Open Access
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
Article Number 03004
Number of page(s) 10
Section Topics in Computer Science
Published online 13 May 2022
  1. A. Furui, S. Eto, K. Nakagaki, K. Shimada, G. Nakamura, A. Masuda, T. Chin, and T. Tsuji, “A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control,” Science Robotics 4, eaaw6339 (2019). [CrossRef] [Google Scholar]
  2. K. Nazarpour,Control of Prosthetic Hands: Challenges and emerging avenues (Institution of Engineering and Technology, 2020). [CrossRef] [Google Scholar]
  3. W. Li, P. Shi, and H. Yu, “Gesture recognition using surface electromyography and deep learning for prostheses hand: State-of-the-art, challenges, and future,” Frontiers in Neuroscience 15 (2021). [Google Scholar]
  4. U.C. Allard, F. Nougarou, C. L. Fall, P. Giguère, C. Gosselin, F. Laviolette, and B. Gosselin, “A convolutional neural network for robotic arm guidance using semg based frequency-features,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2016) pp. 2464–2470. [CrossRef] [Google Scholar]
  5. J. Gibbard, “Open hand project,” Bristol, UK, accessed July 20, 2017 (2013). [Google Scholar]
  6. X. Zha, L. Wehbe, R. J. Sclabassi, Z. Mace, Y. V. Liang, A. Yu, J. Leonardo, B. C. Cheng, T. A. Hillman, D. A. Chen, et al., “A deep learning model for automated classification of intraoperative continuous emg,” IEEE Transactions on Medical Robotics and Bionics 3, 44–52 (2020). [Google Scholar]
  7. S. Lobov, N. Krilova, I. Kastalskiy, V. Kazantsev, and V. A. Makarov, “Latent factors limiting the performance of semg-interfaces,” Sensors 18, 1122 (2018). [CrossRef] [Google Scholar]
  8. C. Pylatiuk, S. Schulz, and L. Döderlein, “Results of an internet survey of myoelectric prosthetic hand users,” Prosthetics and orthotics international 31, 362–370 (2007). [CrossRef] [Google Scholar]
  9. J. T. Belter and A. M. Dollar, “Performance characteristics of anthropomorphic prosthetic hands,” in 2011 IEEE International Conference on Rehabilitation Robotics (IEEE, 2011) pp. 1–7. [Google Scholar]
  10. Z. Zhang, C. He, and K. Yang, “A novel surface electromyographic signal-based hand gesture prediction using a recurrent neural network,” Sensors 20, 3994 (2020). [CrossRef] [Google Scholar]
  11. M. Zanghieri, S. Benatti, A. Burrello, V. Kartsch, F. Conti, and L. Benini, “Robust real-time embedded emg recognition framework using temporal convolutional networks on a multicore iot processor,” IEEE transactions on biomedical circuits and systems 14, 244–256 (2019). [Google Scholar]
  12. S. Guo, M. Pang, B. Gao, H. Hirata, and H. Ishihara, “Comparison of semg-based feature extraction and motion classification methods for upper-limb movement,” sensors 15, 9022–9038 (2015). [Google Scholar]
  13. X. Xi, M. Tang, S. M. Miran, and Z. Luo, “Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable semg sensors,” Sensors 17, 1229 (2017). [CrossRef] [Google Scholar]
  14. J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555 (2014). [Google Scholar]
  15. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation 9, 1735–1780 (1997). [CrossRef] [Google Scholar]
  16. S. Hochreiter, “The vanishing gradient problem during learning recurrent neural nets and problem solutions,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6, 107–116 (1998). [CrossRef] [Google Scholar]
  17. G. Langevin, “Inmoov,” URL (2014). [Google Scholar]

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