Robotic therapy for persons with disabilities using Hidden Markov Model based skill learning

This paper describes the Hidden Markov Model (HMM) based skill learning and its application in a motion therapy system using a haptic interface. A relatively complex task, requiring motion along a labyrinth is used. A normal subject executes this task for a number of times and the best trajectory is selected as the learned skill, which is considered as a virtual therapist who can train persons with disabilities to complete the task. Two persons with disabilities on upper limb (cerebral palsy) were trained using the virtual therapist. The performance before and after therapy training, including the smoothness of the trajectory, distance ratio, time taken, tremor and impact forces are presented in this paper. This labyrinth can be used as a physical therapy for upper limb coordination, tremor reduction and motion control capabilities.