Metrics for Performance Evaluation of Patient Exercises during Physical Therapy.

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 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. 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.

• Publication year

  2017

• Venue

  International Journal of Physical Medicine & Rehabilitation

• Publication date

  2017-04-20

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.4172/2329-9096.1000403PMID 28752104PMCID PMC5526359
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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