用轻量注意力模型实时识别下肢动作，精度超85%。
Attention-Based Convolutional Neural Network Model for Human Lower Limb Activity Recognition using sEMG
- 设计轻量级注意力网络，直接处理原始多通道肌电信号
- 在三个动作类上测试准确率达85.38%，参数仅6万2千余
- 无需复杂预处理，适合嵌入式实时人机交互系统
利用表面肌电（sEMG）信号精确分类下肢运动，在辅助机器人和康复系统中至关重要。本研究提出一种轻量级注意力深度神经网络（DNN），基于公开的BASAN数据集，直接处理多通道sEMG数据，实现实时动作分类。该模型仅含62,876个参数，无需计算成本高的预处理步骤，适用于实时部署。采用留一法验证策略以确保跨被试泛化能力，评估了三种运动类别：行走、屈膝站立和伸膝坐姿。网络在验证集上达到86.74%准确率，测试集达85.38%，在真实条件下表现优异。与文献中现有模型对比显示，该方法在计算效率与性能上更具优势，尤其适合对计算开销和实时响应要求高的场景。结果表明，该模型是集成到人机交互系统高层控制器中的有力候选。
原文摘要 · Abstract (English)
Accurate classification of lower limb movements using surface electromyography (sEMG) signals plays a crucial role in assistive robotics and rehabilitation systems. In this study, we present a lightweight attention-based deep neural network (DNN) for real-time movement classification using multi-channel sEMG data from the publicly available BASAN dataset. The proposed model consists of only 62,876 parameters and is designed without the need for computationally expensive preprocessing, making it suitable for real-time deployment. We employed a leave-oneout validation strategy to ensure generalizability across subjects, and evaluated the model on three movement classes: walking, standing with knee flexion, and sitting with knee extension. The network achieved 86.74% accuracy on the validation set and 85.38% on the test set, demonstrating strong classification performance under realistic conditions. Comparative analysis with existing models in the literature highlights the efficiency and effectiveness of our approach, especially in scenarios where computational cost and real-time response are critical. The results indicate that the proposed model is a promising candidate for integration into upper-level controllers in human-robot interaction systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。