DEVICE FOR ESTIMATING STATE OF LIVING ORGANISM

• US 20130030256A1
• Filed: 01/31/2011
• Published: 01/31/2013
• Est. Priority Date: 02/18/2010
• Status: Active Grant

• Current Assignee: Delta Tooling Co., Ltd.
• Original Assignee: Delta Tooling Co., Ltd.
• Inventors: Fujita, Etsunori, Ogura, Yumi, Maeda, Shinichiro, Ochiai, Naoki, Wagata, Shigeki
• Granted Patent: US 9,144,402 B2
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/6887 mounted on external non-worn devices, e.g. non-medical devices
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 7/00 Instruments for auscultation

Abstract

A technology to grasp a state of a human being more accurately is provided. The technology is provided with means for acquiring a time-series waveform of a frequency from a time-series waveform of a biological signal sampled from the upper body of a human being and for further acquiring a time-series waveform of frequency slope and a time-series waveform of frequency fluctuation and for applying frequency analysis to them. In the frequency analysis, a power spectrum of each frequency corresponding to a functional adjustment signal, a fatigue reception signal, and an activity adjustment signal, respectively, determined in advance is acquired. Then, a state of a human being is determined from a time-series change of each power spectrum. The fatigue reception signal indicates a degree of progress of fatigue in a usual active state and thus, by comparing it with degrees of predominance of the functional adjustment signal and the activity adjustment signal as their distribution rates, a state of a human being (relaxed state, fatigued state, state in which sympathetic nerve is predominant, a state in which parasympathetic nerve is predominant and the like) can be determined more accurately.

Claims

1. A biological body state estimation device for estimating a state of a human being by using a biological signal sampled from the upper body of a human by biological signal measuring means and is characterized by having:

first frequency calculating means for acquiring a zero-crossing point at which a time-series waveform of a biological signal obtained by the biological signal measuring means is changed from positive to negative and for acquiring the time-series waveform of the frequency of the biological signal by using this zero-crossing point;

first frequency slope time-series analysis calculating means for performing movement calculation for acquiring a slope of the frequency at each predetermined time window set with a predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the first frequency calculating means and for outputting a time-series change of the slope of the frequency obtained at each time window as a frequency slope time-series waveform;

power spectrum calculating means for applying frequency analysis to the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating means and acquiring a power spectrum of each frequency corresponding to a functional adjustment signal, a fatigue reception signal, and an activity adjustment signal determined in advance; and

determining means for determining a state of a human being from a time-series change of the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired by the power spectrum calculating means.

2. The biological body state estimation device according to claim 1, further comprising:

first frequency fluctuation time-series analysis calculating means for performing the movement calculation for acquiring a mean value of the frequency at each predetermined time window set with the predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the first frequency calculating means and acquiring a time-series waveform of the mean value of the frequency obtained at each time window as a frequency fluctuation time-series waveform, wherein

the power spectrum calculating means further has means for applying frequency analysis to the frequency fluctuation time-series waveform obtained by the first frequency fluctuation time-series analysis calculating means and acquiring a power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal determined in advance.

3. The biological body state estimation device according to claim 1, further comprising:

second frequency calculating means for acquiring a maximum value by applying smoothing differentiation to an original waveform of the biological signal obtained by the biological signal measuring means and acquiring the time-series waveform of the frequency of the biological signal by using this maximum value; and

second frequency slope time-series analysis calculating means for performing the movement calculation for acquiring a slope of the frequency at the predetermined time window set with the predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the second frequency calculating means and outputting a time-series change of the slope of the frequency obtained at each time window as a frequency slope time-series waveform, wherein

the power spectrum calculating means further has means for applying frequency analysis to the frequency slope time-series waveform obtained by the second frequency slope time-series analysis calculating means and acquiring a power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal determined in advance.

4. The biological body state estimation device according to claim 3, further comprising:

second frequency fluctuation time-series analysis calculating means for performing the movement calculation to acquire a mean value of the frequency at each predetermined time window set with the predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the second frequency calculating means and acquiring a time-series waveform of a mean value of the frequency obtained at each time window as a frequency fluctuation time-series waveform, wherein

the power spectrum calculating means further has means for applying frequency analysis to the frequency fluctuation time-series waveform obtained by the second frequency fluctuation time-series analysis calculating means and acquiring a power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal determined in advance.

5. The biological body state estimation device according to claim 1, wherein

the determining means is means for determining a state of a human being by acquiring a time-series change in the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating means and acquiring a degree of relative predominance of each signal as a distribution rate.

6. The biological body state estimation device according to claim 5, wherein

the determining means determines the state of a human being by acquiring the time-series change in the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating means and by acquiring a degree of relative predominance of each signal as a distribution rate;

and

determines the state of a human being by adding a time-series change of the distribution rate of the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired from at least one of the frequency fluctuation time-series waveform obtained by the first frequency fluctuation time-series analysis calculating means, the frequency slope time-series waveform obtained by the second frequency slope time-series analysis calculating means, and the frequency fluctuation time-series waveform obtained by the second frequency fluctuation time-series analysis calculating means.

7. The biological body state estimation device according to claim 5, wherein

the determining means has means for determining that a time zone in which the power spectrum of the fatigue reception signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating means indicates a time-series change having a lowering tendency is an emergence period of a state change of a human being.

8. The biological body state estimation device according to claim 7, wherein

the determining means has means for determining a sleepiness-related phenomenon emergence period if the power spectrum of the functional adjustment signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating means indicates a time-series change having a rising tendency in a time zone when the power spectrum of the fatigue reception signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating means indicates a time-series change having a lowering tendency.

