WO2009104460A1 - Dispositif d'analyse de fatigue et programme d'ordinateur - Google Patents

Dispositif d'analyse de fatigue et programme d'ordinateur Download PDF

Info

Publication number
WO2009104460A1
WO2009104460A1 PCT/JP2009/051366 JP2009051366W WO2009104460A1 WO 2009104460 A1 WO2009104460 A1 WO 2009104460A1 JP 2009051366 W JP2009051366 W JP 2009051366W WO 2009104460 A1 WO2009104460 A1 WO 2009104460A1
Authority
WO
WIPO (PCT)
Prior art keywords
fatigue
power value
value
slope
calculating means
Prior art date
Application number
PCT/JP2009/051366
Other languages
English (en)
Japanese (ja)
Inventor
悦則 藤田
直輝 落合
重行 小島
由美 小倉
Original Assignee
株式会社デルタツーリング
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社デルタツーリング filed Critical 株式会社デルタツーリング
Publication of WO2009104460A1 publication Critical patent/WO2009104460A1/fr

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/388—Nerve conduction study, e.g. detecting action potential of peripheral nerves
    • 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 pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • 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 pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4029—Detecting, measuring or recording for evaluating the nervous system for evaluating the peripheral nervous systems
    • A61B5/4035—Evaluating the autonomic nervous system

