Realtime motion assessment for rehabilitation exercises: integration of kinematic modeling with fuzzy inference

• Autorzy: Zhao W. , Lun R. , Espy D. D. , Reinthal M. A.

Identyfikatory

DOI

10.1515/jaiscr-2015-0014

• Języki publikacji: EN

Abstrakty

EN

This article describes a novel approach to realtime motion assessment for rehabilitation exercises based on the integration of comprehensive kinematic modeling with fuzzy inference. To facilitate the assessment of all important aspects of a rehabilitation exercise, a kinematic model is developed to capture the essential requirements for static poses, dynamic movements, as well as the invariance that must be observed during an exercise. The kinematic model is expressed in terms of a set of kinematic rules. During the actual execution of a rehabilitation exercise, the similarity between the measured motion data and the model is computed in terms of their distances, which are then used as inputs to a fuzzy interference system to derive the overall quality of the execution. The integrated approach provides both a detailed categorical assessment of the overall execution of the exercise and the degree of adherence to individual kinematic rules.

Słowa kluczowe

EN

realtime motion; rehabilitation exercises; inematic modeling; fuzzy inference

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 4
• Strony: 267--285
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Zhao W.

• Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, Ohio 44115

autor

Lun R.

• Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, Ohio 44115

autor

Espy D. D.

• School of Health Sciences Cleveland State University, Cleveland, Ohio 44115

autor

Reinthal M. A.

• School of Health Sciences Cleveland State University, Cleveland, Ohio 44115

Bibliografia

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