Abstract
BACKGROUND: Stroke is a leading cause of long-term adult disability, with approximately 80% of survivors experiencing upper extremity (UE) motor impairments. Conventional tools like the Fugl-Meyer Assessment (FMA) are widely used but limited by ordinal scales and subjective visual observation. While wearable robotics offer high-resolution data, their clinical translation is hindered by a lack of standardized protocols and limited interpretability for clinical decision-making. OBJECTIVE: This study aimed to develop an objective, standardized, and clinically interpretable method to quantify UE motor qualities by integrating wearable robotic technology with traditional clinical assessment tasks. METHODS: Ten healthy individuals and ten stroke survivors performed seven standardized tasks (six from the FMA-UE and one additional elbow task) while wearing the HARMONY exoskeleton. We developed a "trajectory pattern similarity score" based on the root mean square error between individual joint trajectories and normative averages. Additionally, kinematic synergy analysis was performed using non-negative matrix factorization to evaluate alterations in multi-joint coordination. RESULTS: The trajectory pattern similarity score showed a strong negative correlation with clinical FMA-UE scores (r = -0.93, p < 0.01) and demonstrated excellent test-retest reliability (ICC = 0.98). The number of identified kinematic synergies decreased significantly as motor impairment severity increased (r = 0.79, p < 0.01). Furthermore, kinematic synergy analysis provided a mechanistic explanation for reduced individual joint control. Post-stroke synergies could be explained through the merging (linear combinations of healthy kinematic patterns), preservation, or loss of healthy kinematic synergies, reflected as pathological joint coupling and loss of specific individual joint control. CONCLUSIONS: This study presents a novel, standardized assessment framework that integrates wearable robotic technology with conventional clinical tasks. By bridging the gap between objective robotic data and clinical interpretability, this approach would enable robust motor impairment assessment and intuitive phenotyping of motor characteristics to guide personalized rehabilitation strategies.