Automatic human body feature extraction in serious games applied to rehabilitation robotics

Abstract

Current modern society is characterized by an increasing level of elderly population. This population group is usually suffers important physical and cognitive impairments, which implies that older people need care, attention and supervision by health professionals. In this paper, a new system for supervising rehabilitation therapies using autonomous robots for elderly people is presented. The therapy explained in this work is a modified version of the classical 'Simon Says' game, where a robot executes a list of motions and gestures that the patient has to repeat. The success of this therapy from the point of view of the software is to provide from an algorithm that detect and classified the gestures that the human is imitating. The algorithm proposed in this paper is based on the analysis of sequences of images acquired by a low cost RGB-D sensor. A set of human body features is detected and characterized during the motion, allowing the robot to classify the different gestures. In addition, this paper describes the human-robot interaction performed by the 'Simon Says' game implementation. Experimental results demonstrate the robustness and accuracy of the detection and classification method, which is crucial for the development of the therapy.