@inproceedings{088ba1a9152f4dad99b8d284602be846,
title = "Detection of falls at home using floor vibrations and sound",
abstract = "Falls are very prevalent among the elderly especially in their home. Approximately one in every three adults 65 years old or older falls each year, 30% of those falls result in serious injuries and more than 70% of the event are at home. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In this paper we present an innovative automatic system for detection of elderly people falls at home. The system is based on floor vibration and acoustic sensing, and uses pattern recognition algorithm to discriminate between human fall events and other events. The proposed solution is unique, inexpensive, and does not require the person to wear anything. Using the proposed system we can detect human falls with a sensitivity of 97.5% and specificity of 98.5%.",
keywords = "Fall detection, Pattern recognition, Signal processing",
author = "Dima Litvak and Israel Gannot and Yaniv Zigel",
year = "2008",
month = dec,
day = "1",
doi = "10.1109/EEEI.2008.4736581",
language = "English",
isbn = "9781424424825",
series = "IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings",
pages = "514--518",
booktitle = "2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008",
note = "2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008 ; Conference date: 03-12-2008 Through 05-12-2008",
}