Original Article | 2012, Vol.34, No.6, pp. 637-644
A system for improving fall detection performance using critical phase fall signal and a neural network
Patimakorn Jantaraprim, Pornchai Phukpattaranont, Chusak Limsakul and Booncharoen Wongkittisuksa
pp. 637 - 644
Abstract
We present a system for improving fall detection performance using a short time min-max feature based on the specific signatures of critical phase fall signal and a neural network as a classifier. Two subject groups were tested: Group A involving falls and activities by young subjects; Group B testing falls by young and activities by elderly subjects. The performance was evaluated by comparing the short time min-max with a maximum peak feature using a feed-forward backpropagation network with two-fold cross validation. The results, obtained from 672 sequences, show that the proposed method offers a better performance for both subject groups. Group B’s performance is higher than Group A’s. The best performances are 98.2% sensitivity and 99.3% specificity for Group A, and 99.4% sensitivity and 100% specificity for Group B. The proposed system uses one sensor for a body’s position, without a fixed threshold for 100% sensitivity or specificity and without additional processing of posture after a fall.
