Automatic analysis of hands clapping in severe Alzheimer patient via computer vision techniques

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.