MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||5|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
Gesture recognition system based on CNN-IndRNN and OpenBCI
1 Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310027, China
2 ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310018, China
3 Department of Orthopedic Surgery, 2 nd Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang 310009, China
* Corresponding author: firstname.lastname@example.org
Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.
© The Authors, published by EDP Sciences, 2021
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