School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Biomedical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt.
Expert Rev Med Devices. 2024 Aug;21(8):709-726. doi: 10.1080/17434440.2024.2376699. Epub 2024 Jul 12.
Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research.
This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors.
The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.
将表面肌电图生物反馈（EMG-BF）设备的应用扩展到不同的治疗环境中，突出了可视化肌肉活动在康复过程中的作用逐渐演变。本综述结合循证研究评估了其概念、用途和趋势。
本综述剖析了 EMG-BF 系统的解剖结构，强调其与机器学习（ML）和深度学习（DL）范式的变革性融合。将复杂的 DL 架构应用于高密度 EMG 数据解释、优化 DL 模型性能的技术，以及将 EMG 与脑电图（EEG）信号融合等进展，都被用于加强康复中的生物力学分析。文献调查还根据商业领域的见解，基于功能和临床用途对 EMG-BF 设备进行了分类。
EMG-BF 的当前格局正在迅速发展，主要得益于人工智能（AI）的创新。将 ML 和 DL 纳入 EMG-BF 系统提高了其准确性、可靠性和范围，标志着患者护理的飞跃。尽管模型可解释性和信号噪声方面存在挑战，但正在进行的研究有望解决这些复杂性，改进生物反馈模式。人工智能的整合不仅预测了患者特定的康复时间线，还定制了治疗干预措施，为康复和情感检测中的个性化医疗新时代铺平了道路。
