Averaged Hidden Markov models in kinect-based rehabilitation system

Abstract

In this paper the Averaged Hidden Markov Models (AHMMs) are examined for the upper limb rehabilitation purposes. For the data acquisition the Microsoft Kinect 2.0 sensor is used. The system is intended for low-functioning autistic children whose rehabilitation is often based on sequences of images presenting the subsequent gestures. The number of such training sets is limited and the preparation of a new one is not available for everyone, whereas each child requires the individual therapy. The advantage of the presented system is that new activities models could be easily added. The conducted experiments provide satisfactory results, especially in the case of single hand rehabilitation and both hands rehabilitation based on asymmetric gestures.