[Abstract] Hidden Markov Model Approach for the Assessment of Tele-Rehabilitation Exercises

Abstract

Two mandatory conditions in the development of tele-rehabilitation platforms are:

• (i) being based on affordable technologies and
• (ii) ensuring the patient is performing the exercises correctly.

To do so, the present study proposes a cognitive algorithm based on a Hidden Markov Model (HMM) approach to assess in real-time the quality of a human movement recorded through a low-cost motion capture device.

The assessment of the correctness of the exercises, which includes the detection of multiple undesirable compensatory movements, shows a very high accuracy (the average performance = 97%). In addition, the proposed model shows a potential for providing the patients with real-time feedback on their performance (up to five times a second).

A certain limitation of the model occurs for the compensatory movements characterized by an absence of translational motion of the centre of mass (17% of misclassifications). In this situation, additional features are required to properly assess the quality of the therapeutic exercise.

 

via Hidden Markov Model Approach for the Assessment of Tele-Rehabilitation Exercises | Deters | International Journal of Artificial Intelligence™