Autonomous assistive exoskeleton control system integrating EEG/EMG intent decoding

Abstract

Mobility impairments caused by spinal cord injuries, stroke, and degenerative disorders limit independence for millions worldwide. This study presents a low-cost lower-limb exoskeleton that decodes user intention in real time using electroencephalographic (EEG) and electromyographic (EMG) signals. The system employs a PIC16F877A microcontroller executing adaptive impedance control and gait-phase recognition, with a Kalman filter combining inertial, torque, and bio-signal inputs to generate smooth hip–knee trajectories. Bench-top and hardware-in-the-loop simulations classified four locomotor commands – stand, sit, walk, stop – with 96 % accuracy and a maximum 85 ms latency. The lightweight 3.8 kg aluminium–carbon frame, powered by backdrivable BLDC motors, reproduced natural hip excursions of 15–35° at 1.2 s cadence. The results validate the feasibility of embedding edge AI for assistive gait restoration in resource-constrained clinical contexts.