Human activity monitoring system using MEMS sensors and machine learning (特集 医用システム)
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概要
- 論文の詳細を見る
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.
- 日本知能情報ファジィ学会の論文
- 2008-02-15
著者
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Fujita Takayuki
Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of
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Fujita Takayuki
Department Of Electrical Engineering And Computer Sciences Graduate School Of Engineering University
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MAENAKA Kazusuke
Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, Universi
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MASAKI Kentaro
Department of Electrical Engineering and Computer Sciences Graduate School of Engineering, Universit
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Masaki Kentaro
Department Of Electrical Engineering And Computer Sciences Graduate School Of Engineering University
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Fujita Takayuki
Department Of Electrical Engineering And Computer Sciences Graduate School Of Engineering University
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Maenaka Kazusuke
Department Of Electrical Engineering And Computer Sciences Graduate School Of Engineering University
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