物理治疗过程中患者锻炼效果评估指标。

Aleksandar Vakanski, Jake M Ferguson, Stephen Lee
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引用次数: 0

摘要

目的本文提出了一套用于评估物理治疗练习中患者表现的指标:方法:采用分类法,根据捕捉到的运动序列的抽象程度,将指标分为定量和定性两类。此外,定量指标还分为无模型指标和基于模型的指标,即评估是采用患者运动的原始测量数据,还是基于运动的数学模型。所审查的指标包括均方根距离、库尔贝克-莱布勒发散、对数似然、启发式一致性、福格尔-迈耶评估等:结果:针对 Kinect 传感器捕捉到的一组五个人体动作对这些指标进行了评估:结论:这些指标有可能被整合到一个系统中,该系统采用机器学习方法对患者在家庭治疗环境中的表现进行建模和一致性评估。自动性能评估可以克服人类进行治疗评估时固有的主观性,并能提高对规定治疗计划的依从性,降低医疗成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Metrics for Performance Evaluation of Patient Exercises during Physical Therapy.

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.

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