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- FUJITA Takayuki
- Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo
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- MASAKI Kentaro
- Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo
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- MAENAKA Kazusuke
- Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo
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説明
Observation of daily human activity and status is important from the viewpoints of maintaining health and preventive medical care. In this study, we describe a system for monitoring human activities and conditions that uses microelectromechanical systems (MEMS) sensors. The system contains four MEMS sensors for environmental monitoring-3-axis acceleration, barometric pressure, temperature, and relative humidity -as well as the peripheral circuitry for each sensor. Measured human activity data are stored in a memory via an on-board microprocessor. We measured environmental data for a subject's daily life. To estimate the subject's activity and his condition from a huge volume of data, we applied a soft computing technique to machine learning for the automatic extraction of human-activity classification.
収録刊行物
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- 知能と情報
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知能と情報 20 (1), 3-8, 2008
日本知能情報ファジィ学会
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詳細情報 詳細情報について
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- CRID
- 1390001205186657280
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- NII論文ID
- 110006614041
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- NII書誌ID
- AA1181479X
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- ISSN
- 18817203
- 13477986
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- NDL書誌ID
- 9383300
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可