Metrics for Performance Evaluation of Patient Exercises during Physical Therapy.

Jake M Ferguson,Stephen Lee,Aleksandar Vakanski

doi:10.4172/2329-9096.1000403

Jake M Ferguson, Stephen Lee + Show 1 more

Open Access

https://doi.org/10.4172/2329-9096.1000403

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Abstract

The article proposes a set of metrics for evaluation of patient performance in physical therapy exercises. 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 model-less 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. The metrics are evaluated for a set of five human motions captured with a Kinect sensor. 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.

Highlights

Functional recovery from neuromotor disabilities, various surgical procedures, or musculoskeletal trauma is strongly dependent on patient participation in a physical therapy program
Evaluation Results The following metrics were evaluated for the five actions: rot-mean square distance, loglikelihood, Kullback Leibler (KL) divergence, heuristic consistency, and prediction intervals
The article presents a survey on the current literature on the metrics for evaluation of patient performance in physical therapy

Summary

Introduction

Functional recovery from neuromotor disabilities, various surgical procedures, or musculoskeletal trauma is strongly dependent on patient participation in a physical therapy program. Application of machine learning algorithms for monitoring and evaluation of patient compliance with a prescribed physical therapy program can improve the adherence rates, reduce the required time for functional recovery, and reduce treatment cost. Such methodology is predicated upon the provision of: (i) efficient mathematical models for representation of bodily movements undertaken during physical therapy exercises [14], and (ii) efficient metrics for quantifying the patient executed motions and collating the performance to the prescribed motions by the PT.

Results

Conclusion