Gesture recognition based on surface electromyography‐feature image

• Published in: Concurrency and computation Vol. 33; no. 6
• Main Authors: Cheng, Yangwei, Li, Gongfa, Yu, Mingchao, Jiang, Du, Yun, Juntong, Liu, Ying, Liu, Yibo, Chen, Disi
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 25.03.2021; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; CNN; Convolution; Electromyography; Feature extraction; Feature recognition; Gesture recognition; Machine learning; Object recognition; sEMG; sEMG‐feature image
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6051

Summary For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1‐dim convolution kernel to extract deep features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw‐sEMG images based on the CNN and the sEMG‐feature‐images based on the CNN and sEMG based on the traditional machine learning, the multi‐sEMG‐features image based on the CNN is the highest, which coming up to 82.54%.