Abstract
The is an increasing number of elderly single-person households causing lonely deaths and it is a social problem. We study a watching system for elderly families by laying the piezoelectric sensors inside the house. There are few privacy issues of this system because piezoelectric sensor detects only a person’s vibration signal. Furthermore, it has a benefit of sensing the ability for a bio-signal including the respiration cycle and cardiac cycle. We propose a method of identifying the person who is on the sensor by analyzing the frequency spectrum of the bio-signal. Multiple peaks of harmonics originating from the heartbeat appear in the graph of the frequency spectrum. We propose a method to identify people by using the peak shape as a discrimination criterion.
Highlights
Along with the progress toward an aging society, the number of elderly people living alone grows dramatically, and solitary death became a social problem
We have proposed a senior-citizen-watching system that monitors elderly residents without wearing sensors on themselves by using sheet-type piezoelectric sensors [1,2]
We have found that the frequency spectrum of the signal obtained by the piezoelectric sensor shows a different pattern for each individual
Summary
Along with the progress toward an aging society, the number of elderly people living alone grows dramatically, and solitary death became a social problem. Since non-restricted sensors are used for subjects in this system, this has a merit that biological signals can be acquired in the case where abnormality occurs, even though elderly people do not want to wear wearable sensors. We have found that the frequency spectrum of the signal obtained by the piezoelectric sensor shows a different pattern for each individual. By using this principle, even when there are multiple residents in the elderly person’s home, it becomes possible to determine who has collapsed from that signal and the watching system becomes more useful. In HRV analysis, we analyze the frequency band from 0.0033 Hz to 0.4 Hz [7], whereas in this study we analyze signals with high frequencies, such as 0.3 Hz to 15 Hz, meaning that it is a completely different analysis method
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