alexa Metrics for Performance Evaluation of Patient Exercises during Physical Therapy
ISSN: 2329-9096

International Journal of Physical Medicine & Rehabilitation
Open Access

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Research Article

Metrics for Performance Evaluation of Patient Exercises during Physical Therapy

Aleksandar Vakanski1*, Jake M. Ferguson2 and Stephen Lee3

1Industrial Technology, University of Idaho, Idaho Falls, ID, USA

2Center for Modeling Complex Interactions, University of Idaho, Moscow, ID, USA

3Department of Statistical Science, University of Idaho, Moscow, ID, USA

*Corresponding Author:
Aleksandar Vakanski
Industrial Technology
University of Idaho
1776 Science Center Drive
TAB 309, Idaho Falls, ID, 83402, USA
Tel: 208-757-5422
E-mail: [email protected]

Received Date: April 01, 2017; Accepted Date: April 17, 2017; Published Date: April 20, 2017

Citation: Vakanski A, Ferguson JM, Lee S (2017) Metrics for Performance Evaluation of Patient Exercises during Physical Therapy. Int J Phys Med Rehabil 5:403. doi: 10.4172/2329-9096.1000403

Copyright: © 2017 Vakanski A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Objective: The article proposes a set of metrics for evaluation of patient performance in physical therapy exercises. Methods: Taxonomy is employed that classifies the metrics into quantitative and qualitative categories, based on the level of abstraction of the captured motion sequences. Further, the quantitative metrics are classified into modelless and model-based metrics, in reference to whether the evaluation employs the raw measurements of patient performed motions, or whether the evaluation is based on a mathematical model of the motions. The reviewed metrics include root-mean square distance, Kullback Leibler divergence, log-likelihood, heuristic consistency, Fugl-Meyer Assessment, and similar. Results: The metrics are evaluated for a set of five human motions captured with a Kinect sensor. Conclusion: The metrics can potentially be integrated into a system that employs machine learning for modelling and assessment of the consistency of patient performance in home-based therapy setting. Automated performance evaluation can overcome the inherent subjectivity in human performed therapy assessment, and it can increase the adherence to prescribed therapy plans, and reduce healthcare costs.

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