Development of a healthcare software system for the elderly

This research focused on the implementation of a reliable intelligent fall detection system so as to reduce accidental falls among the elderly people. A video-based detection system was used because it preserved privacy while monitoring the activities of the senior citizens. Another advantage of the video-based system is that the senior citizens are able to move freely without experiencing any hassles in wearing them as opposed to portable fall detection sensors so that they can have a more independent and happy life. A scientific research method was employed to improve the existing fall detection systems in terms of reliability and accuracy. This thesis consists of four stages where the first stage reviews the literature on the current fall detection systems, the second stage investigates the various algorithms of these existing fall detection systems, the third stage describes the proposed fall detection algorithm in detecting falls using two distinct approaches. The first approach deals with the use of specific features of the silhouette, an extracted binary map obtained from the subtraction of the foreground from the background, to determine the fall angle (FA), the bounding box (BB) ratio, the Hidden Markov Models (HMM) and the combination of FA, BB, and HMM. The second approach used is the neural network approach which is incorporated in the algorithm to identify a predetermined set of situations such as praying, sitting, standing, bending, kneeling, and lying down. The fourth stage involves the evalua-