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A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System

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In recent years, health-care industry has received a major boost due to sensors i.e., accelerometers, magnetometers etc., which allow its user to get instant updates about their current health status in indoor/outdoor environments. The real driving force behind the usage of accelerometer has been the fitness industry but it also holds a prominent place in ambient smart home to monitor resident’s life-style. In this paper, we proposed a novel triaxial accelerometer-based human motion detection and recognition system using multiple features and random forest. Triaxial signals have been statistically processed to produce worthy features like variance, positive and negative peaks, and signal magnitude features. The proposed model was evaluated over HMP recognition data sets and achieved satisfactory recognition accuracy of 85.17%. The proposed system is directly applicable to any elderly/children health monitoring system, 3D animated games/movies and examining the indoor behaviors of people at home, malls and offices.

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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1D1A1A02085645).

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Correspondence to Ahmad Jalal.

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Jalal, A., Quaid, M.A.K. & Kim, K. A Wrist Worn Acceleration Based Human Motion Analysis and Classification for Ambient Smart Home System. J. Electr. Eng. Technol. 14, 1733–1739 (2019).

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