Definitions

  • the present invention relates to a fatigue analysis apparatus for obtaining a degree of fatigue as a degree of human fatigue and a computer program used therefor.
  • Patent Document 1 describes the degree of fatigue of a person by comparing the amount of energy by the work of muscles used for maintaining the homeostasis taken by a person with fatigue by the amount of metabolism of the product by the work. The technique calculated
  • This technology collects human biological signals, 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 original waveform of the biological signal data, Power value calculating means for calculating the difference as a power value, power value inclination calculating means for calculating the inclination of the power value with respect to the time axis in a predetermined time range by slide calculation a predetermined number of times, and the inclination of the obtained power value And a means for calculating an integral value by processing an absolute value of the time series signal, and the obtained integral value is obtained as a fatigue level.
  • Patent Document 1 is based on the premise of seeking fatigue that occurs in a mentally relaxed state, and if the fatigue is covered (compensated) by the sympathetic nerve function and does not appear as muscle contraction, the sympathetic nerve activity Even if the blood flow changes due to, it is not considered as fatigue level.
  • sympathetic nerve activity caused mental fatigue and accompanying changes in blood flow, and this was considered to be the main cause of the case where a deviation from the sensory evaluation value occurred.
  • the present invention has been made in view of the above, and it is an object of the present invention to provide a fatigue analysis apparatus and a computer program capable of obtaining a fatigue level closer to a sensory evaluation value in consideration of a metabolic amount of compensation due to sympathetic activity. To do.
  • a fatigue analysis apparatus is a fatigue analysis apparatus that performs fatigue analysis using pulse wave biosignal data collected by a biosignal measuring device, and is an original waveform of the biosignal data
  • 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, and this difference is used as the power value.
  • the standard fatigue is calculated. Obtained by correcting the time series data of the power value used in the first fatigue degree calculating means using the first fatigue degree calculating means for obtaining the degree and the maximum Lyapunov exponent obtained from the biological signal data.
  • a second fatigue degree calculating means for obtaining a corrected fatigue level based on the corrected power value time-series data, a determination means for determining whether or not there is a compensation effect of fatigue due to the sympathetic nerve activity, and a sympathetic nerve activity by the determination means Fatigue due to In a time zone in which it is determined that no compensation has been made, a cumulative sum of the reference fatigue levels obtained by the first fatigue level calculation means is obtained and output, and a state in which compensation for fatigue due to sympathetic nerve activity is being made
  • the determined time zone includes a cumulative fatigue level output unit that calculates and outputs a cumulative sum of the corrected fatigue levels obtained by the second fatigue level calculation unit.
  • the determination means obtains a maximum Lyapunov exponent from the vital sign data, and calculates a degree of arousal based on time series data of the maximum Lyapunov exponent, and arousal degree obtained by the arousal degree calculation means , And a timing determination unit that determines a timing at which the corrected fatigue level of the second fatigue level calculation unit is adopted, and the cumulative fatigue level output unit includes the second fatigue level calculation unit by the timing determination unit.
  • a cumulative sum of the reference fatigue levels obtained by the first fatigue level calculation unit is obtained and output, and the corrected fatigue level of the second fatigue level calculation unit is calculated.
  • the cumulative fatigue correction degree obtained by the second fatigue degree calculating means is calculated and output.
  • Door is preferable.
  • the first fatigue level calculating means 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 original waveform of the biological signal data,
  • a power value calculating means for calculating a time series data of the power value using the difference as the power value;
  • a power value inclination calculating means for obtaining a slope of the power value with respect to a time axis in a predetermined time range by performing slide calculation a predetermined number of times;
  • a power value slope integrating means for performing absolute value processing on a time series signal of the power value slope obtained by the slide calculation by the power value slope calculating means, and calculating an integral value for each predetermined time range as the reference fatigue level; It is preferable to comprise.
  • LF / HF time series data calculating means for obtaining time series data of the power spectrum of LF / HF obtained by frequency analysis of time series data of heart rate or pulse rate collected by the biological signal measuring instrument
  • the power value calculation means multiplies the value of the time series data of the power spectrum of the LF / HF and the value of the time series data of the power value by the values corresponding to each other and LF / HF It is preferably set to calculate the time series data of the LF / HF included power value as the calculated power value.
  • the second fatigue level calculating means includes the value of the time series data of the maximum Lyapunov exponent used in the arousal level calculating means and the value of the time series data of the power value used in the first fatigue level calculating means.
  • a correction power value calculating means for calculating the time series data of the correction power value by multiplying the values corresponding to each other at the corresponding time, and the inclination of the correction power value with respect to the time axis in a predetermined time range
  • Correction power value inclination calculation means obtained by sliding calculation a predetermined number of times, and time-series signals of the correction power value inclination obtained by slide calculation by the correction power value calculation means are subjected to absolute value processing for each predetermined time range.
  • the correction power value gradient integration means for calculating the integrated value as the corrected fatigue degree can be provided.
  • the second fatigue level calculating means includes the time series data value of the maximum Lyapunov exponent used in the arousal level calculating means and the time series data of the LF / HF included power value used in the first fatigue level calculating means.
  • a correction power value calculation means for calculating time series data of the correction power value by multiplying the values of the correction values by values at times corresponding to each other, and a correction power value in a predetermined time range of the correction power value
  • a correction power value inclination calculating means for obtaining the inclination with respect to the time axis by slide calculation a predetermined number of times, and a time series signal of the inclination of the correction power value obtained by the slide calculation by the correction power value calculation means are subjected to absolute value processing,
  • a correction power value gradient integration means for calculating an integrated value for each predetermined time range as the corrected fatigue level may be provided.
  • the arousal degree calculating means calculates a maximum Lyapunov exponent from the vital sign data and calculates time series data of the maximum Lyapunov exponent, and a slope of the maximum Lyapunov exponent with respect to a time axis in a predetermined time range.
  • the maximum Lyapunov exponent slope calculating means obtained by sliding calculation a predetermined number of times, and the time series signal of the slope of the maximum Lyapunov exponent slope obtained by the slide calculation by the maximum Lyapunov exponent slope calculating means is subjected to absolute value processing, and a predetermined time range.
  • a maximum Lyapunov exponent slope integration means for calculating the integral value for each arousal level, and a cumulative arousal level for obtaining and outputting a cumulative sum of the arousal levels obtained by the maximum Lyapunov exponent slope integration means Output means.
  • the timing determination unit is configured to determine the sudden conversion point of the slope of the cumulative arousal level output from the cumulative arousal level output unit as a timing to employ the corrected fatigue level of the second fatigue level calculation unit. It is preferable.
  • the timing determination means obtains the slope of the cumulative arousal degree in a predetermined time range from the start of measurement as a reference slope, and the slope of the cumulative arousal degree in the predetermined time range in the time zone after obtaining the reference slope is a reference It is preferable that the abrupt conversion point is determined when a change of at least 10% occurs with respect to the inclination.
  • the computer program of the present invention is a computer program introduced into a fatigue analysis apparatus that performs fatigue analysis using pulse wave biosignal data collected by a biosignal measuring device, and the original waveform of the biosignal data
  • 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, and this difference is used as the power value.
  • the standard fatigue is calculated. Obtained by correcting the time series data of the power value used in the first fatigue degree calculating means using the first fatigue degree calculating means for obtaining the degree and the maximum Lyapunov exponent obtained from the biological signal data.
  • a second fatigue level calculating means for obtaining a corrected fatigue level based on the corrected power value time-series data, a determination means for determining the presence or absence of a compensation effect of fatigue due to sympathetic nerve activity, and the determination means In a time zone in which it is determined that the fatigue due to the sympathetic nerve activity is not compensated, the cumulative sum of the reference fatigue levels obtained by the first fatigue level calculating means is obtained and output, and the fatigue level due to the sympathetic nerve activity is calculated. And a cumulative fatigue level output means for obtaining and outputting a cumulative sum of the corrected fatigue levels obtained by the second fatigue level calculation means in a time zone determined to be in a state of being compensated. .
  • the determination means obtains a maximum Lyapunov exponent from the vital sign data, and calculates a degree of arousal based on time series data of the maximum Lyapunov exponent, and arousal degree obtained by the arousal degree calculation means , And a timing determination unit that determines a timing at which the corrected fatigue level of the second fatigue level calculation unit is adopted, and the cumulative fatigue level output unit includes the second fatigue level calculation unit by the timing determination unit.
  • a cumulative sum of the reference fatigue levels obtained by the first fatigue level calculation unit is obtained and output, and the corrected fatigue level of the second fatigue level calculation unit is calculated.
  • the cumulative fatigue correction degree obtained by the second fatigue degree calculating means is calculated and output.
  • Door is preferable.
  • the first fatigue level calculating means 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 original waveform of the biological signal data,
  • a power value calculating means for calculating a time series data of the power value using the difference as the power value;
  • a power value inclination calculating means for obtaining a slope of the power value with respect to a time axis in a predetermined time range by performing slide calculation a predetermined number of times;
  • a power value slope integrating means for performing absolute value processing on a time series signal of the power value slope obtained by the slide calculation by the power value slope calculating means, and calculating an integral value for each predetermined time range as the reference fatigue level; It is preferable to comprise.
  • LF / HF time series data calculating means for obtaining time series data of the power spectrum of LF / HF obtained by frequency analysis of time series data of heart rate or pulse rate collected by the biological signal measuring instrument
  • the power value calculation means multiplies the value of the time series data of the power spectrum of the LF / HF and the value of the time series data of the power value by the values corresponding to each other and LF / HF It is preferably set to calculate the time series data of the LF / HF included power value as the calculated power value.
  • the second fatigue level calculating means calculates the value of the time series data of the maximum Lyapunov exponent used by the arousal level calculating means and the value of the time series data of the power value used by the first fatigue level calculating means.
  • a correction power value calculating means for calculating the time series data of the correction power value by multiplying the values corresponding to each other at the corresponding time, and the inclination of the correction power value with respect to the time axis in a predetermined time range
  • Correction power value inclination calculation means obtained by sliding calculation a predetermined number of times, and time-series signals of the correction power value inclination obtained by slide calculation by the correction power value calculation means are subjected to absolute value processing for each predetermined time range.
  • the correction power value gradient integration means for calculating the integrated value as the corrected fatigue degree can be provided.
  • the second fatigue level calculating means includes the time series data value of the maximum Lyapunov exponent used in the arousal level calculating means and the time series data of the LF / HF included power value used in the first fatigue level calculating means.
  • a correction power value calculation means for calculating time series data of the correction power value by multiplying the values of the correction values by values at times corresponding to each other, and a correction power value in a predetermined time range of the correction power value
