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WO2010021227A1 - Analyseur d'état d'organisme, programme informatique et support d'enregistrement - Google Patents

Analyseur d'état d'organisme, programme informatique et support d'enregistrement Download PDF

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Publication number
WO2010021227A1
WO2010021227A1 PCT/JP2009/063390 JP2009063390W WO2010021227A1 WO 2010021227 A1 WO2010021227 A1 WO 2010021227A1 JP 2009063390 W JP2009063390 W JP 2009063390W WO 2010021227 A1 WO2010021227 A1 WO 2010021227A1
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WIPO (PCT)
Prior art keywords
waveform
slope
order differential
time
sleep onset
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Ceased
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PCT/JP2009/063390
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English (en)
Japanese (ja)
Inventor
悦則 藤田
由美 小倉
慎一郎 前田
重行 小島
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Delta Tooling Co Ltd
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Delta Tooling Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6892—Mats
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K28/00—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions
    • B60K28/02—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions responsive to conditions relating to the driver
    • B60K28/06—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions responsive to conditions relating to the driver responsive to incapacity of driver
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0247—Pressure sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles

Definitions

  • 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.
  • Patent Document 1 In recent years, monitoring the biological state of a driver during driving has attracted attention as an accident prevention measure.
  • 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
  • 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.
  • the slope of the power value is obtained by the least square method for a certain time width Tw (180 seconds).
  • Tw time width
  • Tw time width
  • Tl overlap time
  • 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.
  • 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.
  • 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.
  • a living body state analyzing apparatus 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.
  • 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.
  • 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;
  • 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.
  • 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.
  • 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.
  • 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;
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. 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
  • 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. 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. 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. 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.
  • 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
  • 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
  • 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.
  • FIG. 27A shows an air pack pulse wave (“Air-pack sensor” in the figure)
  • 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)
  • 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. 27A shows an air pack pulse wave (“Air-pack sensor” in the figure)
  • 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)
  • 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
  • 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
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the pulse wave of the aorta could be detected in all the above subjects.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • a knitted structure having a honeycomb-shaped (hexagonal) mesh is formed from a yarn obtained by twisting short fibers.
  • 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.
  • 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.
  • 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
  • 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.
  • a foam molded body by a resin bead method containing at least one of polystyrene, polypropylene and polyethylene is used as the bead foam.
  • 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.
  • thermoplastic elastomer elastic fibers disclosed in Japanese Patent Application Laid-Open No. 2007-92217 are melt-bonded to each other.
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a warning control means 69 which is a computer program, gives a command for operating a weak warning to a warning device (not shown).
  • a command to activate a strong warning is set.
  • 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.
  • the warning device 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • Test Example 3 Influence of disturbance vibration and detection of biological signals
  • 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.
  • air cushion unit 100 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
  • the skin member 511 in the seat back portion 510 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.
  • 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.
  • 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.
  • 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. .
  • Test Example 4 Measurement of biological signals
  • 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).
  • 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.
  • 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).
  • 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
  • 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.
  • a notch a signal indicating that the aorta suddenly closes at the end of the stroke period
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • the HF component is an index of parasympathetic nerve activity.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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Abstract

L'invention porte sur un signal biométrique significatif qui peut être détecté sans donner de sensation étrange et qui permet d'analyser correctement l'état d'une personne. L'analyseur d'état d'organisme comprend un moyen de différenciation du second ordre (61) pour différentier deux fois des données de signaux de série temporelle concernant une variation de pression de l'air détecté par un coussin d'air. La forme d'onde de différenciation du second ordre obtenue par le moyen de différenciation du second ordre (61) est traitée pour analyser l'état d'une personne. Les données de signal de série temporelle représentent la pulsation d'une aorte dans le dos d'une personne, et la différenciation du second ordre met en évidence la variation de la forme d'onde des données de signal de série temporelle. Les informations représentant l'état de l'organisme, comprises dans la forme d'onde obtenue par la différenciation du second ordre, sont plus significatives que celles représentant l'état de l'organisme comprises dans la forme d'onde avant la différenciation du second ordre. Il en résulte qu'une analyse de la forme d'onde de série temporelle obtenue par la différenciation du second ordre donne un résultat d'analyse plus précis que celui obtenu par un moyen classique d'analyse d'état d'organisme à l'aide de la pulsation de l'aorte.
PCT/JP2009/063390 2008-08-20 2009-07-28 Analyseur d'état d'organisme, programme informatique et support d'enregistrement Ceased WO2010021227A1 (fr)

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