Abstract
It is conducive to the application of sEMG signals in helping disabled people through combining wearable devices with deep learning. Therefore, design of sEMG gesture recognition system using deep learning based on wearable device is proposed in this paper. The system is mainly consisted of wearable sEMG acquisition device and sEMG gesture recognition method based on deep learning. In the wearable sEMG acquisition device, the sEMG signal sensor is mainly used to convert the human bioelectrical signal into an analog electrical signal. Then it can be acquired using an analog to digital converter. We also use 2.4 GHz wireless communication for data transmission, and use the micro-controller as the core of system control and data processing. In the sEMG gesture recognition method, we designed a model of sEMG signal gesture classification based on convolutional neural network (CNN). It can avoid omission of important feature information and improve accuracy of recognition, effectively. In the experimental part, we collected the sEMG signals of three different gestures using our own wearable sEMG acquisition device. Then, we trained and evaluated on the designed sEMG gesture recognition model using these data. A recognition accuracy of about 79.43% can be achieved in three gestures. Finally, we trained and tested the sEMG gesture recognition model on the Ninapro DB5 dataset and can reach about 74.51% accuracy on 52 gestures. In the case that there are more types of gestures recognized, our accuracy is still 5.02%, 6.61%, and 2.58% higher than Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Long Short Term Memory-CNN (LCNN), respectively. Also, the accuracy rate is 5.47% higher than SVM and Random Forests.
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Acknowledgements
The authors would like to thank the financial support by China Postdoctoral Science Foundation under Grant No.2016M601860, the National Science Foundation of P.R. China under Grant Nos.61401221, 61873131, 61872196, 61701168, 61572261, 61572260, Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant Nos.SJCX19_0240, SJKY19_0823, School Fund of NJUPT under Grant No.NY220055; Teaching Reform Project of Tongda College in NJUPT under Grant No.JG30618003.
The authors are very grateful to the open source dataset provided by the NinaPro project team, which is an important prerequisite for the successful implementation of this paper. Furthermore, we would like to thank NVIDIA for supporting to this project. All of the experiments in this article worked smoothly on NVIDIA TITAN Xp.
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As co-first authors, Shu Shen and Kang Gu contributed equally to this work.
This work was supported in part by the National Science Foundation of P.R.China under Grant Nos.61401221, 61873131, 61872196, 61701168, 61572261, 61572260; China Postdoctoral Science Foundation under Grant No.2016M601860; Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant Nos.SJCX19_0240, SJKY19_0823; School Fund of NJUPT under Grant No.NY2200; Teaching Reform from Project of Tongda College in NJUPT under Grant No.JG30618003.
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Shen, S., Gu, K., Chen, XR. et al. Gesture Recognition Through sEMG with Wearable Device Based on Deep Learning. Mobile Netw Appl 25, 2447–2458 (2020). https://doi.org/10.1007/s11036-020-01590-8
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DOI: https://doi.org/10.1007/s11036-020-01590-8