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Erschienen in:
2015 | OriginalPaper | Buchkapitel
What if Your Floor Could Tell Someone You Fell? A Device Free Fall Detection Method
verfasst von : Roberto Luis Shinmoto Torres, Asanga Wickramasinghe, Viet Ninh Pham, Damith Chinthana Ranasinghe
Erschienen in: Artificial Intelligence in Medicine
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Falls in the home environment are a serious cause of injury in older people leading to loss of independence and increased health related financial costs. In this study we investigate a device free method to detect falls by using simple batteryless radio frequency identification (RFID) tags in a smart RFID enabled carpet. Our method extracts information from the tags and the environment of the carpeted floor and applies machine learning techniques to make an autonomous decision regarding the posture of a person on the floor. This information can be used to automatically seek assistance to help the subject and decrease the negative effects of ‘long-lie’ after a fall. Our approach does not require video monitoring or body worn kinematic sensors; hence preserves the privacy of the dwellers, reduces costs and eliminates the need to remember to wear a device. Our results indicate a good performance for fall detection with an overall F-score of 94%.