Pattern Classification of EMG Signals Using an Event-Driven Task Model
Pattern Classification of EMG Signals Using an Event-Driven Task Model
复制标题
使用事件驱动任务模型对 EMG 信号进行模式分类
DOI：
10.7210/jrsj.20.771
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
M. Kaneko
中科院分区：
文献类型：
--
作者：
T. Tsuji;Kousuke Takahashi;O. Fukuda;M. Kaneko
肌电（EMG）是一种常用的假肢控制信号，它不仅包含了肌肉力量的信息，还包含了操作者的运动意图和关节的机械阻抗。然而，大多数以前的研究，采用基于肌电信号模式识别和/或力估计的假肢的控制方法，并没有利用截肢者执行的任务，如抓取一个对象的任务和舀汤任务的任何知识。在本文中，一个新的肌电信号模式识别方法，提出了使用统计组织的神经网络和事件驱动的任务模型。神经网络根据肌电信号输出运动的后验概率。任务模型用Petri网表示，用来描述任务相关知识，用来修正神经网络的输出。实验结果表明，该任务模型的使用显著提高了肌电信号模式识别的准确性。
Electromyogram (EMG) has been often used as a control signal of a prosthetic arm, which includes information on not only muscle force but operator's motor intention and mechanical impedance of joints. Most of previous researches, however, adopted the control methods of the prosthetic arms based on the EMG pattern discrimination and/or the force estimation from the EMG signals, and did not utilize any knowledge on tasks performed by amputees such as a grasping-an-object task and a spooning-soup task. In this paper, a new EMG pattern discrimination method is proposed using a statistically organized neural network and an event-driven task model. The neural network outputs aposterioriprobabilities of motions depending on the EMG signals. The task model is represented using a Petri net to describe the task dependent knowledge, which is used to modify the neural network's output. Experimental results show that the use of the task model significantly improves the accuracy of the EMG pattern discrimination.