Virtual rehabilitation using sequential learning algorithms

Abstract

Rehabilitation systems are becoming more important now because patients can access motor skills recovery treatment from home, reducing the limitations of time, space and cost of treatment in a medical facility. Traditional rehabilitation systems served as movement guides, later as movement mirrors, and in recent years research has sought to generate feedback messages to the patient based on the evaluation of his or her movements. Currently the most commonly used algorithms for exercise evaluation are Dynamic time warping (DTW), Hidden Markov model (HMM), Support vector machine (SVM). However, the larger the set of exercises to be evaluated, the less accurate the recognition becomes, generating confusion between exercises that have similar posture descriptors. This research paper compares two HMM classifiers and Hidden Conditional Random Fields (HCRF) plus two types of posture descriptors, based on points and based on angles. Point representation proves to be superior to angle representation, although the latter is still acceptable. Similar results are found in HCRF and HMM.