Game based, force assistive portable manipulandum system for upper extremity recovery: design, control, and performance verification

Abstract

Upper extremity motor impairments remain a leading cause of long-term disability following stroke. Although Repetitive Task Practice (RTP) enhances motor recovery, traditional therapist-guided rehabilitation is constrained by high cost, limited accessibility, and insufficient training intensity. Available robotic and sensor-based systems, while clinically effective, frequently lack the affordability and adaptive haptic assistance necessary for widespread home-based use. This research presents the design, development, and verification of a low cost, portable Rehabilitation Manipulandum Device (RMD) engineered to bridge the gap between clinical robotics and accessible game-based therapy. The electromechanical architecture centers on a direct drive transmission with a modular handle interface targeting specific muscle groups of the hand, wrist, and forearm. A custom STM32F4 printed circuit board provides the real time processing required for closed-loop haptic control. The control architecture implements eight haptic modes: passive, resistive, and proportional derivative assistive configurations, delivered through bidirectional closed loop communication with an open source RTP gaming platform. Three supporting desktop applications (a Rehab Prescription App, a Result Analyzer App, and a Statistical Analysis App) complete the software package, enabling therapy configuration and multisession performance evaluation without requiring specialized technical expertise. System performance was evaluated through a two-phase engineering validation study. Phase 1 applied Statistical Process Control to eleven consecutive sessions under a fixed Assistive Constant Force configuration with a controlled mechanical load. Results confirmed stable and repeatable device output, with response time coefficients of variation ranging from 6% to 11% and no detectable drift or fatigue trend. Phase 2 engaged six healthy adult participants across all five haptic modes, demonstrating that mode selection significantly differentiated game-based performance outcomes and established the measurement sensitivity of the RMD across therapeutic conditions. The findings position the device as a validated engineering platform ready for future clinical trials involving stroke survivors.