The implementation of an intelligent and video-based fall detection system using a neural network

This paper presents the development of a smart fall detector to minimise accidental falls which occur among elderly people, especially for indoor situations. A video-based detection system was utilised, as this can preserve privacy and monitor the physical activities of elderly people. In order to identify the correct situation among a set of predetermined situations, which consisted of praying, sitting, standing, bending, kneeling and lying down, a neural network system was incorporated in the fall detection computation algorithm. The neural network analysed the binary map image of the person and then identified which plausible situation the person was in at any particular instant in time. The fall detector's performance in successfully detecting falls was then evaluated using two statistical metrics: specificity and sensitivity. The performance of this fall detection system in identifying falls was also evaluated during two non-normal gait movements, stumbling and limping, so as to mimic the motions of a good proportion of the elderly people having these types of walking gait movements. It was shown that the implemented video-based fall detection system could be a promising solution for detecting indoor falls among senior citizens.