecting air pressure fluctuation of the air bag accompanying a wave, processing the time series signal data, and processing the human body A biological state analyzer for analyzing the state of a person supported by a support means,
Biological state analysis characterized by comprising second-order differential operation means for second-order differentiation of the time-series signal data, and analyzing a human state by processing a second-order differential waveform obtained by the second-order differential operation means apparatus.
Further, the difference between the peak value on the upper limit side and the peak value on the lower limit side is calculated for each predetermined time range from the peak value of each cycle of the second-order differential waveform obtained by the second-order differential calculation means, and this difference is calculated as the power. A second-order differential waveform power value slope calculating means for obtaining time series data of power values and obtaining a slope of the power value with respect to the time axis in a predetermined time range by sliding calculation a predetermined number of times;
Second-order differentiation obtained by obtaining time-series data of the maximum Lyapunov exponent from the second-order differential waveform obtained by the second-order differentiation calculation means and slidingly calculating the slope of the maximum Lyapunov exponent with respect to the time axis in a predetermined time range by a predetermined number of times. Waveform maximum Lyapunov exponent slope calculation means,
When the slope time-series waveforms obtained by the second-order differential waveform power value slope calculating means and the second-order differential waveform maximum Lyapunov exponent slope calculating means are overlapped, the two slope time-series waveforms have an antiphase relationship. The biological state analysis apparatus according to claim 1, further comprising second-order differential waveform sleep onset predicting means for determining a waveform as a sleep onset sign signal.
Further, the difference between the peak value on the upper limit side and the peak value on the lower limit side is calculated for each predetermined time range from the peak value of each period of the time series signal data of the air pressure fluctuation detected by the air cushion, and this difference is calculated. An original waveform power value slope calculating means for obtaining a power value, obtaining time-series data of the power value, and obtaining a slope with respect to a time axis in a predetermined time range by a predetermined number of slide calculations;
From the time series signal data of the air pressure fluctuation detected by the air cushion, the time series data of the maximum Lyapunov exponent is obtained, and the original waveform obtained by sliding the slope of the maximum Lyapunov exponent with respect to the time axis in a given time range a predetermined number of times Means for calculating the maximum Lyapunov exponent slope;
When the respective slope time series waveforms obtained by the original waveform power value slope calculating means and the original waveform maximum Lyapunov exponent slope calculating means are overlapped, a waveform in which the two slope time series waveforms are in an antiphase relationship is displayed. An original waveform sleep onset predictor determining means for determining a signal;
Whether a sleep onset sign signal has been determined only by the second-order differential waveform sleep onset determination means, or whether both of the second-order differential waveform sleep onset determination means and the original waveform onset sign determination means have determined a sleep onset sign signal in the same time period A comparison judgment means for making a comparison judgment;
3. The biological state analyzing apparatus according to claim 2, further comprising warning control means for operating a warning for being awakened according to a comparison result of each ratio judging means.
The warning control means includes a case in which the comparison judgment means determines that a sleep onset sign signal is determined only by the second-order differential waveform sleep onset determination means, and the second-order differential waveform sleep onset prediction determination means and the original waveform onset sleep prediction determination means. 4. The biological state analyzing apparatus according to claim 3, wherein different types of warnings are made to function depending on whether the sleep onset symptom signal is determined in the same time zone in both cases.
In the human body support means, an air cushion provided with an air bag incorporated between an epidermis member of a part supporting at least the vicinity of the human lumbar region and a cushion support member disposed on the back side of the epidermis member, and an arterial pulse Receiving a time series signal data of the sensor due to air pressure fluctuation from a biological signal measuring device comprising a sensor for detecting air pressure fluctuation of the air bag accompanying a wave, processing the time series signal data, and processing the human body A computer program installed in a biological state analyzer for analyzing the state of a person supported by a support means,
A computer program comprising second-order differential calculation means for second-order differentiation of the time-series signal data, and processing a second-order differential waveform obtained by the second-order differential calculation means to analyze a human state.
Further, the difference between the peak value on the upper limit side and the peak value on the lower limit side is calculated for each predetermined time range from the peak value of each cycle of the second-order differential waveform obtained by the second-order differential calculation means, and this difference is calculated as the power. A second-order differential waveform power value slope calculating means for obtaining time series data of power values and obtaining a slope of the power value with respect to the time axis in a predetermined time range by sliding calculation a predetermined number of times;
Second-order differentiation obtained by obtaining time-series data of the maximum Lyapunov exponent from the second-order differential waveform obtained by the second-order differentiation calculation means and slidingly calculating the slope of the maximum Lyapunov exponent with respect to the time axis in a predetermined time range by a predetermined number of times. Waveform maximum Lyapunov exponent slope calculation means,
When the slope time-series waveforms obtained by the second-order differential waveform power value slope calculating means and the second-order differential waveform maximum Lyapunov exponent slope calculating means are overlapped, the two slope time-series waveforms have an antiphase relationship. 6. The computer program according to claim 5, further comprising second-order differential waveform sleep onset predicting means for determining a waveform as a sleep onset sign signal.
Further, the difference between the peak value on the upper limit side and the peak value on the lower limit side is calculated for each predetermined time range from the peak value of each period of the time series signal data of the air pressure fluctuation detected by the air cushion, and this difference is calculated. An original waveform power value slope calculating means for obtaining a power value, obtaining time-series data of the power value, and obtaining a slope with respect to a time axis in a predetermined time range by a predetermined number of slide calculations;
From the time series signal data of the air pressure fluctuation detected by the air cushion, the time series data of the maximum Lyapunov exponent is obtained, and the original waveform obtained by sliding the slope of the maximum Lyapunov exponent with respect to the time axis in a given time range a predetermined number of times Means for calculating the maximum Lyapunov exponent slope;
When the respective slope time series waveforms obtained by the original waveform power value slope calculating means and the original waveform maximum Lyapunov exponent slope calculating means are overlapped, a waveform in which the two slope time series waveforms are in an antiphase relationship is displayed. An original waveform sleep onset predictor determining means for determining a signal;
Whether a sleep onset sign signal has been determined only by the second-order differential waveform sleep onset determination means, or whether both of the second-order differential waveform sleep onset determination means and the original waveform onset sign determination means have determined a sleep onset sign signal in the same time period A comparison judgment means for making a comparison judgment;
7. The computer program according to claim 6, further comprising warning control means for operating a warning to be awakened according to a comparison result of each ratio judging means.
The warning control means includes a case in which the comparison judgment means determines that a sleep onset sign signal is determined only by the second-order differential waveform sleep onset determination means, and the second-order differential waveform sleep onset prediction determination means and the original waveform onset sleep prediction determination means. 8. The computer program according to claim 7, wherein different types of warnings are made to function depending on whether the sleep onset symptom signal is determined in the same time zone in both cases.
A computer-readable recording medium on which the computer program according to any one of claims 5 to 8 is recorded.