  • a correction power value inclination calculating means for obtaining the inclination with respect to the time axis by slide calculation a predetermined number of times, and a time series signal of the inclination of the correction power value obtained by the slide calculation by the correction power value calculation means are subjected to absolute value processing,
  • a correction power value gradient integration means for calculating an integrated value for each predetermined time range as the corrected fatigue level may be provided.
  • the arousal degree calculating means calculates a maximum Lyapunov exponent from the vital sign data and calculates time series data of the maximum Lyapunov exponent, and a slope of the maximum Lyapunov exponent with respect to a time axis in a predetermined time range.
  • the maximum Lyapunov exponent slope calculating means obtained by sliding calculation a predetermined number of times, and the time series signal of the slope of the maximum Lyapunov exponent slope obtained by the slide calculation by the maximum Lyapunov exponent slope calculating means is subjected to absolute value processing, and a predetermined time range.
  • a maximum Lyapunov exponent slope integration means for calculating the integral value for each arousal level, and a cumulative arousal level for obtaining and outputting a cumulative sum of the arousal levels obtained by the maximum Lyapunov exponent slope integration means Output means.
  • the timing determination unit is configured to determine the sudden conversion point of the slope of the cumulative arousal level output from the cumulative arousal level output unit as a timing to employ the corrected fatigue level of the second fatigue level calculation unit. It is preferable.
  • the timing determination means obtains the slope of the cumulative arousal degree in a predetermined time range from the start of measurement as a reference slope, and the slope of the cumulative arousal degree in the predetermined time range in the time zone after obtaining the reference slope is a reference It is preferable that the abrupt conversion point is determined when a change of at least 10% occurs with respect to the inclination.
  • the present invention based on time-series data of power values, or based on values (LF / HF included power values) in consideration of time-series data of LF / HF power spectrum in time-series data of power values.
  • a second fatigue level calculating means for correcting the data value and obtaining a corrected fatigue level based on the obtained time series data of the corrected power value; a determining means for determining the presence or absence of a compensation effect of fatigue due to sympathetic nerve activity;
  • the cumulative fatigue level output means calculates and outputs the cumulative sum of the standard fatigue levels obtained by the first fatigue level calculation means in a time zone when it is determined that the fatigue due to sympathetic nerve activity is not compensated.
  • the fatigue level based on only the power value or only the LF / HF power value (reference fatigue level) or the fatigue level considering the maximum Lyapunov exponent (correction) Therefore, the change in the cumulative fatigue level is closer to the sensory evaluation value, that is, closer to the actual fatigue feeling.
  • the determination means obtains the maximum Lyapunov exponent from the biological signal data, calculates the degree of arousal based on the time series data of the maximum Lyapunov exponent, and the second degree from the arousal degree obtained by the arousal degree calculator. It is preferable that the apparatus includes a timing determination unit that determines a timing at which the corrected fatigue level of the fatigue level calculation unit is adopted. The higher the maximum Lyapunov exponent, the higher the adaptability, tension and concentration to things, and the lower the maximum Lyapunov exponent, the more mentally relaxed.
  • the “arousal degree” obtained by the determination means employed in the present invention is set so that the slope of the maximum Lyapunov exponent is obtained from the time series data of the maximum Lyapunov exponent, and the slope is obtained by absolute value processing and integration. is doing.
  • the “degree of arousal” captures a global trend of how the maximum Lyapunov exponent, which indicates the degree of stimulation received mentally, fluctuates.
  • “arousal level” is an index that captures fatigue that cannot be captured as muscle contraction, that is, mental fatigue of the brain that cannot be captured as muscle fatigue, because it is compensated for by sympathetic nerve tension.
  • mental fatigue includes the mental fatigue of the autonomic nervous system and the mental fatigue of the brain, but the mental fatigue of the autonomic nervous system is similar to the mental fatigue of the brain. It becomes the object of compensation by the activity. For example, when a fatigue analysis is performed after lying down and taking a sufficient rest or sleep, there is almost no physical fatigue of antigravity muscles at the start of the analysis, and the autonomic nervous system and brain mental fatigue None of this has occurred so much. For this reason, when determining the fatigue level from this state, first determine the standard fatigue level by peripheral physical fatigue, and then determine the fatigue level considering the mental fatigue of the brain based on the arousal level, It approaches human sensory evaluation values.
  • the peak value of each cycle of the original waveform of the biological signal data which is an index of peripheral physical fatigue
  • the LF / HF obtained from the heart rate or pulse rate which is an index of mental fatigue of the autonomic nervous system, is calculated as 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. It is preferable to adopt a configuration in which a value considering the time series data of the power spectrum (LF / HF included power value) is employed, thereby obtaining the reference fatigue level.
  • the obtained fatigue level can be used for various applications. For example, it is possible to more accurately determine the seat and bedding that are suitable for a person based on the degree of fatigue when the person is seated on the seat or the degree of fatigue when the person is lying on the bedding. This is because whether the seat is good or bad is determined by the feeling of sitting fatigue, and whether the bedding is good or bad is also determined by the feeling of fatigue when lying down.
  • FIG. 1 is a block diagram showing a configuration of a fatigue analysis apparatus according to an embodiment of the present invention.
  • FIG. 2 is a diagram for explaining the configuration of the first fatigue level calculation means, the second fatigue level calculation means, and the cumulative fatigue level output means introduced into the fatigue analysis apparatus.
  • FIG. 3 is a diagram for explaining a configuration of a determination unit introduced into the fatigue analysis apparatus.
  • FIG. 4 is data showing an example of heart rate fluctuations collected from an electrocardiograph for calculating LF / HF time-series data.
  • FIG. 5 is a diagram showing the power spectra of the HF component and the LF component obtained by continuous wavelet analysis of the time-series data of the heart rate shown in FIG.
  • FIG. 6 shows LF / HF time-series data obtained from FIG.
  • FIG. 1 is a block diagram showing a configuration of a fatigue analysis apparatus according to an embodiment of the present invention.
  • FIG. 2 is a diagram for explaining the configuration of the first fatigue level calculation means, the second fatigue level calculation means,
  • FIG. 7 shows time-series data of average values calculated every 5 seconds from the LF / HF time-series data of FIG.
  • FIG. 8 is a diagram for explaining the LF / HF inclusion means, in which FIG. 8A is a diagram obtained by multiplying the time series data of FIG. 7 by 1/5, and FIG. FIG. 8C shows time series data of average values of power values every 5 seconds obtained by processing time series data of pulse waves obtained by the volume pulse wave meter.
  • FIG. This is time series data of LF / HF power values obtained by multiplying the series data and the time series data of FIG. 8B by corresponding time values.
  • FIG. 8A is a diagram obtained by multiplying the time series data of FIG. 7 by 1/5
  • FIG. 8C shows time series data of average values of power values every 5 seconds obtained by processing time series data of pulse waves obtained by the volume pulse wave meter.
  • FIG. This is time series data of LF / HF power values obtained by multiplying the series data and the time series data of FIG. 8
  • FIG. 9 is a diagram illustrating an example of time-series data of the average value of the maximum Lyapunov exponent used for calculation of the corrected power value every 5 seconds.
  • FIG. 10 is a diagram for explaining the process of calculating the maximum Lyapunov exponent.
  • FIG. 10A is an example of time-series data of the average value of the maximum Lyapunov exponent every 5 seconds (measurement time 1800 seconds).
  • FIG. 10B shows the average value of the power values every 5 seconds obtained by processing the time-series data of the pulse wave obtained by the fingertip plethysmograph by the power value calculating means.
  • FIG. 10C is an example of time-series data, and FIG. 10C is a correction obtained by multiplying the time-series data of FIG.
  • FIG. 10A and the time-series data of FIG. 10B by corresponding time values It is an example of the time series data of a power value.
  • FIG. 11 is a diagram showing test results when test subject A sits on a urethane sheet under static conditions in Test Example 1, (a) shows a time-series change in the degree of subjective fatigue, and (b) shows FIG. 4 is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 12 is a diagram showing test results when test subject A is seated on a wooden chair under static conditions in Test Example 1, (a) shows a time-series change in the degree of subjective fatigue, and (b) shows FIG. 4 is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 11 is a diagram showing test results when test subject A sits on a urethane sheet under static conditions in Test Example 1, (a) shows a time-series change in the degree of subjective fatigue, and (b) shows FIG. 4 is a diagram showing fatigue curves (
  • FIG. 13 is a diagram showing test results when test subject A is seated on a urethane sheet in a vibration condition in Test Example 1, (a) shows a time-series change in the degree of subjective fatigue, and (b) FIG. 3 is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 14 is a diagram showing test results when subject A sits on a wooden chair under vibration conditions in Test Example 1, (a) shows time-series changes in the degree of subjective fatigue, and (b) FIG. 3 is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 14 is a diagram showing test results when subject A sits on a wooden chair under vibration conditions in Test Example 1, (a) shows time-series changes in the degree of subjective fatigue, and (b) FIG. 3 is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 15 is a diagram showing test results when test subject B sits on a wooden chair under static conditions in Test Example 1, (a) shows the time-series change in the degree of subjective fatigue, and (b) shows FIG. 4 is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 16 is a diagram showing test results when test subject A is seated on a urethane sheet in a static condition in Test Example 2, (a) shows a time-series change in the degree of subjective fatigue, and (b) shows A time series change of the arousal level / time and the cumulative arousal level is shown, and (c) is a diagram showing the fatigue curves (A) to (D) together.
  • FIG. 16 is a diagram showing test results when test subject A is seated on a urethane sheet in a static condition in Test Example 2, (a) shows a time-series change in the degree of subjective fatigue, and (b) shows A time series change of the arousal level / time and the cumulative a
  • FIG. 17 is a diagram showing test results when subject A sits on a wooden chair under static conditions in Test Example 2, (a) shows time-series changes in the degree of subjective fatigue, and (b) A time series change of the arousal level / time and the cumulative arousal level is shown, and (c) is a diagram showing the fatigue curves (A) to (D) together.
  • FIG. 18 is a diagram showing test results when test subject A is seated on a urethane sheet in a vibration condition in Test Example 2, (a) shows a time series change in the degree of subjective fatigue, and (b) shows arousal. A time series change of degree / time and cumulative arousal degree is shown, and (c) is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 19 is a diagram showing test results when test subject A is seated on a wooden chair under vibration conditions in Test Example 2, (a) shows time-series changes in subjective fatigue level, and (b) shows arousal. A time series change of degree / time and cumulative arousal degree is shown, and (c) is a diagram showing fatigue curves (A) to (D) together.
  • FIG. 20 is a diagram showing test results when subject B sits on a wooden chair under static conditions in Test Example 2, (a) shows the time-series change in the degree of subjective fatigue, and (b) shows A time series change of the arousal level / time and the cumulative arousal level is shown, and (c) is a diagram showing the fatigue curves (A) to (D) together.
  • FIG. 20 is a diagram showing test results when subject B sits on a wooden chair under static conditions in Test Example 2, (a) shows the time-series change in the degree of subjective fatigue, and (b) shows A time series change of the arousal level
  • FIG. 21 is a diagram showing test results when test subject A is seated on a urethane sheet in a static condition in Test Example 3, (a) shows a time-series change in the degree of subjective fatigue, and (b) shows It is the figure which showed each fatigue curve (A)-(D) together, (c) shows the time series change of the arousal degree / time and the cumulative arousal degree, and (d) is the time of the arousal degree / time. It is the figure which set the threshold value to the series change.
  • FIG. 22 is a diagram showing test results when subject A sits on a wooden chair under static conditions in Test Example 3, (a) shows the time-series change in the degree of subjective fatigue, and (b) shows It is the figure which showed each fatigue curve (A)-(D) together, (c) shows the time series change of the arousal degree / time and the cumulative arousal degree, and (d) is the time of the arousal degree / time. It is the figure which set the threshold value to the series change.
  • FIG. 23 is a diagram showing test results when test subject A is seated on a urethane sheet in a vibration condition in Test Example 3; (a) shows a time-series change in the degree of subjective fatigue; and (b) It is the figure which showed fatigue curves (A)-(D) together, (c) shows the time series change of the arousal degree / time and the cumulative arousal degree, and (d) is the time series of the arousal degree / time. It is the figure which set the threshold value to the change.
