A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study.
A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study.
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DOI：
10.3390/s150820097
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发表时间：
2015-08-14
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影响因子：
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通讯作者：
Son SH
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文献类型：
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作者：
Otten P;Kim J;Son SH
标准上肢运动功能损伤评估，例如 Fugl-Meyer 评估 (FMA)，是神经系统疾病康复的一个重要方面。临床医生对患者进行这些评估通常需要很长时间（FMA 大约需要 30 分钟），这在临床环境中是一个沉重的负担。在本文中，我们提出了一个自动化上肢运动评估的框架，该框架使用低成本传感器来收集运动数据。然后通过机器学习算法处理传感器数据，以确定患者上肢功能的评分。为了证明所提出方法的可行性，我们基于所提出的框架实现了一个系统，该系统可以自动化大部分 FMA。我们的实验表明，该系统提供与临床医生评分相似的 FMA 评分，并将评估每位患者所花费的时间减少了 82%。此外，所提出的框架可用于实施定制测试或其他现有标准评估方法中指定的测试。
Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient’s upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA. Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.