Relearning Upper Limb Proprioception After Stroke Through Robotic Therapy: A Feasibility Analysis

Abstract

Background: Motor learning can occur through active reaching with the arm hidden from view, leading to improvements in somatosensory acuity and modulation of functional connectivity in sensorimotor and reward networks. In this proof-of-principle study, we assess if the same paradigm benefits stroke survivors using a compact end-effector robot with integrated gaming elements. Methods: Nine community-dwelling chronic hemiplegic stroke survivors with persistent somatosensory deficits participated in 15 training sessions, each lasting 1 h. Every session comprised a robotic-based joint approximation block, followed by 240 repetitions of training using a forward-reaching task with the affected forearm covered from view. During movement, the robot provided haptic guidance along the movement path as enhanced sensory cues. Augmented reward feedback was given following every successful movement as positive reinforcement. Baseline, post-intervention, and 1-month follow-up assessments were conducted, with the latter two sessions occurring after the final training day. Results: Training led to reliable improvements in endpoint accuracy, faster completion times, and smoother movements. Acceptability and feasibility analyses were performed to understand the viability of the intervention. Significant improvement was observed mainly in robotic-based sensory outcomes up to a month post training, suggesting that training effects were predominantly sensory, rather than motor. Conclusions: The study outcomes provide preliminary evidence supporting the feasibility of this intervention for future adoption in neurorehabilitation.