  • FIG. 24 is a diagram showing test results when test subject A is seated on a wooden chair under vibration conditions in Test Example 3, (a) shows the time-series change in the degree of subjective fatigue, and (b) It is the figure which showed fatigue curves (A)-(D) together, (c) shows the time series change of the arousal degree / time and the cumulative arousal degree, and (d) is the time series of the arousal degree / time. It is the figure which set the threshold value to the change.
  • 25 is a diagram showing test results when subject B sits on a wooden chair under static conditions in Test Example 3, (a) shows a time-series change in the degree of subjective fatigue, and (b) It is the figure which showed each fatigue curve (A)-(D) together, (c) shows the time series change of the arousal degree / time and the cumulative arousal degree, and (d) is the time of the arousal degree / time. It is the figure which set the threshold value to the series change.
  • FIG. 1 is a diagram illustrating a configuration of a fatigue analysis apparatus 1 according to the present embodiment.
  • the fatigue analysis apparatus 1 is composed of a computer or the like, and includes data receiving means 10 for receiving data of a fingertip plethysmograph 20 as a biological signal measuring instrument, and also receives data.
  • First fatigue level calculation means 11, second fatigue level calculation means 12, determination means 13, and cumulative fatigue level output, which are computer programs for processing time series data of fingertip volume pulse waves as biological signal data received by means 10 Means 14 is set.
  • the first fatigue level calculating means 11 includes a power value calculating means 111, a power value slope calculating means 112, and a power value slope integrating means 113, as shown in FIG.
  • the power value calculation unit 111 includes the following processing steps. First, for the time series data of the fingertip plethysmogram collected by the fingertip plethysmograph 20 and received by the data receiving means 11, the maximum value and the minimum value are obtained by the smoothing differential method by Savitzky and Golay, respectively. . Next, the local maximum value and the local minimum value are divided every predetermined time range set in advance, in this embodiment, every 5 seconds, and the average value (maximum peak value) of the local maximum value and the local minimum value in the time range.
  • the average value (peak value on the lower limit side) of the values is obtained, and the difference between them is obtained as the power value.
  • the difference between the average value of the maximum value and the average value of the minimum value in the predetermined time range is squared to obtain the power value.
  • the power value inclination calculation means 112 obtains the inclination with respect to the time axis by the least square method for a predetermined time width Tw (180 seconds in the present embodiment) from the time series data of the power values obtained by the power value calculation means 111. Next, the next time width Tw (180 seconds) is similarly calculated with the overlap time Tl (162 seconds), and the result is plotted. This calculation (slide calculation) is repeated sequentially. Thereby, in this example, the inclination of the power value is plotted every 18 seconds, and the time series data is obtained.
  • the power value slope integration means 113 first performs absolute value processing on the time series signal of the power value slope obtained by the slide calculation by the power value slope calculation means 112. That is, the power values obtained every 18 seconds in the above example are all made positive. Next, an integral value of a time (in this example, 18 seconds) from a time point at which a slope of a certain power value is plotted to a time point at which the slope of the next power value is plotted is obtained every predetermined time range. Then, the obtained integral value (obtained every 18 seconds in this example) is set as the reference fatigue level.
  • the power value gradient calculating unit 112 calculates the power value gradient using the power value obtained from the power value calculating unit 111 to obtain the reference fatigue level. As described above, it is preferable to obtain the above in consideration of mental fatigue of the autonomic nervous system.
  • LF / HF time-series data calculation for obtaining time-series data of LF / HF power spectrum obtained by frequency analysis of time-series data of heart rate or pulse rate measured by an electrocardiograph or fingertip plethysmograph Means 111a is provided, and the power value calculation means 111 adds the value of the time series data of the LF / HF power spectrum obtained by the LF / HF time series data calculation means 111a to the value of the time series data of the power value. It is preferable to obtain the LF / HF power value.
  • the method of adding the value of the time series data of the power spectrum of LF / HF to the power value is as follows.
  • a frequency analysis method it is preferable to use continuous wavelet transform.
  • the LF component is 0.04 to 0.15 Hz and the HF component is 0.15 to 0.4 Hz.
  • the continuous wavelet transform has a high resolution, Waves can be taken well and is suitable for frequency analysis of heart rate or pulse rate variation.
  • time-series data of heart rate or pulse rate per minute obtained from RR interval data is obtained.
  • continuous wavelet analysis is performed to calculate the total value of the power spectrum of the HF component and the LF component per unit time (RR interval) to obtain LF / HF time series data (FIGS. 4 to 5). 6).
  • an average value is obtained every 5 seconds for the LF / HF time-series data (see FIG. 7). Since LF / HF is obtained every RR interval, the average value every 5 seconds includes the sum of the LF / HF values for each RR interval included in 5 seconds. Calculated by dividing by the number of RR intervals. Then, the time-series data of the average value every 5 seconds is multiplied by the time-series data of the power value calculated every 5 seconds similarly (see FIG. 8). Thereby, time series data of the LF / HF inclusion power value is obtained.
  • the average value of LF / HF may be used as it is, but it may be multiplied by the power value after being multiplied by several times (for example, 1/5 times) for easy processing. Moreover, although the average value of LF / HF and said power value are calculated
  • the second fatigue level calculating means 12 includes a corrected power value calculating means 121, a corrected power value slope calculating means 122, and a corrected power value slope integrating means 123.
  • the corrected power value calculating means 121 obtains the time series data of the maximum Lyapunov exponent from the time series data of the fingertip volume pulse wave, the value of the time series data of the maximum Lyapunov exponent, and the power of the first fatigue level calculating means 11
  • the power value time-series data values used by the value calculating means 111 are multiplied by the values corresponding to each other in time to obtain a corrected power value, and the corrected power value time-series data is calculated.
  • the value of the maximum Lyapunov exponent is obtained every second from the time series data of the fingertip volume pulse wave. Further, in this embodiment, the average value every 5 seconds is calculated from the time series data of this maximum Lyapunov exponent. Is calculated (FIGS. 9 and 10A). Then, the average value every 5 seconds is multiplied by the power value obtained every 5 seconds (FIG. 10B), and the time series data of the corrected power value including the maximum Lyapunov exponent (FIG. 10 ( c)).
  • the second fatigue degree calculation means 12 Since the maximum Lyapunov exponent is obtained by the arousal degree calculation means 131 described later, the second fatigue degree calculation means 12 does not obtain the maximum Lyapunov exponent again, but calculates the corrected power value of the maximum Lyapunov exponent obtained by the arousal degree calculation means 131. It is preferable to use it. However, when the first fatigue level calculating unit 11 calculates the reference fatigue level from the LF / HF included power value, when the second fatigue level calculating unit 12 starts operating, the corrected power value calculating unit 121 Instead of the time series data of / HF, the value of the time series data of the maximum Lyapunov exponent is multiplied by the value of the time series data of the power value.
  • the corrected power value calculation means 121 multiplies the power value time series data multiplied by the LF / HF time series data and the time series data value of the maximum Lyapunov exponent as it is. It can also be used as a correction power value.
  • the corrected power value slope calculating means 122 has the same configuration as that of the power value slope calculating means 112 described above except that the corrected power value is used as data serving as a basis for calculation. Find the tilt with respect to the axis.
  • the correction power value slope integration means 123 is the same as that of the power value slope integration means 113 except that the slope of the correction power value is used as the basis data for the calculation. These time series signals are subjected to absolute value processing, and an integral value for each predetermined time range is calculated. This integrated value is used as the corrected fatigue level.
  • the determination unit 13 includes an arousal degree calculation unit 131, a cumulative arousal level output unit 132, and a timing determination unit 133.
  • the arousal degree calculation means 131 further includes a maximum Lyapunov exponent calculation means 131a, a maximum Lyapunov exponent slope calculation means 131b, and a maximum Lyapunov exponent slope integration means 131c.
  • the maximum Lyapunov exponent calculation means 131a calculates the maximum Lyapunov exponent from the time series data of the fingertip plethysmogram received by the data receiving means 11, and calculates the time series data of the maximum Lyapunov exponent.
  • the time series data of the fingertip volume pulse wave is first reconstructed into a state space by a time delay method.
  • the time-series delay time of the fingertip plethysmogram is 50 ms, and when the FNN (False Near Neighbors) method is used as the embedding dimension, the FNN becomes almost zero in the dimension 3 and completely zero in the dimension 4 Therefore, the optimum embedding dimension is set to 4 dimensions.
  • the attractor is reconfigured with a time width of 30 seconds, and the value of the maximum Lyapunov exponent is plotted every second by sliding the time width by 1 second, Find time series data of the maximum Lyapunov exponent.
  • the maximum Lyapunov exponent slope calculating means 131b obtains the slope of the maximum Lyapunov exponent with respect to the time axis in a predetermined time range by performing slide calculation a predetermined number of times. Specifically, the maximum Lyapunov exponent determined as described above is smoothed by a smoothing differential method using Savitzky and Golay to obtain a maximum value and a minimum value, and then plots the time series data of the maximum value and the minimum value. The inclination with respect to the time axis in a predetermined time range is obtained. The method of obtaining the inclination is the same as that of the power value inclination calculating means 112 described above.
  • the inclination with respect to the time axis is obtained by the least square method for the predetermined time width Tw (180 seconds in the present embodiment).
  • the next time width Tw (180 seconds) is similarly calculated with the overlap time Tl (162 seconds), and the result is plotted. This calculation (slide calculation) is repeated sequentially.
  • the slope of the maximum Lyapunov exponent is plotted every 18 seconds, and the time series data is obtained.
  • the maximum Lyapunov exponent slope integration means 131c first performs absolute value processing on the time series signal of the slope of the maximum Lyapunov exponent slope obtained by the slide calculation by the maximum Lyapunov exponent slope calculation means 131b. That is, the maximum Lyapunov exponent slope obtained every 18 seconds in the above example is set to a positive value. Next, an integral value of a time (in this example, 18 seconds) from a time point when a slope of a certain maximum Lyapunov exponent is plotted to a time point when a slope of the next maximum Lyapunov exponent is plotted is obtained every predetermined time range. Then, the obtained integral value (obtained every 18 seconds in this example) is used as an arousal level (Ergonomics Vol.
  • the cumulative arousal output means 132 calculates and outputs a cumulative sum of each integral value (arousal degree) obtained by the maximum Lyapunov exponent slope integral means 131b. Thereby, the fluctuation tendency of arousal level can be grasped.
  • the timing determination unit 133 uses the sudden conversion point of the slope of the cumulative evoke level output by the cumulative evoke level output unit 132 and uses the corrected fatigue level of the second fatigue level calculation unit 12 in the cumulative fatigue level output unit 14. It is a means to determine.
  • the “sudden conversion point” is obtained by using the slope of the cumulative arousal level in the predetermined time range from the start of measurement as the reference slope, and the slope of the cumulative arousal level in the predetermined time range in the time zone after obtaining this reference slope is the reference slope
  • the reference inclination is preferably a time zone before mental fatigue is greatly affected, and when analyzing fatigue when seated, such as a car seat or office chair, 3 to 15 minutes after the start of measurement. It is preferable to adopt an average slope of 3 minutes to 12 minutes in the range.
  • the slope of the cumulative arousal level compared with the reference slope is preferably an average slope for 2 minutes to 5 minutes.
  • the cumulative fatigue level output means 14 is a power value gradient of the first fatigue level calculation means 11 in a time zone before the timing determination means 133 determines that the corrected fatigue level of the second fatigue level calculation means 12 is adopted.
  • the cumulative sum of the standard fatigue levels obtained by the integrating means 113 is obtained and output.
  • the cumulative sum of the corrected fatigue levels obtained by the corrected power value slope integrating unit 123 of the second fatigue level calculating unit 12 is obtained. Output.
