HUMAN FATIGUE ASSESSMENT DEVICE AND HUMAN FATIGUE ASSESSMENT METHOD

• US 20110288424A1
• Filed: 10/26/2010
• Published: 11/24/2011
• Est. Priority Date: 10/29/2009
• Status: Active Grant

• Current Assignee: Panasonic Corporation (Panasonic Holdings Corporation)
• Original Assignee: Panasonic Corporation (Panasonic Holdings Corporation)
• Inventors: Tanaka, Masaaki, Kanai, Etsuko, Shigihara, Yoshihito, Funakura, Masami, Mizuno, Kei, Watanabe, Yasuyoshi
• Granted Patent: US 8,706,206 B2
• Time in Patent Office: Days
• US Class Current: 600/500
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system

Abstract

A human fatigue assessment device capable of performing highly accurate fatigue assessment is provided. The human fatigue assessment device (100) includes: a physiological signal measuring unit (101) which measures a pulse wave signal of a user; a feature value extracting unit (102) which extracts first feature values each of which is obtained from a systolic posterior component of the pulse wave signal measured by the physiological signal measuring unit (101); a storage unit (103) in which the first feature values extracted to by the feature value extracting unit (102) are stored; and a fatigue determining unit (104) which determines whether or not the user is fatigued, using the first feature values extracted by the feature value extracting unit (102), in which the fatigue determining unit (104) compares a first feature value among the first feature values extracted by the feature value extracting unit (102) and at least one of the first feature values stored in the storage unit (103), to determine whether or not the user is fatigued.

Claims

1. A human fatigue assessment device comprising:

a physiological signal measuring unit configured to measure a pulse wave signal of a user;

a feature value extracting unit configured to extract first feature values each of which is obtained from a systolic posterior component of the pulse wave signal measured by said physiological signal measuring unit;

a storage unit in which the first feature values extracted by said feature value extracting unit are stored; and

a fatigue determining unit configured to determine whether or not the user is fatigued, using the first feature values extracted by said feature value extracting unit,

wherein said fatigue determining unit is configured to compare a first feature value among the first feature values extracted by said feature value extracting unit and at least one of the first feature values stored in said storage unit, to determine whether or not the user is fatigued.

2. The human fatigue assessment device according to claim 1,

wherein said feature value extracting unit is configured to calculate an accelerated plethysmogram from the pulse wave signal, and to extract the first feature values, using information on at least a c wave or a d wave which is a component wave of an accelerated plethysmogram corresponding to the systolic posterior component.

3. The human fatigue assessment device according to claim 2,

wherein said feature value extracting unit is configured to extract a ratio of a peak value of the c wave with respect to a peak value of an a wave, a b wave, or an e wave in the accelerated plethysmogram as the first feature value, and

said fatigue determining unit is configured to determine that the user is fatigued, when absolute values of the first feature values increase in time-series.

4. The human fatigue assessment device according to claim 2,

wherein said feature value extracting unit is configured to extract a difference between peak values of the a wave and the c wave in the accelerated plethysmogram, and

said fatigue determining unit is configured to determine that the user is fatigued when absolute values of the first feature values decrease in time-series.

5. The human fatigue assessment device according to claim 2,

wherein said feature value extracting unit is configured to extract a value obtained by dividing a difference between peak values of the c wave and the d wave in the accelerated plethysmogram by the a wave in the accelerated plethysmogram, and

said fatigue determining unit is configured to determine that the user is fatigued when absolute values of the first feature values increase in time-series.

6. The human fatigue assessment device according to claim 1, further comprising

a device controller for controlling an external device which stimulates the user when said fatigue determining unit determines that the user is fatigued.

7. The human fatigue assessment device according to claim 1,

wherein said physiological signal measuring unit is further configured to measure a heartbeat or pulse wave of the user as a physiological signal,

said feature value extracting unit is further configured to extract second feature values each indicating amount of parasympathetic nerve activity, and each obtained from the physiological signal measured by said physiological signal measuring unit,

said storage unit further stores the second feature values extracted by said feature value extracting unit,

said human fatigue assessment device further comprises

a fatigue type determining unit configured to determine a type of the fatigue of the user as to whether the fatigue is due to difficult work or due to monotonous work, and

when said fatigue determining unit determines that the user is fatigued, said fatigue type determining unit is configured to determine the type of fatigue by comparing a second feature value among the second feature values extracted by said feature value extracting unit and at least one of the second feature values stored in said second feature values stored in said storage unit.

8. The human fatigue assessment device according to claim 7,

wherein said fatigue type determining unit is configured to determine that the fatigue is due to difficult work when the second feature values decrease in time-series, and to determine that the fatigue due to monotonous work when the second feature values do not decrease in time-series.

9. The human fatigue assessment device according to claim 1,

wherein said physiological signal measuring unit is further configured to measure a brain signal of the user as a physiological signal,

said feature value extracting unit is further configured to extract third feature values each of which is obtained from the physiological signal measured by said physiological signal measuring unit, and is related to at least one of a β

wave and an α

wave,

said storage unit stores the third feature values extracted by said feature value extracting unit,

said human fatigue assessment device further comprises

a fatigue type determining unit configured to determine a type of the fatigue of the user as to whether the fatigue is due to difficult work or due to monotonous work, using the third feature values extracted by said feature value extracting unit, and

when said fatigue determining unit determines that the user is fatigued, said fatigue type determining unit is configured to determine the type of fatigue by comparing a third feature value among the third feature values extracted by said feature value extracting unit and at least one of the third feature values stored in said storage unit.

