Aleksandar Vakanski1, Jake M Ferguson2, Stephen Lee3. 1. Industrial Technology, University of Idaho, Idaho Falls, ID, USA. 2. Center for Modeling Complex Interactions, University of Idaho, Moscow, ID, USA. 3. Department of Statistical Science, University of Idaho, Moscow, ID, USA.
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 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.
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.
Authors: Azad Shademan; Ryan S Decker; Justin D Opfermann; Simon Leonard; Axel Krieger; Peter C W Kim Journal: Sci Transl Med Date: 2016-05-04 Impact factor: 17.956