Detection of Interaction with Depth Sensing and Body Tracking Cameras in Physical Rehabilitation

Abstract

Body tracking sensors have become an integral part of the physical therapy not only as a motivational tool used by games but also as a diagnostic instrument. Skeletal tracking helps to analyze and quantify human motion and thus can provide tangible results from therapy sessions. Markerless skeletal tracking with depth sensing cameras represents currently the most popular approach mainly due to low cost cameras that use a model based approach to recognize a human skeleton. The model based approach works in many scenarios but faces limitations as well. This paper presents a method for identifying the patient and detecting interactions between the patient and the therapist. Identifying interactions helps to discriminate between active and passive motion of the patient as well as to estimate the accuracy of the skeletal data. Our experiments show the state-of-the-art performance of real-time face recognition from a low resolution images that is sufficient to use in adaptive systems. We also compare the performance of our interaction detection method with two other approaches (markerless and marker based approach) and shows its superior performance.