A new method for fall detection of elderly based on human shape and motion variation

Fall detection for elderly and patient has been an active research topic due to the great demand for products and technology of fall detection in the healthcare industry. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, we present a new method for fall detection based on the variation of shape and motion. First, we use the CodeBook method to extract the person silhouette from the video. Then, information of rectangle, ellipse and histogram projection are used to provide features to analyze the person shape. In addition, we represent the person shape by three blocks extracted from rectangle. Then, we use optical flow to analyze the person motion within each blocks. Finally, falls are detected from normal activities using thresholding-based method. All experiments show that our fall detection system achieves very good performances in accuracy and error rate. © Springer International Publishing AG 2016.