Abstract
The sEMG (surface electromyogram signal) reflects the degree of muscle activity, which contains a large amount of motion information about the human body. It has important research significance and application value in sports science, rehabilitation medicine, and so on. There are more and more products related to sEMG acquisition, which are mainly divided into wired and wireless. Compared with wired sEMG acquisition equipment, wireless sEMG acquisition equipment can free the subjects from the shackles of connecting lines and space, so it is more concerned by researchers. However, due to the independence among the wireless acquisition modules, synchronization time errors between each module inevitably occur, which will bring difficulties to the subsequent data analysis. To solve this problem, a synchronized four-channel sEMG data acquisition system is designed in this paper. The experimental results show that the synchronization time errors among the 7 modules are almost controlled below 30 us, which are better than similar products in the market (about 300 us), and it has high SNR (Signal Noise Ratio), so the effective data collected can be used for sEMG signal analysis and processing.
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Oleksii Barybin, Elina Zaitseva, and Volodymyr Brazhnyi. Testing the security esp32 internet of things devices. In 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S T), pages 143–146, 2019. https://doi.org/10.1109/PICST47496.2019.9061269
Wang Li. Study on multi-channel wireless semg simultaneous acquisition technology. Master’s thesis, Zhejiang University, 2018
Sheng, Y., Zeng, J., Liu, J., Liu, H.: Metric-based muscle synergy consistency for upper limb motor functions. IEEE Transactions on Instrumentation and Measurement 71, 1–11 (2022). https://doi.org/10.1109/TIM.2021.3132345
Wei Xuhui. Research on high-density eeg / emg wireless acquisition system
Yang, X., Yan, J., Fang, Y., Zhou, D., Liu, H.: Simultaneous prediction of wrist/hand motion via wearable ultrasound sensing. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 970–977 (2020)
Yangbo Yu, Hongze Jiang, Yu Zhou, and Honghai Liu. Analysis of stable semg features for bilateral upper limb motion. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1945–1950, 2018. https://doi.org/10.1109/SMC.2018.00336
Z. Yu, Y. Fang, Z. Jia, K. Li, and H. Liu. A multi-channel emg-driven fes solution for stroke rehabilitation: 11th international conference, icira 2018, newcastle, nsw, australia, august 9–11, 2018, proceedings, part i. International Conference on Intelligent Robotics & Applications, 2018
Jia Zeng, Yu Zhou, Yicheng Yang, Jipeng Yan, and Honghai Liu. Fatigue-sensitivity comparison of semg and a-mode ultrasound based hand gesture recognition. IEEE Journal of Biomedical and Health Informatics, 2021
Jia Zeng, Yu Zhou, Yicheng Yang, Jiaole Wang, and Honghai Liu. Feature fusion of semg and ultrasound signals in hand gesture recognition. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 3911–3916. IEEE, 2020
Rong Zhou, Qiang Luo, Xiaorong Feng, and Chenglong Li. Design of a wireless multi-channel surface emg signal acquisition system. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pages 279–283, 2017. https://doi.org/10.1109/CompComm.2017.8322556
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Cheng, N., Yang, H., Chang, H., Liu, Y., Cao, R., Liu, H. (2022). Design of Wireless Synchronous sEMG Information Acquisition System. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_27
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DOI: https://doi.org/10.1007/978-3-031-13841-6_27
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