Itakura N, Kinbara Y, Fuwa T, Sakamoto K
Department of Communications and Systems Engineering, University of Electro-Communications, Tokyo, Japan.
Appl Human Sci. 1996 Nov;15(6):287-94. doi: 10.2114/jpa.15.287.
We tried to discriminate different forearm's motions by surface EMG signals using neural network. In order to get a higher discrimination rate, the positions of electrodes were improved. We also tried to discriminate similar motions in order to clarify the limitation of the discrimination by surface EMG signals. Two experiments were carried out. One was to discriminate five different motions: grasp, wrist flexion, wrist extension, forearm pronation, and forearm supination (Experiment 1). The other was to discriminate four similar motions which have different quantitative definitions at grasp, wrist flexion/ extension, or forearm pronation/supination (Experiment 2). Four surface electrodes were placed on the skin above the main active muscles: short radial extensor m. of wrist, supinator m., long radial extensor m. of wrist, and ulnar flexor m. of wrist, considering anatomical functions of the forearm's muscles. EMG signals were recorded during 2 sec while the subjects kept the motions. Recorded EMG signals were sampled at 200 msec intervals after full-wave rectifying and low-pass filtering. Therefore, the number of sampling data patterns of EMG signals was 10 for every motion. Three layers of neural network was used for discrimination. The number of units in the input layer is 4, and the number of units in the output layer is 5 or 4. In order to get the best discrimination rate of the motions, we changed the number of units in the hidden layer from 3 to 12. The neural network was trained by the back-propagation algorithm. In Experiment 1, the best average values of discrimination rates under three patterns of EMG signals for each subject were 96.0%, 98.0%, and 87.2% when the numbers of units in the hidden layer were 10, 11, and 3 respectively. In Experiment 2 using original EMG patterns, the best average values of discrimination rates at grasp, extension/flexion, and pronation/supination were 59.5%, 76.0%, and 25.0% respectively. By using normalized EMG patterns, these were 40.0%, 84.8%, and 55.5% respectively.
我们尝试使用神经网络通过表面肌电信号来区分前臂的不同动作。为了获得更高的区分率，对电极位置进行了改进。我们还尝试区分相似动作，以阐明表面肌电信号区分的局限性。进行了两个实验。一个是区分五种不同动作：抓握、腕关节屈曲、腕关节伸展、前臂旋前和前臂旋后（实验1）。另一个是区分在抓握、腕关节屈曲/伸展或前臂旋前/旋后时有不同定量定义的四种相似动作（实验2）。考虑到前臂肌肉的解剖功能，在主要活动肌肉上方的皮肤上放置了四个表面电极：腕部桡侧短伸肌、旋后肌、腕部桡侧长伸肌和腕部尺侧屈肌。在受试者保持动作的2秒内记录肌电信号。记录的肌电信号在全波整流和低通滤波后以200毫秒的间隔进行采样。因此，每个动作的肌电信号采样数据模式数量为10。使用三层神经网络进行区分。输入层的单元数为4，输出层的单元数为5或4。为了获得动作的最佳区分率，我们将隐藏层的单元数从3改为12。神经网络通过反向传播算法进行训练。在实验1中，当隐藏层的单元数分别为10、11和3时，每个受试者在三种肌电信号模式下的最佳区分率平均值分别为96.0%、98.0%和87.2%。在使用原始肌电模式的实验2中，抓握、伸展/屈曲和旋前/旋后的最佳区分率平均值分别为59.5%、76.0%和25.0%。通过使用归一化肌电模式，这些值分别为40.0%、84.8%和55.5%。
