HMM Based Evaluation of Physical Therapy Movements Using Kinect Tracking

Recognition of human activities in videos has experienced considerable changes with the introduction of cost-effective technology that allows for the tracking of individual body parts. This has led to the development of numerous tele-health applications that aim to help patients in their recovery process. Most of these systems are based on techniques to measure the degree of similarity of time series, together with thresholds to evaluate whether the movement satisfies the specification. This means that sequences similar enough to a template, but containing deviations from the correct form, may be considered correct, and thus the quality of movement incorrectly assessed. In this paper we propose the use of Hidden Markov Models as novelty detectors to evaluate the quality of movement in human beings. The results show the potential of this approach in detecting the sequences that deviate from normality for a wide range of activities common in physical therapy and rehabilitation.