Quantitative Assessment of Upper Limb Multi-Modal Feature Fusion Under Task-Oriented Movement

Assessment of motor function is an important component of a post-stroke rehabilitation program. The traditional assessment process mainly relies on clinical experience and lacks quantitative analysis. To objectively assess the upper limb motor status of post-stroke hemiplegic patients, this study proposes a novel assessment method based on multi-modal feature fusion of the upper limb for task-oriented movement. Features are extracted from each modal data and input into the corresponding base classifiers. The kinematic and muscle synergy are quantified by singular value decomposition (SVD) and similarity metric index, and the results are integrated to construct an aggregated classifier for in-depth quantitative assessment of different movement modalities. To exploit the complementary nature of kinematic and muscular level assessment results, a multi-modal feature fusion scheme is proposed and a probability-based functional scoring mechanism is generated to comprehensively analyze upper extremity motor function. Experimental results show that integrating synergy analyses into the assessment system improves the classification accuracy by 2.39% and 2.31%, respectively, which can be further improved to 90.75% by fusing the features extracted from different modalities. Furthermore, the assessment results of multi-modal fusion framework are significantly correlated with standard clinical trial scores ( $r$ =-0.81, $p$ =0.0147). These promising results suggest that it is feasible to apply the proposed method to the clinical assessment of hemiplegic patients after stroke.