  • the accumulation of fatigue that occurs in the relaxed state is obtained as the cumulative sum of the standard fatigue levels, and the accumulation of fatigue in the state where the mental fatigue compensated by the exchange nerve activity is taken into account is the cumulative sum of the corrected fatigue levels.
  • the change in the cumulative fatigue level becomes closer to the sensory evaluation value as compared with the case based on the fatigue level calculated only by the conventional power value.
  • the determination of the sudden conversion point of the inclination by the timing determination unit 133 compares the average inclinations of 2 minutes to 5 minutes, so that the occurrence of the sudden conversion point is 2 minutes from the actual generation time of the sudden conversion point. After 5 minutes.
  • the cumulative fatigue level output means 14 outputs the cumulative sum of the corrected fatigue levels of the second fatigue level calculation means 12.
  • the timing determination unit 133 As for the calculation of the corrected fatigue level, it is preferable to automatically determine the timing at which the corrected fatigue level is adopted using the timing determination unit 133 as in the fatigue analysis apparatus 1 of the present embodiment, but from 3 minutes to 15 minutes after the start of measurement.
  • a predetermined threshold value is set from the “degree of arousal / time” (every 18 seconds in this embodiment) obtained by the maximum Lyapunov exponent slope integration means 131c in the range, and when the predetermined threshold value is exceeded, the second fatigue level is calculated. It can also be set so that the cumulative sum of the corrected fatigue levels of the means 12 is output.
  • the excitation suppression coexistence period (ergonomics Vol.
  • a computer program including the first fatigue level calculation unit 11, the second fatigue level calculation unit 12, the determination unit 13, the cumulative fatigue level output unit 14, and the like is 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 the fatigue analysis apparatus of the present invention by preinstalling or downloading the above program to a general-purpose terminal apparatus.
  • Test example 1 (Verification test of whether the use of the maximum Lyapunov index is effective) Used by two Japanese adult subjects (subject A: male, 32 years old, height 178 cm, weight 69.2 kg, subject B: male, 34 years old, height 167 cm, weight 68 kg) in the driver's seat of a wooden chair or passenger car Fatigue in a state of sitting on a urethane sheet for 30 minutes was analyzed. In addition, the experiment was also performed when the robot was seated statically without being shaken, or when it was placed on a shaker and shaken. The excitation condition was a reproduction of the vibration of the floor during actual vehicle running with an average acceleration of 0.1G.
  • a fingertip plethysmograph 20 was attached to each subject and a fingertip vessel pulse wave was collected, and a power value, LF / HF, and maximum Lyapunov index were determined, and a standard fatigue level, a corrected fatigue level, and an arousal level were determined.
  • the standard fatigue level was calculated using an LF / HF inclusion power value in consideration of LF / HF time-series data of pulse rate.
  • the slope of the power value, the slope of the corrected power value, and the slope of the maximum Lyapunov exponent were obtained with an overlap time of 162 seconds for 180 seconds.
  • the results are shown in FIGS.
  • FIGS. 11 to 15 (a) also shows the sensory evaluation value of each subject.
  • the sensory evaluation value uses the Borg scale.
  • FIG. 11 shows the test results when the subject A is seated on the urethane sheet under static conditions
  • FIG. 12 shows the test results when the subject A is seated on the wooden chair under static conditions
  • FIG. FIG. 14 shows the test results when subject A sits on the urethane sheet under vibration conditions
  • FIG. 14 shows the test results when subject A sits on the wooden chair under vibration conditions
  • FIG. 15 shows static results when subject B sits on the wooden chair. The test results when seated under conditions are shown.
  • the “awareness fatigue” shown in each of FIGS. 11 to 15 is the output result of Test Example 1.
  • This “subject fatigue level” is calculated by adding LF / L to the power value output by the power value slope integrating means 113 in the time zone before the time indicated by the alternate long and short dash line in each of FIGS.
  • the cumulative fatigue level which is the cumulative sum of the standard fatigue levels calculated from the LF / HF calculated power value including HF is adopted, and in the time zone after the time indicated by the one-dot chain line, the second fatigue level calculating means 12
  • the correction power value calculation means 121 obtains a correction power value by multiplying the time series data value of the maximum Lyapunov exponent by the value of the time series data of the power value instead of the time series data of LF / HF, and calculates the correction power value.
  • the corrected fatigue level is obtained from the value, and the cumulative fatigue level is adopted.
  • the time indicated by the alternate long and short dash line when the corrected fatigue level is adopted is based on the point when the tendency of sensory evaluation changes relatively, but the subjective fatigue level obtained in Test Example 1 is the sensory evaluation value. It can be seen that the curve is close to.
  • FIGS. 11 to 15 (b) shows the fatigue curve (A) of the subjective fatigue degree shown in the figure of (a), and the cumulative sum of the standard fatigue degree calculated from only the power value in all time zones.
  • Fatigue curve that combines the standard fatigue level obtained from the LF / HF power value in the time zone and the corrected fatigue level that is multiplied by the maximum Lyapunov exponent slope while adding LF / HF in the subsequent time zone ( D) is also shown.
  • the fatigue curves (B) and (C) show different changes from the fatigue curve (A) of the subjective fatigue degree after the one-dot chain line time zone, and tend to deviate from the sensory evaluation value. It can be seen that the fatigue level closer to the sensory evaluation value can be obtained by adopting the corrected fatigue level including the maximum Lyapunov exponent at the time point.
  • the fatigue curve (D) when compared with the fatigue curves (B) and (C), the sensory evaluation value is approached, but the time series data of LF / HF is included as the standard fatigue level
  • the fatigue curve (A) shown as the subjective fatigue level of Test Example 1 the time series data of the LF / HF is removed at the time when the time series change of the maximum Lyapunov slope is included. It turned out that it becomes a tendency close to evaluation.
  • Test example 2 (A test that automatically determines the maximum Lyapunov index inclusion timing)
  • Test Example 2 As in Test Example 1, as the standard fatigue level, an LF / HF included power value obtained by adding LF / HF time-series data of the pulse rate to the power value was used. Further, in Test Example 2, the corrected fatigue that includes the maximum Lyapunov exponent using the determination means 13 including the arousal degree calculation means 131, the cumulative arousal degree output means 132, and the timing determination means 133 of the above embodiment. The timing to adopt the degree was automatically judged.
  • the timing determination unit 133 determines the sudden conversion point of the cumulative arousal level obtained by the cumulative arousal level output unit 132, and after that time, the corrected power value calculation unit 121 of the second fatigue level calculation unit 12 performs LF / Instead of the time series data of HF, the value of the time series data of the maximum Lyapunov exponent is multiplied by the value of the time series data of the power value to obtain the corrected power value, the corrected fatigue value is obtained from the corrected power value, and further accumulated The fatigue level was output. As in Test Example 1, the combination of the cumulative sum of the standard fatigue levels and the cumulative sum of the corrected fatigue levels was defined as the subjective fatigue level.
  • FIGS. 16 to 20 The “subject's subjective fatigue” shown in the diagrams (a) of FIGS. 16 to 20 is the output result of Test Example 2.
  • FIGS. 16 to 20 (b) is a diagram showing time-series changes in the “arousal level / time” and the “cumulative arousal level”.
  • “Rounding degree / time” is an integral value every 18 seconds output by the maximum Lyapunov exponent slope integrating means 131c
  • “cumulative arousing degree” is a cumulative sum of the integral values.
  • the time indicated by the alternate long and short dash line is the time point when the timing determination means 133 determines that the corrected fatigue level is to be adopted.
  • the timing determination unit 133 obtains the average inclination of the cumulative arousal level as the reference inclination for 4.45 minutes after the start of measurement for 4.45 minutes (the horizontal scale on the horizontal axis of each (b)), and thereafter The average inclination every 2.225 minutes (one scale on the horizontal axis in the figure of each (b)) was compared with the reference inclination, and the time when a change of 10% or more occurred was determined as the sudden conversion point.
  • the time point indicated by the symbol “X” is the sudden conversion point of the slope
  • the time point indicated by the symbol “Y” (the diagram of FIG. 16 to FIG. 20 (a)).
  • the adoption timing of the corrected fatigue that is, the maximum Lyapunov exponent.
  • the next 2.225 minutes with respect to this reference inclination that is, 7. Compare the average slope from 75 minutes to 9.975 minutes. At this time, it can be judged when 9.975 minutes have elapsed that the average slope from 7.75 minutes to 9.975 minutes has changed by 10% or more with respect to the reference slope from 3.3 minutes to 7.75 minutes. . Therefore, the sudden conversion point X is set to a time point of 7.75 minutes, and the inclusion timing is when 9.975 minutes have elapsed.
  • FIG. 16 shows the test results when subject A sits on the urethane sheet under static conditions
  • FIG. 17 shows the test results when subject A sits on the wooden chair under static conditions
  • FIG. FIG. 19 shows the test results when subject A sits on the urethane sheet under vibration conditions
  • FIG. 19 shows the test results when subject A sits on the wooden chair under vibration conditions
  • FIG. 20 shows static results when subject B sits on the wooden chair. The test results when seated under conditions are shown.
  • each graph of (c) in FIGS. 16 to 20 shows the fatigue curve (A) of the subjective fatigue level shown in the graph of (a), and the reference fatigue level calculated from only the power value in all time zones.
  • the curve (D) is also shown, the time series data of the LF / HF at the time when the time series change of the maximum Lyapunov slope is included as in the fatigue curve (A) shown as the subjective fatigue level of Test Example 2 It ’s better to remove It was found to be in close trend in performance evaluation.
  • Test example 3 (A test in which the maximum Lyapunov index was counted from the “degree of arousal / time”)
  • Test Example 3 As in Test Examples 1 and 2, as the standard fatigue level, an LF / HF included power value obtained by adding time-series data of LF / HF of the pulse rate to the power value was used.
  • a predetermined threshold value is set for the “degree of arousal / time”, which is an integral value every 18 seconds output by the maximum Lyapunov exponent slope integration means 131c, and the LF / Instead of the time series data of HF, the corrected fatigue degree including the maximum Lyapunov exponent was adopted.
  • the combination of the standard fatigue level and the corrected fatigue level is shown as the subjective fatigue level in FIGS. 21 to 25 (a).
  • each broken line shown in parallel with the horizontal axis is the threshold value.
  • the threshold value refers to the change in “degree of arousal / time” from 4. 3 minutes after the start of measurement to 4.45 minutes (2 scales on the horizontal axis in each figure (b)).
  • the measurer set a value slightly higher than the fluctuation range of the “arousal level / time” of the belt. 21 shows the test results when subject A sits on the urethane sheet under static conditions, FIG. 22 shows the test results when subject A sits on the wooden chair under static conditions, and FIG. FIG. 24 shows the test results when subject A sits on a urethane sheet under vibration conditions, FIG. 24 shows the test results when subject A sits on a wooden chair under vibration conditions, and FIG.
  • FIGS. 21 to 25 shows static results when subject B sits on a wooden chair. The test results when seated under conditions are shown.
  • FIGS. 21 to 25 (b) shows the fatigue curve (A) of the subjective fatigue degree shown in the figure (a), and the reference fatigue degree calculated from only the power value in all time zones.
  • FIG. 21 to FIG. 25 (c) is a diagram showing time-series changes in “degree of arousal / time” and “cumulative degree of arousal”.
  • a predetermined time zone after the start of measurement preferably a time zone of 3 to 12 minutes (4.45 minutes in this test example) in the range of 3 to 15 minutes after the start of measurement. It can be seen that a fatigue curve with a subjective fatigue level close to sensory evaluation can be obtained even if the threshold value is set with reference to the change in “arousal level / time”.
  • the configuration in which the maximum Lyapunov exponent is automatically calculated from the change in the slope of the cumulative arousal level is more computationally intensive than the manual setting of the threshold value based on the change in “Rounding level / time” in Test Example 3.
  • Test Example 3 In view of the fact that the value is large, depending on the computing ability of the fatigue analysis device, it is also possible to obtain the degree of subjective fatigue by the method of Test Example 3. However, as long as you set a ratio (10%, 20%, etc.) to be judged as a sudden conversion point of the slope of the cumulative arousal level, it automatically determines the timing of inclusion of the maximum Lyapunov index and outputs a subjective fatigue level close to sensory evaluation The technique of Test Example 2 is more preferable.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Veterinary Medicine (AREA)
  • Pathology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Physics & Mathematics (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Child & Adolescent Psychology (AREA)
  • Hospice & Palliative Care (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Developmental Disabilities (AREA)
  • Educational Technology (AREA)
  • Signal Processing (AREA)
  • Neurology (AREA)
  • Neurosurgery (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

