A Data-Driven Investigation on Surface Electromyography Based Clinical Assessment in Chronic Stroke

• Published in: Frontiers in neurorobotics Vol. 15; p. 648855
• Main Authors: Ye, Fuqiang, Yang, Bibo, Nam, Chingyi, Xie, Yunong, Chen, Fei, Hu, Xiaoling
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 15.07.2021; Frontiers Media S.A
• Subjects: Automation; chronic stroke; clinical assessment; data-driven model; Elbow; Electromyography; Intervention; Investigations; Kinematics; Muscles; Neural networks; Neuroscience; Regression analysis; Rehabilitation; Robotics; Robots; Spasticity; Stroke; surface electromyography; Therapists; upper limb; Wrist; Hong Kong
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2021.648855

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, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention ( r > 0.9, 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. Clinical Trial Registration: www.ClinicalTrials.gov , identifier: NCT02117089.