Abstract
In the field of human-machine interaction, gesture recognition using sparse multichannel surface electromyography (sEMG) remains a challenge. Based on the Hilbert filling curve, a dual-view multi-scale convolutional neural network (DVMSCNN) is designed to enhance gesture recognition performance in this paper. The network consists of two parts. In the first part, sEMG is filled using Hilbert filling curve, and the obtained images in the time and electrode domain are used as inputs to the block. In the second part, the depth features learned by block are fused and classified by a “layer fusion” based view aggregation network. The evaluation of the architecture in the four databases of Ninapro-DB1, DB2, DB3 and DB4 shows that DVMSCNN is more than 7% more accurate than other state-of-the-art methods. When validated using a home-grown dataset, DVMSCNN was able to achieve a recognition rate of 0.8848.
Highlights
As human-machine interaction playing an important role in modern life, the question of how to interact with computers in an efficient and natural way has become an important research topic
The results showed that convolutional neural network (CNN) consistently exhibited better performance
Comparison with the state-of-the-art gesture recognition approaches To evaluate the performance of the dual-view multi-scale convolutional neural network (DVMSCNN), comparative study is conducted with other state-of-the-art surface electromyography (sEMG)-based models
Summary
As human-machine interaction playing an important role in modern life, the question of how to interact with computers in an efficient and natural way has become an important research topic. Chen L. et al [34] proposed a feature extraction method based on Hilbert-Huang transform and used extreme learning machine for classification. Y Zhang et al.: Research on sEMG Based gesture recognition by dual-view deep learning (a). [35] used Hilbert curves to represent mammograms as 1dimensional vectors and extracted features from them to detect breast cancer, with a final accuracy of 85.83%. In this case, for the input image problem of CNN, filling sEMG with Hilbert curve helps to enhance the classification effect of gesture recognition. We design a dual-view multi-scale convolutional neural network (DVMSCNN) to improve sEMG-based gesture recognition performance.
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