Selon l'invention, un degré de fatigue est obtenu par une valeur d'évaluation de détection prenant en compte une compensation par une activité du nerf sympathique. L'invention porte sur un dispositif d'analyse de fatigue comprenant : un premier moyen de calcul de degré de fatigue (11) qui permet d'obtenir un degré de fatigue de référence ; un second moyen de calcul de degré de fatigue (12) qui permet d'obtenir un degré de fatigue corrigé par utilisation de l'exposant de Lyapunov maximum ; et un moyen de jugement (13) qui juge si ou non une compensation de fatigue par l'activité du nerf sympathique est présente. Dans la bande temporelle où il est jugé qu'aucune compensation de fatigue n'est effectuée par l'activité du nerf sympathique, un moyen d'émission de degré de fatigue accumulé (14) émet une somme accumulée des degrés de fatigue de référence obtenus par le premier moyen de calcul de degré de fatigue (11). Dans la bande temporelle où il est jugé qu'une compensation de fatigue n'est pas effectuée par l'activité du nerf sympathique, un moyen d'émission de degré de fatigue accumulé (14) émet une somme accumulée des degrés de fatigue corrigés obtenus par le second moyen de calcul de degré de fatigue (12). Etant donné que le degré de fatigue de référence ou que le degré de fatigue corrigé est émis selon la présence/absence de la compensation de fatigue par l'activité du nerf sympathique, le degré de fatigue accumulé approche la valeur d'évaluation de détection.
PCT/JP2009/051366 2008-02-20 2009-01-28 Dispositif d'analyse de fatigue et programme d'ordinateur WO2009104460A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2008-038767 2008-02-20
JP2008038767A JP5086834B2 (ja) 2008-02-20 2008-02-20 疲労解析装置及びコンピュータプログラム

