基于深度学习与灰度图的肌电机械手手势识别

Electromyography(EMG)Robotic Hand Gesture Recognition Based on Deep Learning and Grayscale Images

为了提高肌电机械手手势识别的精度,首先将肌电信号信息转换为图片,实现更直观、更明显的肌电信息展示,然后使用迁移学习微调Res Net50模型,构建基于肌电信号灰度图的肌电机械手手势识别模型,并使用该模型对三种手势进行识别实验。结果显示,ResNet50模型对基于肌电灰度图的手势识别率高达93%,有效减少了过拟合现象并提高了识别精度。

This study aims to convert electromyography(EMG)signals into two-dimensional grayscale images and use grayscale images and deep learning to improve the accuracy of EMG-controlled robotic hand gesture recognition.Firstly,the EMG signal information is transformed into images to achieve a more intuitive and clear display of EMG information.Then,transfer learning is used to fine-tune the ResNet50 model,constructing an EMG-controlled robotic hand gesture recognition model based on EMG grayscale images.This model is used to recognize three types of hand gestures,and the experimental results show that the ResNet50 model achieves a recognition rate of 93%for EMG grayscale-based gesture recognition.This effectively reduces overfitting and improves recognition accuracy.