10. The human fatigue assessment device according to claim 9, further comprising

a checking unit configured to generate checking information for checking whether the user is in an open-eye state or in a closed-eye state,

wherein said physiological signal measuring unit is configured to attach the checking information to the measured physiological signal, and

said feature value extracting unit is configured to extract the third feature values each of which is using at least one of a power value in a β

waveband and a power value in an α

waveband that are in a time period in which said checking unit determines that the user is in the open-eye state or the closed-eye state.

11. The human fatigue assessment device according to claim 10,

wherein said feature value extracting unit is configured to extract the third feature values each of which is using the power value in the α

waveband in the time period in which said checking unit determines that the user is in the closed-eye state, and

said fatigue type determining unit is configured to determine that the fatigue is due to difficult work when the third feature values increase in time-series.

12. The human fatigue assessment device according to claim 10,

wherein said feature value extracting unit is configured to extract the third feature values each of which is using the power value in the β

waveband in the time period in which said checking unit determines that the user is in the open-eye state or the closed-eye state, and

said fatigue type determining unit is configured to determine that the fatigue is due to monotonous work when the third feature values decrease in time-series.

13. The human fatigue assessment device according to claim 1, further comprising:

a stimulation output unit configured to output audio stimulation for stimulating the user'"'"'s auditory sense; and

a fatigue type determining unit configured to determine a type of the fatigue of the user as to whether the fatigue is due to difficult work or due to monotonous work, using the first feature values extracted by said feature value extracting unit,

wherein, when said fatigue determining unit determines that the user is fatigued, said fatigue type determining unit is configured to determine the type of the fatigue by comparing (i) first feature values stored in said storage unit in a time period before an output of audio stimulation by said stimulation output unit and (ii) first feature values in a time period when said stimulation output unit outputs the audio stimulation.

14. The human fatigue assessment device according to claim 13,

wherein said feature value extracting unit is configured to calculate an accelerated plethysmogram from the pulse wave signal, and to extract a ratio of a peak value of the c wave with respect to a peak value of the a wave in the plethysmogram as the first feature value, and

said fatigue type determining unit is configured to determine that the fatigue is due to monotonous work when the first feature values in the time period in which said stimulation output unit outputs the audio stimulation are increased from the first feature values stored in said storage unit in a time period before the output of the audio stimulation by said stimulation output unit, and to determine that the fatigue is due to difficult work when the first feature values in the time period in which said stimulation output unit outputs the audio stimulation are not increased from the first feature values stored in said storage unit in a time period before the output of the audio stimulation by said stimulation output unit.

15. The human fatigue assessment device according to claim 7, further comprising

a device controller for controlling an external device which stimulates the user according to the type of fatigue determined by said fatigue type determining unit.

16. A human fatigue assessment device comprising:

a physiological signal measuring unit configured to measure a heartbeat or pulse wave of a user as a physiological signal;

a feature value extracting unit configured to extract second feature values each indicating amount of parasympathetic nerve activity and each obtained from the physiological signal measured by said physiological signal measuring unit;

a storage unit in which the second feature values extracted by said feature value extracting unit; and

a fatigue type determining unit configured to determine a type of the user'"'"'s fatigue as to whether the fatigue is due to difficult work or due to monotonous work, using the second feature values extracted by said feature value extracting unit,

wherein said fatigue type determining unit is configured to determine the type of the user'"'"'s fatigue by comparing a second feature value among the second feature values extracted by said feature value extracting unit and at least one of the second feature values stored in said storage unit.

17. A human fatigue assessment device comprising:

a physiological signal measuring unit configured to measure a brain signal of a user as a physiological signal;

a feature value extracting unit configured to extract third feature values each of which is obtained from the physiological signal measured by said physiological signal measuring unit and related to at least one of a β

wave and an α

wave;

a storage unit in which the third feature values extracted by said feature value extracting unit is stored; and

a fatigue type determining unit configured to determine a type of the user'"'"'s fatigue as to whether the fatigue is due to difficult work or due to monotonous work, using the third feature values extracted by said feature value extracting unit,

wherein said fatigue type determining unit is configured to determine the type of the fatigue by comparing a third feature value among the third feature values extracted by said feature value extracting unit and at least one of the third feature values stored in said storage unit.

18. A human fatigue assessment device comprising:

a stimulation output unit configured to output audio stimulation for stimulating a user'"'"'s auditory sense;

a physiological signal measuring unit configured to measure a pulse wave signal of the user;

a feature value extracting unit configured to extract first feature values each of which is obtained from a systolic posterior component of the pulse wave signal measured by said physiological signal measuring unit;

a storage unit in which the first feature values extracted by said feature value extracting unit is stored; and

a fatigue type determining unit configured to determine a type of the fatigue of the user whether it is fatigue due to difficult work or due to monotonous work, using the first feature values extracted by said feature value extracting unit,

wherein said fatigue type determining unit is configured to determine the type of the fatigue by comparing (i) first feature values stored in said storage unit in a time period before an output of audio stimulation by said stimulation output unit and (ii) first feature values in a time period when said stimulation output unit outputs the audio stimulation.

19. A human fatigue assessment method for assessing a fatigue of a living body by a computer, said method comprising:

measuring a pulse wave signal of a user;

extracting first feature values each of which is obtained from a systolic posterior component of the pulse wave signal measured in said measuring;

storing, in a storage unit, the first feature values extracted in said extracting; and

determining whether or not the user is fatigued, using the first feature values extracted in said extracting,

wherein, in said determining, a first feature value among the first feature values extracted in said extracting and at least one of the first feature values read from the storage unit among the first feature values stored in the storage unit are compared to determine whether or not the user is fatigued.

20. A program stored in a non-transitory computer readable recording medium, said program causing a computer to execute steps in the human fatigue assessment method according to claim 19.