Publications (1)

Publication Number Publication Date
WO2009104460A1 true WO2009104460A1 (fr) 2009-08-27

Family

ID=40985346

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2009/051366 WO2009104460A1 (fr) 2008-02-20 2009-01-28 Dispositif d'analyse de fatigue et programme d'ordinateur

Country Status (2)

Country Link
JP (1) JP5086834B2 (fr)
WO (1) WO2009104460A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011046178A1 (fr) * 2009-10-14 2011-04-21 株式会社デルタツーリング Dispositif et système d'estimation d'état biologique, et programme informatique
US9398875B2 (en) 2013-11-07 2016-07-26 Honda Motor Co., Ltd. Method and system for biological signal analysis
US9440646B2 (en) 2011-02-18 2016-09-13 Honda Motor Co., Ltd. System and method for responding to driver behavior
US9475502B2 (en) 2011-02-18 2016-10-25 Honda Motor Co., Ltd. Coordinated vehicle response system and method for driver behavior
US9751534B2 (en) 2013-03-15 2017-09-05 Honda Motor Co., Ltd. System and method for responding to driver state
US10499856B2 (en) 2013-04-06 2019-12-10 Honda Motor Co., Ltd. System and method for biological signal processing with highly auto-correlated carrier sequences

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6130273B2 (ja) * 2013-08-30 2017-05-17 フクダ電子株式会社 歩行試験システム及び通信端末
JP6654904B2 (ja) * 2016-01-13 2020-02-26 エコナビスタ株式会社 情報処理装置、プログラムおよび情報処理方法
KR101706508B1 (ko) * 2016-07-12 2017-02-16 한국에너지기술연구원 풍력 발전기의 피로 해석 및 등가하중 해석 시스템
CN110477897B (zh) * 2019-08-15 2022-04-01 中国体育国际经济技术合作有限公司 一种体能测试方法及系统
WO2024034072A1 (fr) * 2022-08-10 2024-02-15 三菱電機株式会社 Dispositif d'estimation d'activité cérébrale, appareil pourvu d'un dispositif d'estimation d'activité cérébrale, et dispositif de climatisation

