基于灰度理论模型的截肢受试者手势分类方法研究

Research on Gesture Classification Methods in Amputee Subjects Based on Gray Theory Model

针对截肢者手势动作特征提取复杂、动作识别率较低的问题,该文提出一种基于灰度模型的特征提取方法。首先对预处理后的肌电信号与加速度信号经滑动窗信号截取。然后提取表面肌电信号均值、灰度模型的驱动项系数和加速度信号的绝对值均值构成特征向量,最后对滑动窗截取信号特征进行连续的识别。该文采用NinaPro(Non invasive adaptive Prosthetics)公开数据集对提出的方法进行验证,实验表明该文算法能够有效提取肌电和加速度信号的特征,对9名截肢受试者的17类手势动作的平均识别率达到91.14%,提高了17类手势的识别准确率,为仿生假肢人机交互控制算法提供了一种新的思路。

In view of the complexity and low accuracy of feature extraction of amputees’ movement gestures, a feature extraction method based on gray model is proposed in this paper. Firstly, the pre-processed surface ElectroMyoGraphy(sEMG) and acceleration signals are intercepted by sliding window. Then, the mean value of the surface EMG signal, the driving coefficient of the gray model and the absolute mean value of the acceleration signal are extracted as features to form a feature vector. Finally, the features of the signal intercepted by sliding window are identified continuously. The proposed method is verified using NinaPro(Non Invasive Adaptive Prosthetics) public dataset, experimental results show that the proposed algorithm can effectively extract the characteristics of the electromyography and acceleration signals. An average accuracy of91.14% is reached for 17 action gestures of 9 amputation subjects. The proposed approach provides a new way for the control algorithm of bionic limbs based human-computer interaction.