SmartFall: an automatic fall detection system based on subsequence matching for the SmartCane

Authors:
Mars Lan;Ani Nahapetian;Alireza Vahdatpour;Lawrence Au;William Kaiser;Majid Sarrafzadeh
Affiliations:
University of California Los Angeles, Los Angeles, CA;University of California Los Angeles, Los Angeles, CA;University of California Los Angeles, Los Angeles, CA;University of California Los Angeles, Los Angeles, CA;University of California Los Angeles, Los Angeles, CA;University of California Los Angeles, Los Angeles, CA
Venue:
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Year:
2009

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Abstract

Fall-induced injury has become a leading cause of death for the elderly. Many elderly people rely on canes as an assistive device to overcome problems such as balance disorder and leg weakness, which are believed to have led to many incidents of falling. In this paper, we present the design and the implementation of SmartFall, an automatic fall detection system for the SmartCane system we have developed previously. SmartFall employs subsequence matching, which differs fundamentally from most existing fall detection systems based on multi-stage thresholding. The SmartFall system achieves a near perfect fall detection rate for the four types of fall conducted in the experiments. After augmenting the algorithm with an assessment on the peak impact force, we have successfully reduced the false-positive rate of the system to close to zero for all six non-falling activities performed in the experiment.