Rehabilitation as a Game: ‘Assist as Needed’ Reaching Movements as Nash Equilibria

In stroke rehabilitation, tailoring assistance to individual needs is crucial for more effective training. This study investigates the control architecture of an artificial partner (AP) inspired by game theory. The AP modulates assistance in a planar reaching task using adaptive control strategies. We compare AP performance in “lazy” and “generous” conditions. Results show that the AP adjusts its assistance effectively based on game-theoretic principles. This approach shows promise for enhancing robot-assisted rehabilitation through personalized therapy. Future research will explore the long-term effects of these policies and refine the AP’s sensory system and state observer for improved precision.