The clinical assessment after stroke depends on the rating scale, usually lack of quantitative feedback such as biomedical signal captured from stroke patients. This study attempts to develop a unified assessment framework for persons after stroke via surface electromyography (sEMG) bias from bilateral limbs, based on four types of selected movements, namely forward lift arm, lateral lift arm, forearm internal/external rotation, forearm pronation/supination. Eleven healthy subjects and six stroke patients are recruited to participate in the experiment to perform the bilateral-mirrored paradigm with six channels of sEMG signals recorded from each of their arms. The linear discriminant analysis (LDA), random forest algorithm (RF) and support vector machine (SVM) are adopted, trained and used for stroke patients qualitative recognition. The bilateral bias diagnosis algorithm (BBDA) is developed to evaluate the stroke severity quantitatively based on the similarity index (SI) of the sEMG. The results reveal that: (1) the sEMG feature bias of bilateral arms for stroke patients is different from that of healthy people; (2) the RF and SVM demonstrate a better performance with an average recognition accuracy of 0.92 ± 0.12 and 0.93 ± 0.12 than LDA (0.84 ± 0.20) in distinguishing stroke patients from healthy subjects; (3) there is a strong positive correlation between SI and the Fugl-Meyer score (r = 0.93). These research findings indicate that the dominant qualitative assessment after stroke could be complementary by its counterpart quantitative solutions, and stroke rehabilitation could be automated with less involvement of professional therapists.

中风后的临床评估取决于评分量表，通常缺乏定量反馈，例如从中风患者身上捕获的生物医学信号。本研究试图通过双侧肢体的表面肌电图 (sEMG) 偏差为中风后的人制定统一的评估框架，基于四种选定的运动类型，即前举臂、侧举臂、前臂内/外旋、前臂旋前/旋后。招募了 11 名健康受试者和 6 名中风患者参与该实验，以执行双边镜像范例，并从他们的每只手臂记录六个通道的 sEMG 信号。采用线性判别分析（LDA）、随机森林算法（RF）和支持向量机（SVM），对中风患者进行定性识别。双边偏差诊断算法 (BBDA) 被开发用于基于 sEMG 的相似性指数 (SI) 定量评估中风严重程度。结果表明：（1）脑卒中患者双臂sEMG特征偏向与健康人不同；(2) RF和SVM在区分中风患者和健康受试者方面表现出比LDA(0.84±0.20)更好的性能，平均识别准确率为0.92±0.12和0.93±0.12；(3) SI 与 Fugl-Meyer 评分之间存在很强的正相关关系（(2) RF和SVM在区分中风患者和健康受试者方面表现出比LDA(0.84±0.20)更好的性能，平均识别准确率为0.92±0.12和0.93±0.12；(3) SI 与 Fugl-Meyer 评分之间存在很强的正相关关系（(2) RF和SVM在区分中风患者和健康受试者方面表现出比LDA(0.84±0.20)更好的性能，平均识别准确率为0.92±0.12和0.93±0.12；(3) SI 与 Fugl-Meyer 评分之间存在很强的正相关关系（r  = 0.93）。这些研究结果表明，中风后主要的定性评估可以通过其对应的定量解决方案进行补充，并且中风康复可以在专业治疗师参与较少的情况下实现自动化。