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- tion of the developed video-based fall detection system using different metrics which measure sensitivity (i.e. the capacity of the fall detection system to detect as well as declare a fall) and specificity (i.e. the capacity of the algorithm to detect only falls) of this algorithm. The video camera was properly positioned to avoid any occluding objects and also to cover a certain range of motion of the stunt participants performing the falls. The silhouette is extracted using an approximate median filtering approach and the threshold criteria value of 30 pixels was used. Morphological filtering methods that were dilation and erosion were used to remove any spurious noises from the extracted image prior to subsequent feature analysis. Then, this extracted silhouette was scaled and quantised using 8 bits/pixel and compared to the set of predetermined scenarios using a neural network of perceptrons. This neural network was trained based on various situations and the falls of the participants which represent inputs to the neural network algorithm during the neural learning process. In this research study, the built neural network consisted of 600 inputs, as well as 10 neurons in the hidden layer together with 7 distinct outputs which represent the set of predefined situations. Furthermore, an alarm generation algorithm was included in the fall detection algorithm such that there were three states that were STATE NULL (set at 0), STATE LYING (set at 1) and STATE ALL OTHERS (set at 2) and the initial alarm count was set to 90 frames (meaning 3 seconds of recorded consecutive images at 30 frames per second). Therefore, an alarm was generated only when the in-built counter surpassed this threshold of 90 frames to signal that a fall occurred. Following the evaluation stage, it was found that the combination of the first approach fall detection algorithm method (fall angle, bounding box, and hidden Markov) was 89% with specificity and 84.2% with sensitivity which is better than individual performance. Moreover, it was found that the second approach fall detection algorithm method (neural network performance) 94.3% of the scenarios were successfully classified whereby the specificity of the developed algorithm was determined to be 94.8% and the sensitivity was 93.8% which altogether show a promising overall performance of the fall detection video-based intelligent system. Moreover, the developed fall detection system were tested using two types of handicaps such as limping and stumbling stunt participants to observe how well this detection algorithm can detect falls as in the practical situations encountered or present in elderly people. In these cases it was found that about 90.2% of the falls were detected which showed still that the developed algorithm was quite robust and reliable subjected to these two physical handicaps motion behaviours.
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