Pre-Impact Fall Detection Based on Wearable Device Using Dynamic Threshold Model

Abstract

Falling accidents, including slipping, tripping and falling, are the primary reason of injury related to death not only for elderly, but for young people or worker happening at workplace also. If falling accident can be early detected in pre-fall or critical fall phase, called pre-impact fall detection, it will be very useful such as conducting airbag inflation. Furthermore, various detection methods, with an uncomplicated threshold detection method, do maximizing the true positive prediction values but the lead-time, time before subject impacts to the floor, will likely increases the chance of false alarms. Consequently the researcher found that the using of adaptive threshold may reduce false alarms. In this paper, the dynamic threshold method, automatically adjustable threshold for pre-impact fall detection in wearable device, has been proposed and experimented. For our evaluation, 192 instances of several kinds of activity of daily living and falling, were captured. All activities were performed by 6 different young healthy volunteers, 4 males and 2 females, aged between 19 and 21. The several experiments were conducted for performance evaluation including sensitivity, specificity and accuracy measurements. The results of proposed method can detect the pre-impact fall from normal activities of daily living with 99.48% sensitivity, 95.31% specificity and 97.40% accuracy with 365.12 msec of lead time. The results confirm that our proposed method with automatically adjustable threshold based on motion history, is suitable for using in pre-impact fall detection system than fixed threshold based method.