Systematic Advances in the Field of Assistive and Rehabilitation of Upper Limb Exoskeletons Based on Active Intention Perception and Control Strategies

Abstract

Upper limb dysfunction caused by stroke and spinal cord injury has become a heavy social burden. Effective active rehabilitation training is crucial for restoring hand function. The focus of this article is on analysis how upper limb rehabilitation exoskeletons sense patients’ motor intentions and, based on this, discusses various sensory processing methods and control strategies. For patients at different stages of rehabilitation, achieving precise recognition and control of motor intentions is crucial. Research has found that for early- stage patients who have completely lost motor function, force- topology coupling control based on electroencephalogram (EEG) signals overcomes the limitations of peripheral nerve signal loss. For intermediate- stage patients with some electromyographic signals, the impedance control or torque prediction model combining surface electromyography (sEMG) and neural networks was successfully used to achieve an interaction that combines sensitivity and compliance. For patients in later stages, magnetic field orientation control (FOC) or impedance control, which integrates sensors and motion models, enables end-effector trajectory tracking and compliant assistance with high linearity, high robustness, and precise physical interaction transparency.