Autonomous assistive exoskeleton control system integrating EEG/EMG intent decoding
Bibliographic entry
Autonomous assistive exoskeleton control system integrating EEG/EMG intent decoding = / T. Sean, D. Muchenje, D. T. Simango, B. S. Makusha // Приборостроение-2025 : материалы 18-й Международной научно-технической конференции, 13–15 ноября 2025 года Минск, Республика Беларусь / редкол.: А. И. Свистун (пред.), О. К. Гусев, Р. И. Воробей [и др.]. – Минск : БНТУ, 2025. – С. 432-433.
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.