Zhang Xu, Qu Yonggang, Zhang Gang, Wang Zhiqiang, Chen Changbing, Xu Xin
Shendong Coal Group Co., Ltd., CHN Energy Group, Yulin 017209, China.
The Research Center for Mine Ventilation Safety and Occupational Health Protection of the State Energy Group, Yulin 017209, China.
Sensors (Basel). 2025 Apr 13;25(8):2448. doi: 10.3390/s25082448.
The global aging trend is becoming increasingly severe, and the demand for life assistance and medical rehabilitation for frail and disabled elderly people is growing. As the best solution for assisting limb movement, guiding limb rehabilitation, and enhancing limb strength, exoskeleton robots are becoming the focus of attention from all walks of life. This paper reviews the progress of research on upper limb exoskeleton robots, sEMG technology, and intention recognition technology. It analyzes the literature using keyword clustering analysis and comprehensively discusses the application of sEMG technology, deep learning methods, and machine learning methods in the process of human movement intention recognition by exoskeleton robots. It is proposed that the focus of current research is to find algorithms with strong adaptability and high classification accuracy. Finally, traditional machine learning and deep learning algorithms are discussed, and future research directions are proposed, such as using a deep learning algorithm based on multi-information fusion to fuse EEG signals, electromyographic signals, and basic reference signals. A model with stronger generalization ability is obtained after training, thereby improving the accuracy of human movement intention recognition based on sEMG technology, which provides important support for the realization of human-machine fusion-embodied intelligence of exoskeleton robots.
全球老龄化趋势日益严峻，体弱多病的老年人对生活协助和医疗康复的需求不断增长。作为协助肢体运动、指导肢体康复和增强肢体力量的最佳解决方案，外骨骼机器人正成为各界关注的焦点。本文综述了上肢外骨骼机器人、表面肌电（sEMG）技术和意图识别技术的研究进展。通过关键词聚类分析对文献进行分析，全面探讨了sEMG技术、深度学习方法和机器学习方法在外骨骼机器人人体运动意图识别过程中的应用。提出当前研究的重点是寻找适应性强、分类准确率高的算法。最后，对传统机器学习和深度学习算法进行了讨论，并提出了未来的研究方向，如利用基于多信息融合的深度学习算法融合脑电信号、肌电信号和基本参考信号。经过训练得到泛化能力更强的模型，从而提高基于sEMG技术的人体运动意图识别准确率，为外骨骼机器人实现人机融合具身智能提供重要支撑。
