发表机构
Polytechnique Montréal（蒙特利尔综合理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究上肢运动障碍者用sEMG控制辅助机器人手臂的问题，核心方法是基于一维卷积神经网络，主要贡献是实现高分类性能、稳定实时行为，证明sEMG遥操作可行性，强调整合多技术的重要性，为后续研究指明方向。
AI 中文摘要
上肢运动障碍显著降低日常活动自主性，辅助机器人手臂是有前景的解决方案。本文提出基于表面肌电（sEMG）的辅助机器人手臂实时遥操作接口。系统包括四通道sEMG采集、信号预处理、滑动窗口分割及一维卷积神经网络分类。研究了多种实时策略，在仿真和真实机器人平台实现并评估了完整流程。基于CNN的方法分类性能高，系统实时行为稳定，证明了基于sEMG遥操作辅助机器人的可行性，强调统一实时框架中整合信号处理、深度学习和控制策略的重要性，未来可探索混合控制方法。
英文摘要
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.
