Englehart Kevin, Hudgins Bernard
Department of Biomedical Engineering, University of New Brunswick, 25 Dineen Drive, Fredericton, NB E3B5A3, Canada.
IEEE Trans Biomed Eng. 2003 Jul;50(7):848-54. doi: 10.1109/TBME.2003.813539.
This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.
本文介绍了一项正在进行的关于利用肌电信号（MES）对上肢假肢进行灵巧自然控制的研究。文中描述的方案使用模式识别来处理四个通道的肌电信号，任务是区分多类肢体运动。该方法不需要对肌电信号数据进行分割，从而能够将连续的分类决策流传输到假肢装置。本文表明，通过利用当前计算系统固有的处理能力，可以在分类器准确性和响应时间方面取得显著提高。假肢控制系统的其他重要特性也得到了满足。由于分类器为每个个体学习每个期望类别的肌肉激活模式，因此产生了自然的控制驱动。连续的决策流允许不间断地执行涉及多个关节的复杂操作序列。最后，所需的存储容量最小，这在嵌入式控制系统中是一个重要因素。
