Abstract
Rehabilitation systems are becoming more impor-tant 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.
Highlights
Rehabilitation systems are becoming more important because patients can access motor skills recovery treatment from home
Virtual rehabilitation systems are intended to meet the needs of the mechanical part of the motor skills recovery treatment[14]
The training set was trained in four situations: Hidden Conditional Random Fields (HCRF) with point based descriptor, HCRF with angle based descriptor, Hidden Markov model (HMM) with point based descriptor and HMM with angle based descriptor
Summary
Rehabilitation systems are becoming more important because patients can access motor skills recovery treatment from home. Virtual rehabilitation systems are intended to meet the needs of the mechanical part of the motor skills recovery treatment[14]. These rehabilitation systems were focused only on being a guide of movements, since they showed an avatar that carried out the example of the movement to be carried out. With the introduction of cheaper motion sensors such as Kinect(Kv1) in 2010, systems were built that served as a mirror, i.e. showed the user their movements on the screen so that the patient could visualize and self-correct or have a record that could be evaluated later by the therapist. One way to evaluate movements is to apply a sequential learning algorithm to position descriptors obtained by a depth sensor such as Kv1 [1], [2], [3]
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More From: International Journal of Advanced Computer Science and Applications
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