A hybrid CNN-LSTM network for hand gesture recognition with surface EMG signals

Zehua Cai; Yuesheng Zhu

doi:10.1117/12.2601074

30 June 2021 A hybrid CNN-LSTM network for hand gesture recognition with surface EMG signals

Zehua Cai, Yuesheng Zhu

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187804 (2021) https://doi.org/10.1117/12.2601074
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

In this paper, hybrid network based on convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed to improve hand gesture recognition accuracy. Distinguish from the large number of traditional surface electromyography (sEMG) features proposed by previous researchers, without involving a lot of manual design and professional domain knowledge, this hybrid network can automatically extract both spatial features and temporal features from the input sEMG signals. The hybrid CNN-LSTM Network has two parallel feature extraction stages: spatial features extraction using CNN and temporal features extraction using LSTM. The hybrid CNN-LSTM network combines spatial features and temporal features as Hybrid features (HybridFeat) and feeds HybridFeat into traditional classifiers, including linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighbor (KNN). The experiments showed that both in inter-session scenario and inter-subject scenario, the HybridFeat outperforms all the tested traditional features and CNNFeat. Besides, it was found that combining HybridFeat with traditional features can further improve the accuracy.

Citation Download Citation

Zehua Cai and Yuesheng Zhu "A hybrid CNN-LSTM network for hand gesture recognition with surface EMG signals", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187804 (30 June 2021); https://doi.org/10.1117/12.2601074

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