Functional assessment is an essential part of rehabilitation protocols after stroke. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. In order to objectively quantify the upper-limb motor impairments in patients with post-stroke hemiparesis, this study proposes a novel assessment approach based on motor synergy quantification and multi-modality fusion. Fifteen post-stroke hemiparetic patients and fifteen age-matched healthy persons participated in this study. During different goal-directed tasks, kinematic data and surface electromyography(sEMG) signals were synchronously collected from these participants, and then motor features extracted from each modal data could be fed into the respective local classifiers. In addition, kinematic synergies and muscle synergies were quantified by principal component analysis (PCA) and ${k}$ weighted angular similarity ( ${k}$ WAS) algorithm to provide in-depth analysis of the coactivated features responsible for observable movement impairments. By integrating the outputs of local classifiers and the quantification results of motor synergies, ensemble classifiers can be created to generate quantitative assessment for different modalities separately. In order to further exploit the complementarity between the evaluation results at kinematic and muscular levels, a multi-modal fusion scheme was developed to comprehensively analyze the upper-limb motor function and generate a probability-based function score. Under the proposed assessment framework, three types of machine learning methods were employed to search the optimal performance of each classifier. Experimental results demonstrated that the classification accuracy was respectively improved by 4.86% and 2.78% when the analysis of kinematic and muscle synergies was embedded in the assessment system, and could be further enhanced to 96.06% by fusing the characteristics derived from different modalities. Furthermore, the assessment result of multi-modality fusion framework exhibited a significant correlation with the score of standard clinical tests ( ${R = - {0.87},\;{P} = {1.98}{e} - {5}}$ ). These promising results show the feasibility of applying the proposed method to clinical assessments for post-stroke hemiparetic patients.

中文翻译：

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基于运动协同分析和多模态融合的中风康复后上肢运动功能定量评估

功能评估是中风后康复方案的重要组成部分。按照惯例，评估过程在很大程度上依赖于临床经验，并且缺乏定量分析。为了客观地量化卒中后偏瘫患者的上肢运动障碍，本研究提出了一种基于运动协同量化和多模态融合的新型评估方法。15名中风后偏瘫患者和15名年龄匹配的健康人参加了这项研究。在不同的目标任务中，从这些参与者同步收集运动数据和表面肌电图（sEMG）信号，然后将从每个模态数据中提取的运动特征输入各自的局部分类器中。此外， $ {k} $ 加权角相似度（ $ {k} $ WAS）算法可提供对导致可观察到的运动障碍的共激活特征的深入分析。通过整合本地分类器的输出和运动协同作用的量化结果，可以创建集成分类器以分别生成针对不同模式的定量评估。为了进一步利用运动学和肌肉水平评估结果之间的互补性，开发了一种多模式融合方案，以全面分析上肢运动功能并生成基于概率的功能评分。在提出的评估框架下，采用了三种类型的机器学习方法来搜索每个分类器的最佳性能。实验结果表明分类精度分别提高了4.86％和2。当运动和肌肉协同作用的分析被嵌入评估系统中时，这一比例为78％，并且可以通过融合来自不同方式的特征而进一步提高到96.06％。此外，多模态融合框架的评估结果与标准临床测试的得分有显着相关性（ $ {R =-{0.87}，\; {P} = {1.98} {e}-{5}} $ ）。这些有希望的结果表明，将所提出的方法应用于中风后偏瘫患者的临床评估的可行性。