9. The biological body state estimation device according to claim 8, wherein

the determining means further has means for determining the sleepiness-related phenomenon emergence period if the functional adjustment signal acquired from at least one of the frequency fluctuation time-series waveform obtained by the first frequency fluctuation time-series analysis calculating means, the frequency slope time-series waveform obtained by the second frequency slope time-series analysis calculating means, and the frequency fluctuation time-series waveform obtained by the second frequency fluctuation time-series analysis calculating means indicates a time-series change with a rising tendency.

10. The biological body state estimation device according to claim 1, wherein

the functional adjustment signal used by the power spectrum calculating means has a frequency of 0.0027 Hz or less, the fatigue reception signal has a frequency within a range from 0.002 to 0.0052 Hz, and the activity adjustment signal has a frequency within a range from 0.004 to 0.007 Hz.

11. A computer program incorporated in a biological body state estimation device for estimating a state of a human being by using a biological signal sampled from the upper body of a human being by the biological signal measuring means, comprising:

a first frequency calculating step of acquiring a zero-crossing point at which a time-series waveform of a biological signal obtained by the biological signal measuring means is changed from positive to negative and of acquiring the time-series waveform of the frequency of the biological signal by using this zero-crossing point;

first frequency slope time-series analysis calculating step of performing movement calculation for acquiring a slope of the frequency at each predetermined time window set with a predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the first frequency calculating step and of outputting a time-series change of the slope of the frequency obtained at each time window as a frequency slope time-series waveform;

power spectrum calculating step of applying frequency analysis to the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating step and acquiring a power spectrum of each frequency corresponding to a functional adjustment signal, a fatigue reception signal, and an activity adjustment signal determined in advance; and

determining step of determining a state of a human being from a time-series change of the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired by the power spectrum calculating step.

12. The computer program according to claim 11, further comprising:

a first frequency fluctuation time-series analysis calculating step of performing the movement calculation for acquiring a mean value of the frequency at each predetermined time window set with the predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the first frequency calculating step and acquiring a time-series waveform of the mean value of the frequency obtained at each time window as a frequency fluctuation time-series waveform; and

the power spectrum calculating step further has a step of applying frequency analysis to the frequency fluctuation time-series waveform obtained by the first frequency fluctuation time-series analysis calculating step and of acquiring a power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal determined in advance.

13. The computer program according to claim 11, further comprising:

a second frequency calculating step of acquiring a maximum value by applying smoothing differentiation to an original waveform of the biological signal obtained by the biological signal measuring means and of acquiring the time-series waveform of the frequency of the biological signal by using this maximum value; and

a second frequency slope time-series analysis calculating step of performing the movement calculation for acquiring a slope of the frequency at each predetermined time window set with the predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the second frequency calculating step and of outputting a time-series change of the slope of the frequency obtained at each time window as a frequency slope time-series waveform, wherein

the power spectrum calculating step further has a step of applying frequency analysis to the frequency slope time-series waveform obtained by the second frequency slope time-series analysis calculating step and of acquiring a power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal determined in advance.

14. The computer program according to claim 13, further comprising:

a second frequency fluctuation time-series analysis calculating step of performing the movement calculation to acquire a mean value of the frequency at each predetermined time window set with the predetermined overlap time in the time-series waveform of the frequency of the biological signal obtained by the second frequency calculating step and acquiring a time-series waveform of a mean value of the frequency obtained at each time window as a frequency fluctuation time-series waveform, wherein

the power spectrum calculating step further has a step of applying frequency analysis to the frequency fluctuation time-series waveform obtained by the second frequency fluctuation time-series analysis calculating step and acquiring a power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal determined in advance.

15. The computer program according to claim 11, wherein

the determining step is a step for determining a state of a human being by acquiring a time-series change in the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating step and by acquiring a degree of relative predominance of each signal as a distribution rate.

16. The computer program according to claim 15, wherein

the determining step determines the state of a human being by acquiring the time-series change in the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating step and by acquiring a degree of relative predominance of each signal as a distribution rate;

and

determines the state of a human being by adding a time-series change of the distribution rate of the power spectrum of each frequency corresponding to the functional adjustment signal, the fatigue reception signal, and the activity adjustment signal acquired from at least one of the frequency fluctuation time-series waveform obtained by the first frequency fluctuation time-series analysis calculating step, the frequency slope time-series waveform obtained by the second frequency slope time-series analysis calculating step, and the frequency fluctuation time-series waveform obtained by the second frequency fluctuation time-series analysis calculating step.

17. The computer program according to claim 15, wherein

the determining step has a step of determining that a time zone in which the power spectrum of the fatigue reception signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating step indicates a time-series change having a lowering tendency is an emergence period of a state change of a human being.

18. The computer program according to claim 17, wherein

the determining step has a step of determining a sleepiness-related phenomenon emergence period if the power spectrum of the functional adjustment signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating step indicates a time-series change having a rising tendency in a time zone when the power spectrum of the fatigue reception signal acquired from the frequency slope time-series waveform obtained by the first frequency slope time-series analysis calculating step indicates a time-series change having a lowering tendency.

19. The computer program according to claim 18, wherein

the determining step further has a step of determining the sleepiness-related phenomenon emergence period if the functional adjustment signal acquired from at least one of the frequency fluctuation time-series waveform obtained by the first frequency fluctuation time-series analysis calculating step, the frequency slope time-series waveform obtained by the second frequency slope time-series analysis calculating step, and the frequency fluctuation time-series waveform obtained by the second frequency fluctuation time-series analysis calculating step indicates a time-series change with a rising tendency.

20. The computer program according to claim 11, wherein

the functional adjustment signal used by the power spectrum calculating step has a frequency of 0.0027 Hz or less, the fatigue reception signal has a frequency within a range from 0.002 to 0.0052 Hz, and the activity adjustment signal has a frequency within a range from 0.004 to 0.007 Hz.