Scheme Erik, Englehart Kevin
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada.
J Rehabil Res Dev. 2011;48(6):643-59. doi: 10.1682/jrrd.2010.09.0177.
Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.
利用肌电图（EMG）信号控制上肢假肢是一种重要的临床选择，它通过收缩残留肌肉为截肢者提供控制自主权。然而，人们控制假肢的灵活性进展甚微，尤其是在控制多个自由度时。在研究文献中，利用模式识别来区分多个自由度已显示出巨大的前景，但它尚未转变为临床上可行的选择。本文描述了肌电图模式识别中的相关问题和最佳实践，确定了部署稳健控制的主要挑战，并倡导可能在不久的将来产生影响的研究方向。
