Examining the Impact of Muscle-Electrode Distance in sEMG Based Hand Motion Recognition
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6489-5_5
Reference14 articles.
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2. Zhao, J., Yu, Y., Sheng, X., Zhu, X.: Extracting stable control information from EMG signals to drive a musculoskeletal model - a preliminary study. In: Liu, H., et al (eds.). Intelligent Robotics and Applications. ICIRA 2022, vol. 13456. Lecture Notes in Computer Science. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13822-5_66
3. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;J Roy,2020
4. Fang, B., et al.: Simultaneous semg recognition of gestures and force levels for interaction with prosthetic hand. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2426–2436 (2022)
5. Zheng, J., Xie, C., Song, R., Xu, T.: A statistical learning approach for cable-driven robot in rehabilitation training. In: 2022 International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 296−301. IEEE (2022)
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