Human Activity Monitoring System Using MEMS Sensors and Machine Learning

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  • FUJITA Takayuki
    Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo
  • MASAKI Kentaro
    Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo
  • MAENAKA Kazusuke
    Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo

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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.

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