Over the past decade, the integration of electromyography (EMG) techniques with machine learning has significantly advanced prosthetic device control. Researchers have developed sophisticated deep learning classifiers for gesture recognition and created EMG controllers capable of simultaneous proportional control across multiple degrees of freedom. However, the increasing complexity of these machine learning models demands greater computational power, creating challenges for real-time deployment on embedded prosthetic controllers. Various optimization techniques - including hyperdimensional computing, pruning, and quantization - have demonstrated effectiveness in reducing computational requirements while preserving system performance. Concurrently, biomedical research has explored muscle and task synergies as methods to simplify inputs for machine learning models. This review examines synergy extraction in upper limb prosthetics research and identifies the need for standardized hardware specifications to facilitate proper validation and comparison of research outcomes. Furthermore, it explores how optimization techniques from Internet of Things (IoT) applications could enhance EMG controllers in biomedical settings. The analysis identifies sensor fusion and high-density EMG as particularly promising approaches for achieving robust, generalized control of upper limb prosthetics.

中文翻译：

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肌电假肢手：肌肉协同、机器学习和边缘计算综述

在过去十年中，肌电图 （EMG） 技术与机器学习的整合显著推动了假肢装置的控制。研究人员开发了用于手势识别的复杂深度学习分类器，并创建了能够在多个自由度上同时进行比例控制的 EMG 控制器。然而，这些机器学习模型的复杂性日益增加，需要更强大的计算能力，这为嵌入式修复控制器的实时部署带来了挑战。各种优化技术（包括超维计算、修剪和量化）已证明在降低计算要求同时保持系统性能方面的有效性。同时，生物医学研究探索了肌肉和任务协同作用作为简化机器学习模型输入的方法。本综述研究了上肢假肢研究中的协同提取，并确定了标准化硬件规范的需求，以促进研究结果的正确验证和比较。此外，它还探讨了物联网 （IoT） 应用的优化技术如何增强生物医学环境中的 EMG 控制器。该分析确定传感器融合和高密度 EMG 是实现上肢假肢稳健、广义控制的特别有前途的方法。