Hudgins B, Parker P, Scott R N
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada.
IEEE Trans Biomed Eng. 1993 Jan;40(1):82-94. doi: 10.1109/10.204774.
This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.
本文描述了一种基于肌电模式分类来控制多功能假肢的新方法。结果表明，在肌肉收缩的初始阶段，肌电信号呈现出一种确定性结构。从肌电信号的几个时间段中提取特征以保留模式结构。然后使用人工神经网络对这些特征进行分类。控制信号源自自然收缩模式，经过很少的受试者训练就能可靠地产生。新的控制方案增加了可由单个肌电信号通道控制的功能数量，而且是以一种不会增加截肢者所需努力的方式实现的。文中给出了支持该方法的结果。
