Sul Jung-Hoon, Piyathilaka Lasitha, Moratuwage Diluka, Dunu Arachchige Sanura, Jayawardena Amal, Kahandawa Gayan, Preethichandra D M G
School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia.
Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia.
Sensors (Basel). 2025 Jun 27;25(13):4004. doi: 10.3390/s25134004.
Electromyography (EMG) has emerged as a vital tool in the development of wearable robotic exoskeletons, enabling intuitive and responsive control by capturing neuromuscular signals. This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. Various EMG acquisition methods, including surface, intramuscular, and high-density surface EMG, are evaluated for their applicability in real-time control. The review addresses prevalent signal quality challenges, such as motion artifacts, power-line interference, and crosstalk. It also highlights both traditional filtering techniques and advanced methods, such as wavelet transforms, empirical mode decomposition, and adaptive filtering. Feature extraction techniques are explored to support pattern recognition and motion classification. Machine learning approaches are examined for their roles in pattern recognition-based and hybrid control architectures. This article emphasizes muscle synergy analysis and adaptive control algorithms to enhance personalization and fatigue compensation, followed by the benefits of multimodal sensing and edge computing in addressing the limitations of EMG-only systems. By focusing on EMG-driven strategies through signal processing, machine learning, and sensor fusion innovations, this review bridges gaps in human-machine interaction, offering insights into improving the precision, adaptability, and robustness of next generation exoskeletons.
肌电图（EMG）已成为可穿戴机器人外骨骼发展中的一项重要工具，通过捕捉神经肌肉信号实现直观且响应灵敏的控制。本综述对针对外骨骼应用量身定制的肌电信号处理流程进行了全面分析，涵盖信号采集、噪声抑制、数据预处理、特征提取和控制策略。评估了包括表面肌电图、肌内肌电图和高密度表面肌电图在内的各种肌电采集方法在实时控制中的适用性。该综述探讨了常见的信号质量挑战，如运动伪迹、电力线干扰和串扰。它还重点介绍了传统滤波技术以及小波变换、经验模态分解和自适应滤波等先进方法。探索了特征提取技术以支持模式识别和运动分类。研究了机器学习方法在基于模式识别的控制架构和混合控制架构中的作用。本文强调肌肉协同分析和自适应控制算法，以增强个性化和疲劳补偿，随后阐述了多模态传感和边缘计算在解决仅使用肌电系统局限性方面的优势。通过专注于通过信号处理、机器学习和传感器融合创新实现的肌电驱动策略，本综述弥合了人机交互中的差距，为提高下一代外骨骼的精度、适应性和鲁棒性提供了见解。