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005039415A1 (fr) * 2003-10-23 2005-05-06 Delta Tooling Co., Ltd. Dispositif de mesure du degre de fatigue, dispositif de detection de la fatigue et programme informatique
JP2007209453A (ja) * 2006-02-08 2007-08-23 Delta Tooling Co Ltd 筋疲労評価装置
JP2009022610A (ja) * 2007-07-20 2009-02-05 Delta Tooling Co Ltd 疲労度演算装置及びコンピュータプログラム

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009026610A1 (fr) * 2007-08-24 2009-03-05 Eamon Bergin Generateur ou moteur alimente par flottabilite de gaz

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005039415A1 (fr) * 2003-10-23 2005-05-06 Delta Tooling Co., Ltd. Dispositif de mesure du degre de fatigue, dispositif de detection de la fatigue et programme informatique
JP2007209453A (ja) * 2006-02-08 2007-08-23 Delta Tooling Co Ltd 筋疲労評価装置
JP2009022610A (ja) * 2007-07-20 2009-02-05 Delta Tooling Co Ltd 疲労度演算装置及びコンピュータプログラム

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"Preprints of Meeting on Automotive Engineers, No.11-05", 18 May 2005, article NAOTERU OCHIAI ET AL.: "Shisen Yoseki Myakuha no Yuragi o Mochiita Hirodo Hyokaho no Kaihatsu", pages: 15 - 18 *
ETSUNORI FUJITA ET AL.: "Kaiteki Seat Gijutsu", JIDOSHA GIJUTSU, vol. 62, no. 2, 1 February 2008 (2008-02-01), pages 33 - 41 *
ETSUNORI FUJITA ET AL.: "Shisen Yoseki Myakuha Joho o Mochiita Chojikan Sa Za Hiro no Kan'i Hyokaho no Kaihatsu", THE JAPANESE JOURNAL OF ERGONOMICS, vol. 40, no. 5, 15 October 2004 (2004-10-15), pages 254 - 263 *

Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011046178A1 (fr) * 2009-10-14 2011-04-21 株式会社デルタツーリング Dispositif et système d'estimation d'état biologique, et programme informatique
JP5704651B2 (ja) * 2009-10-14 2015-04-22 株式会社デルタツーリング 生体状態推定装置、生体状態推定システム及びコンピュータプログラム
US10136850B2 (en) 2009-10-14 2018-11-27 Delta Tooling Co., Ltd. Biological state estimation device, biological state estimation system, and computer program
US11377094B2 (en) 2011-02-18 2022-07-05 Honda Motor Co., Ltd. System and method for responding to driver behavior
US9440646B2 (en) 2011-02-18 2016-09-13 Honda Motor Co., Ltd. System and method for responding to driver behavior
US9475502B2 (en) 2011-02-18 2016-10-25 Honda Motor Co., Ltd. Coordinated vehicle response system and method for driver behavior
US9505402B2 (en) 2011-02-18 2016-11-29 Honda Motor Co., Ltd. System and method for responding to driver behavior
US10875536B2 (en) 2011-02-18 2020-12-29 Honda Motor Co., Ltd. Coordinated vehicle response system and method for driver behavior
US9855945B2 (en) 2011-02-18 2018-01-02 Honda Motor Co., Ltd. System and method for responding to driver behavior
US9873437B2 (en) 2011-02-18 2018-01-23 Honda Motor Co., Ltd. Coordinated vehicle response system and method for driver behavior
US10246098B2 (en) 2013-03-15 2019-04-02 Honda Motor Co., Ltd. System and method for responding to driver state
US10308258B2 (en) 2013-03-15 2019-06-04 Honda Motor Co., Ltd. System and method for responding to driver state
US10752252B2 (en) 2013-03-15 2020-08-25 Honda Motor Co., Ltd. System and method for responding to driver state
US10759436B2 (en) 2013-03-15 2020-09-01 Honda Motor Co., Ltd. System and method for responding to driver state
US10759437B2 (en) 2013-03-15 2020-09-01 Honda Motor Co., Ltd. System and method for responding to driver state
US10759438B2 (en) 2013-03-15 2020-09-01 Honda Motor Co., Ltd. System and method for responding to driver state
US10780891B2 (en) 2013-03-15 2020-09-22 Honda Motor Co., Ltd. System and method for responding to driver state
US9751534B2 (en) 2013-03-15 2017-09-05 Honda Motor Co., Ltd. System and method for responding to driver state
US11383721B2 (en) 2013-03-15 2022-07-12 Honda Motor Co., Ltd. System and method for responding to driver state
US10499856B2 (en) 2013-04-06 2019-12-10 Honda Motor Co., Ltd. System and method for biological signal processing with highly auto-correlated carrier sequences
US9398875B2 (en) 2013-11-07 2016-07-26 Honda Motor Co., Ltd. Method and system for biological signal analysis

Also Published As

Publication number Publication date
JP5086834B2 (ja) 2012-11-28
JP2009195384A (ja) 2009-09-03

Similar Documents

Publication Publication Date Title
JP5086834B2 (ja) 疲労解析装置及びコンピュータプログラム
JP5022530B2 (ja) 疲労度演算装置及びコンピュータプログラム
Sekine et al. Discrimination of walking patterns using wavelet-based fractal analysis
US7470231B2 (en) Fatigue degree measurement device, fatigue detection device and computer program to be used therein
FI126600B (en) Detection of sleep phenomena using ballistocardiography
JP6757532B2 (ja) 睡眠段階判定装置、睡眠段階判定方法、睡眠段階判定プログラム
EP3187116B1 (fr) Procédé d'évaluation d'état dépressif et dispositif d'évaluation d'état dépressif
CN108888271B (zh) 一种生理参数测量系统及配备该测量系统的智能座椅
US9192333B1 (en) System and method for early detection of mild traumatic brain injury
JP5036383B2 (ja) 睡眠状態判定装置、睡眠状態判定方法及びコンピュータプログラム
Bonde et al. VVRRM: Vehicular vibration-based heart RR-interval monitoring system
JP5857609B2 (ja) 睡眠状態判別装置
JP2011083564A (ja) 自律神経成分指標推定装置及び自律神経成分指標推定方法
WO2004107978A1 (fr) Procede et dispositif d'estimation d'un stade de sommeil
JP4636861B2 (ja) 快適感評価装置
JP2007209453A (ja) 筋疲労評価装置
JP2001204714A (ja) メンタルストレス判定装置
JP2017064364A (ja) 生体状態推定装置、生体状態推定方法及びコンピュータプログラム
Malek et al. Comparison of mechanomyographic sensors during incremental cycle ergometry for the quadriceps femoris
EP3387994A1 (fr) Dispositif d'estimation biologique, procédé d'estimation biologique, programme informatique et support d'enregistrement
US20180146915A1 (en) Sleep stage determination apparatus and sleep stage determination method
JP2002209862A (ja) 体調判定装置及び身体状態判定装置
WO2017057022A1 (fr) Dispositif et procédé d'estimation d'état biologique et programme informatique associé
WO2014132722A1 (fr) Dispositif d'estimation de l'état d'un organisme vivant, programme informatique et support d'enregistrement
Yuda et al. Detection of daily emotions by wearable biometric sensors

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 09712905

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 09712905

Country of ref document: EP

Kind code of ref document: A1