Abstract
In recent years, aging of the population has become a major social problem. Various types of sensor networks have been applied to elderly monitoring for addressing the care problems of the elderly living alone. We have also proposed elderly monitoring systems based on wireless sensor network devices. However, vision-based sensors can also cause a mental burden on the elderly’s privacy. Furthermore, the number of sensors must be reduced, if possible. Therefore, this study proposes an elderly monitoring system composed of two vibration sensors placed on the floor and a pneumatic sensor placed on the bed. Because both sensors include considerable measurement noise, we propose a human behavior estimation method that includes anomaly detection from time-series measurement data using an autocorrelation coefficient. Finally, we discuss the effectiveness and usability of the proposed system through several experimental results. The accuracy of walking detection reaches 94.4%, while the error of heartbeat detection is 3.01 bpm.
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More From: Journal of Advanced Computational Intelligence and Intelligent Informatics
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