ICT Convergence Rehabilitation Engineering Research Center, Soonchunhyang University, Asan 31538, Korea.
Department of ICT Convergence Rehabilitation Engineering, Soonchunhyang University, Asan 31538, Korea.
Sensors (Basel). 2020 Mar 14;20(6):1622. doi: 10.3390/s20061622.
Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.
痉挛是神经功能障碍患者中经常观察到的症状。周期性地测量其上下肢的痉挛运动，以评估物理康复的功能结果，并通过临床结果测量来量化，例如改良 Ashworth 量表 (MAS)。本研究提出了一种通过分析从患者患侧肘部采集的加速度和旋转属性，以及机器学习算法来确定肘部痉挛严重程度的方法，从而对痉挛运动的程度进行分类；这种方法可与 MAS 评分相媲美。我们使用装有惯性测量单元的可穿戴设备在被动伸展测试中从参与者身上收集惯性数据。机器学习算法——包括决策树、随机森林 (RF)、支持向量机、线性判别分析和多层感知机——在两种分段技术和特征集的组合中进行了评估。RF 的表现非常出色，最高可达到 95.4%的准确率。这项工作不仅成功地展示了可穿戴技术和机器学习如何用于生成具有临床意义的指标，而且还为康复患者提供了一个机会，可以监测痉挛的程度，即使在没有临床专业人员帮助的非医疗机构也是如此。
