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Automatic Analysis of Hands Clapping in Severe Alzheimer Patient via Computer Vision Techniques
Authors: Ovgu Ozturk, Ceyhun Burak Akgul, Aytul Ercil, Melike Sahiner, Turker Sahiner
Published in: Alzheimers & Dementia - ALZHEIMERS DEMENT , vol. 7, no. 4, pp. S454-S454, 2011
Publication year: 2011
Abstract: Background: In this work, we present a hands clapping rhythm analysis module of a video analytics framework, which monitors elderly patients and automatically collect statistical data about patient activities. Hands clapping activity is analyzed in terms of frequency of clapping, extent of clapping, and direction change. A severe level Alzheimer patient was chosen from an elderly house. Methods: The main idea makes use of optical flow vectors which represent themotion change of image features in consecutive frames. The algorithm steps are composed of detecting optical flow vectors in skin regions, clustering based on the direction, calculating the average flow vector in each cluster and observing these vectors over time. The magnitude of the average flow represents the speed of motion. Results: In the supplementary figure, handsclapping.png, the experimental results are presented. Hands motion of the patient on the right has been observed for 100 frames (4 secs). Input hands region, detected optical flows are demonstrated, followed by the two resultant motion flow groups depicted by black and white regions. The patient is active during 100 frames and claps hands eight times, two of which are long extent clapping, when the patient is very happy. In the graphs, blue lines represent the motion of right hand, while red lines represent left hand. The occurrence of clapping hands is detected by finding the instant, when right hand moves in (+) direction and changes direction to (-); and left handmoves in (-) direction and changes to (+); and the speed of each hand is greater than 2 units. It happens at frames: 4, 11,17,28,53,63,68,87. The graph in the bottom shows the distance traveled by each hand per frame. The symmetry in motion waves of right and left hand depicts the clapping motion characteristics and validates effectiveness of the proposed method. Conclusions: In this work, a hands clapping analysis module is introduced and used to monitor hand movements of a severe Alzheimer patient. The results of the experiments demonstrate the successful analysis of hands clapping motion. This initial work shows the potential of computer vision systems to automatically analyze patient behaviors and obtain statistical data.
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