Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment. Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl–Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation. Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r>0.9, P0.9, P<0.001). Significant correlations (P<0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r=0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention. Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes.

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基于表面肌电图 (sEMG) 的慢性中风临床评估的数据驱动研究

背景：基于表面肌电图（sEMG）的机器人辅助康复系统已被用于慢性中风幸存者恢复上肢运动功能。然而，机器人辅助干预过程中康复效果的评估依赖于传统的人工评估。本研究旨在开发一种用于自动评估的新型 sEMG 数据驱动模型。方法：构建基于三层反向传播神经网络（BPNN）的数据驱动模型，将表面肌电数据映射到两个广泛使用的临床量表，即 Fugl-Meyer 评估（FMA）和改良 Ashworth 量表（MAS） 。29 名中风参与者被招募参加为期 20 次的 sEMG 驱动机器人辅助上肢康复训练，其中包括伸手和缩手任务。干预前后记录偏瘫上肢四块肌肉的表面肌电信号，即肱二头肌（BIC）、肱三头肌（TRI）、指屈肌（FD）和指伸肌（ED）。同时，由盲法评估员手动测量相应的 FMA 和 MAS 临床量表。采用包括平均绝对值 (MAV)、过零 (ZC)、斜率符号变化 (SSC)、均方根 (RMS) 和波长 (WL) 在内的 sEMG 特征作为数据驱动模型的输入。通过皮尔逊相关性将数据驱动模型映射的临床评分与手动评分进行比较。结果：建立了隐藏层15个节点和sEMG特征，即MAV、ZC、SSC和RMS作为模型的输入的BPNN，实现了具有显着相关性的最佳映射性能（r>0.9， P0.9，P<0.001）。MAS 的映射评分和手动评分之间存在显着相关性（P < 0.001），干预后手指处的相关系数 r = 0.91，手腕处的相关系数 r = 0.88，肘部的相关系数 r = 0.91。结论：成功开发了 sEMG 数据驱动的 BPNN 模型。一旦经过大样本量验证，它可以评估慢性中风的上肢运动功能，并在中风后康复的自动评估中具有潜在的应用。