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- W2280464496 abstract "Societies are now rapidly aging around the world and the number of elderly people who live alone is rapidly increasing. About one out of every three elderly people falls each year. Falls are a major cause of serious injury and even death among the elderly. Therefore, it is important to detect falls immediately, even in private houses. Our previous paper proposed a fall detection method that uses an acceleration sensor. However, it is difficult to discriminate fall actions from other strong acceleration events such as stair walking and running. This paper proposes a wearable sensor that captures acceleration and angular velocity data simultaneously; we extend our fall detection method to use this sensor and evaluate it. Our fall detection method achieves a fall detection rate of 100% for all 420 events. Therefore, it is possible to discriminate fall actions from large acceleration actions such as stair walking and running." @default.
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- W2280464496 date "2010-01-01" @default.
- W2280464496 modified "2023-09-28" @default.
- W2280464496 title "Novel Fall Detection Method with a Wearable Hybrid-Type Sensor" @default.
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- W2280464496 doi "https://doi.org/10.1007/978-3-642-14515-5_339" @default.
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