Abstract Hand impairments caused by stroke or neuromuscular disorders severely restrict daily activities, underscoring the need for effective, home-applicable rehabilitation systems. This study presents HandMaid, a wearable hand exoskeleton that restores motion through electromyography (EMG) -based intention detection and an active backdrive safety mechanism. The system integrates a 3D-printed rigid exoskeleton, linear actuators, and a neural network interface that is retrained before each use to compensate for variations in electrode placement, skin resistance, temperature, and humidity. This adaptive training procedure ensures reliable signal interpretation under different environmental and physiological conditions. Kinematic and experimental analyses demonstrate that the device covers approximately 81% of the anatomical range of motion (MCP ≈ 50 ^ ∘, PIP ≈ 95 ^ ∘, DIP ≈ 90 ^ ∘), while maintaining a mean classification accuracy of 98. 1 ± 1. 6% and an average response latency of ≈ 1. 05 s. The threshold-based active backdrive logic provides immediate reversal in case of user resistance or discomfort, ensuring both safety and comfort during use. Weighing around 400 g and with a total production cost below 350, HandMaid offers a portable, low-cost, and anatomically consistent rehabilitation solution. The results confirm that the system enables user-safe, adaptive, and cost-effective robotic rehabilitation suitable for both clinical and home environments, while its retrainable EMG model ensures robustness against session-to-session variability.
Güçlü et al. (Fri,) studied this question.