Hidden Markov Model Approach for the Assessment of Tele-Rehabilitation Exercises
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.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.