1996 Fiscal Year Final Research Report Summary
Portable Classification System of Daily Activity and Exercise Load
| Project/Area Number |
07555394
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 試験 |
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| Research Field |
Dynamics/Control
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| Research Institution | Tohoku University |
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Principal Investigator |
INOOKA Hikaru Tohoku University, Graduate School of Information Sciences Professor, 大学院・情報科学研究科, 教授 (20006191)
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| Co-Investigator(Kenkyū-buntansha) |
SAGAWA Koichi Tohoku University, Graduate School of Information Sciences Research Associate, 大学院・情報科学研究科, 助手 (30272016)
OHBA Kotaro Tohoku University, Dept.of Mechanical Engineering Lecturer, 工学部, 講師 (70221835)
ISHIHARA Tadashi Tohoku University, Graduate School of Information Sciences Associate Professor, 大学院・情報科学研究科, 助教授 (10134016)
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| Project Period (FY) |
1995 – 1996
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| Keywords | Activity / Gait / Stairs / Estimation / Acceleration / Experiment / Measurement / ECG |
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| Research Abstract |
This study proposes new algorithm to classify and record human walking pattern and daily activity by using a portable measurement system.
Classification method of human gait developed by Inooka et al is programd in the system. The method classifies the human gait into walking flat, going up the stairs and going down the stairs. The system performs real-time classification of human gait and records the estimated walking patterns into the Holter ECG recorder with the ECG of human. It is suggested that the system yields the relation between human walking patterns and the ECG.
Modification algorithm of human walking path is also proposed because numerical error is included in the estimated walking path calculated by the integration of acceleration and angular velocity. This study utilizes the estimated action and already-known landmarks within an error ellipse. If one landmark has a relation to the estimated action, then the calculated path is modified to pass the landmark. Existence of a chair as a landmark is related to action of sitting down, for example.
Moreover, we proposed a method to classify the human activity into four kinds of patterns : walking, rest, movement and lying. These activities are derived by statistical characteristics of acceleration and comparison the acceleration wave form with reference wave form already measured. Furthermore, the continuous activity is determined by an automaton of behavior transition which defines sequence of human behavior. The application of the automaton in running its course and tracing back is effective to the reliable estimation of human activity. The experimental results show that the human activity in a room is favorably estimated by using the automaton.
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