A Human-in-the-Loop Comparison of Proportional Control and Adaptive Gravity Compensation in Elbow EMG-Driven Exoskeletons

Overview & Synopsis

Electromyography (EMG)-driven control enables wearable robots to assist users based on their neuromuscular activity. Upper-limb exoskeletons typically rely on proportional myoelectric control (PMC), which amplifies user effort but may compromise stability at high assistance levels. EMG-driven adaptive gravity compensation (AGC) offers a promising alternative, counterbalancing gravitational torques while continuously adapting to neuromuscular activity. However, these approaches have not been systematically compared in a human-in-the-loop setting. This study evaluates five control algorithms: two PMC strategies at different assistance levels, a purely integral AGC (AGC-PI), a kinematics- and acceleration-aware model-based (MB) AGC (AGC-AW), and a novel MB AGC approach based on Recursive Least Squares (AGC-RLS) for online payload estimation. Fourteen healthy participants performed target-reaching tasks using an active elbow exoskeleton under 0 kg and 2 kg loading conditions. Performance metrics included muscular activity, movement accuracy, task efficiency, and subjective workload. PMC exhibited a trade-off between physical effort and task performance: low assistance levels required greater physical effort under load, whereas high gains impaired task execution. AGC-PI reduced agonist activity but induced antagonist activation. In contrast, both model-based AGC approaches effectively compensated for external payloads, reducing physical and cognitive demands while improving movement accuracy and completion time.