Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network

2026-07-14

AI Summary

This study addresses the limited autonomy experienced by individuals with upper-limb motor impairments during daily tasks by proposing a surface electromyography (sEMG)-based real-time teleoperation control framework. The system employs four-channel sEMG signals segmented via sliding windows and integrates a one-dimensional convolutional neural network with a hybrid approach combining threshold-triggered detection and a two-stage classification strategy to enable end-to-end gesture recognition and robotic arm control. Designed within a unified architecture, the method jointly optimizes accuracy and latency, achieving stable and reliable discrete gesture control on both simulated and physical robotic platforms. Experimental results demonstrate an average classification accuracy exceeding 90% with a latency of approximately 0.32 seconds.

Abstract

Motor impairments affecting the upper limb significantly reduce autonomy in daily activities, particularly for tasks involving object manipulation. Assistive robotic arms offer a promising solution, provided they can be controlled in an intuitive, reliable, and responsive manner. Among human--machine interface approaches, surface electromyography (sEMG) enables non-invasive access to muscle activity and thus to the user's motor intentions. This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm. The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN). Several real-time strategies are investigated, including threshold-based onset detection, a two-stage classification approach (rest vs movement followed by gesture recognition), and a single classifier handling both rest and five gestures. The complete pipeline is implemented and evaluated both in simulation and on a real robotic platform. The CNN-based approach achieves high classification performance, with a test accuracy above 90\% and strong generalization on experimentally acquired signals. The system exhibits stable real-time behavior, with an average latency of approximately 0.32 s consistent with the chosen windowing strategy, and the robot can be controlled reliably using discrete gestures, producing coherent and smooth movements in both simulated and real environments. These findings demonstrate the feasibility of sEMG-based telecontrol for assistive robotics and highlight the importance of integrating signal processing, deep learning, and control strategies within a unified real-time framework. Future work may explore hybrid control approaches combining sEMG with additional sensing modalities to further improve robustness and usability.

Problem

• assistive robotic arm
• real-time control
• sEMG-based telecontrol
• motor impairments
• human-machine interface

Innovation

• sEMG
• 1D CNN
• real-time telecontrol
• assistive robotics
• gesture classification