Upper-limb functional assessment after stroke using mirror contraction: A pilot study

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.