Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network
This research developed a real-time system to control an assistive robotic arm using muscle activity detected via surface electromyography (sEMG). A 1D Convolutional Neural Network was used to classify six hand gestures from sEMG signals. The system successfully translated these muscle movements into discrete robot commands, demonstrating feasible, responsive control for individuals with upper limb motor impairments.