Quantitative Assessment via Multi-Domain Fusion of Muscle Synergy Associated with Upper-Limb Motor Function for Stroke Rehabilitation

Quantitative assessment of upper limb motor function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the most frequently used assessments are based on clinical scales or kinematic metrics, which rely on subjective scores or may be masked at the kinematic level by compensatory strategies. Recently, muscle synergies which encodes the simplified neuromuscular control strategy deployed by the central nervous system have been gradually used to assess post-stroke impairment. In general, muscle synergies are decomposed into two components: synergy vectors and synergy activation. Synergy vectors represent the relative weighting of each muscle within each synergy, that is muscle coordination; synergy activation represents the recruitment of the muscle synergy over time, that is muscle activation strength. Both the characteristics of synergy vectors and synergy activation are crucial for adequately assessing patients' motor function. Therefore, we integrate the spatial domain and temporal domain features extracted from synergy vectors and synergy activation for constructing a multi-domain assessment system based on Random Forest classifier, which may provide great qualitative classification accuracy. Furthermore, a novel functional score is generated from the probabilities belonging to the pathological group. Finally, we conduct a study with ten healthy subjects and ten post-stroke patients to verify the effectiveness of the proposed method. The experimental results show that the classification accuracy was enhanced to 98.56% by fusing the characteristics derived from different domains, which was higher than that based on spatial domain (94.90%) and temporal domain (91.08%), respectively. Furthermore, the assessment score generated by multi-domain fusion framework exhibited a significant correlation with the clinical score. These promising results show the potential of applying the proposed method to clinical assessments for post-stroke patients.