Markerless Motion Capture in Routine Clinical Upper Limb Assessments: Validity and Insights Beyond Ordinal Scoring

The Action Research Arm Test (ARAT) is a widely-used upper limb outcome measure in neurorehabilitation, but its ordinal scoring is subjective and suffers from limited sensitivity and specificity. We evaluated whether artificial-intelligence (AI)-based markerless motion capture (MMC), embedded into ARAT assessments during clinical routine, accurately reconstructs upper limb movement and yields valid, objective kinematic metrics carrying clinically meaningful information beyond the ordinal score. Across 47 sessions from 20 mixed-neurological patients (1,174 ARAT tasks), biomechanical reconstruction was accurate and robust across impairment levels, and kinematic metrics showed the discrimination pattern expected of a construct-valid measure. In longitudinal case studies, the metrics added the specificity and sensitivity the ordinal score lacks: a domain decomposition exposed patient-specific recovery profiles underlying equal ARAT gains (specificity), and kinematic improvement continued to be detected after the ARAT had saturated (sensitivity). MMC in clinical routine can thus provide valid, objective, sensitive, and specific kinematic measurement complementing ordinal scoring.