Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada.
Division of Hand Therapy, Hand and Upper Limb Centre, St. Joseph's Health Care, London, ON N5V 3A1, Canada.
Sensors (Basel). 2019 Jul 27;19(15):3309. doi: 10.3390/s19153309.
Wearable robotic braces have the potential to improve rehabilitative therapies for patients suffering from musculoskeletal (MSK) conditions. Ideally, a quantitative assessment of health would be incorporated into rehabilitative devices to monitor patient recovery. The purpose of this work is to develop a model to distinguish between the healthy and injured arms of elbow trauma patients based on electromyography (EMG) data. Surface EMG recordings were collected from the healthy and injured limbs of 30 elbow trauma patients while performing 10 upper-limb motions. Forty-two features and five feature sets were extracted from the data. Feature selection was performed to improve the class separation and to reduce the computational complexity of the feature sets. The following classifiers were tested: linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF). The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Maximum fractal length (MFL), myopulse percentage rate (MYOP), power spectrum ratio (PSR) and spike shape analysis features were identified as the best features for classifying elbow muscle health. A majority vote of the LDA classification models provided a cross-validation accuracy of 82.1%. The work described in this paper indicates that it is possible to discern between healthy and injured limbs of patients with MSK elbow injuries. Further assessment and optimization could improve the consistency and accuracy of the classification models. This work is the first of its kind to identify EMG metrics for muscle health assessment by wearable rehabilitative devices.
可穿戴式机器人矫具有可能改善患有肌肉骨骼 (MSK) 疾病的患者的康复治疗。理想情况下，会将健康的定量评估纳入康复设备中，以监测患者的康复情况。这项工作的目的是开发一种模型，根据肌电图 (EMG) 数据来区分肘部创伤患者的健康手臂和受伤手臂。从 30 名肘部创伤患者的健康和受伤肢体中采集了表面 EMG 记录，同时进行了 10 项上肢运动。从数据中提取了 42 个特征和 5 个特征集。进行特征选择以改善分类分离并降低特征集的计算复杂性。测试了以下分类器：线性判别分析 (LDA)、支持向量机 (SVM) 和随机森林 (RF)。这些分类器用于区分两种健康水平：健康和受伤（基线准确率为 50%）。最大分形长度 (MFL)、肌电脉冲百分比率 (MYOP)、功率谱比 (PSR) 和尖峰形状分析特征被确定为分类肘部肌肉健康的最佳特征。LDA 分类模型的多数票提供了 82.1%的交叉验证准确率。本文所述的工作表明，有可能区分 MSK 肘部受伤患者的健康和受伤肢体。进一步的评估和优化可以提高分类模型的一致性和准确性。这项工作是首例通过可穿戴康复设备识别肌电图指标进行肌肉健康评估的工作。
