Multi-scale EMG classification with spatial-temporal attention for prosthetic hands

Author:

M Emimal1,Hans W. Jino1,T.M Inbamalar2,Lindsay N. Mahiban3

Affiliation:

1. Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India

2. Department of Electronics and Communication Engineering, RMK College of Engineering and Technology, Puduvoyal, Chennai, India

3. Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, India

Publisher

Informa UK Limited

Subject

Computer Science Applications,Human-Computer Interaction,Biomedical Engineering,General Medicine,Bioengineering

Reference55 articles.

1. Allard UC, Nougarou F, Fall CL, Giguère P, Gosselin C, Laviolette F, Gosselin B. 2016. A convolutional neural network for robotic arm guidance using sEMG based frequency-features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. Daejeon, Korea: IEEE; p. 2464–2470.

2. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

3. Atzori M Gijsberts A Heynen S Mittaz Hager A-G Deriaz O van der Smagt P Castellini C Caputo B Muller H. 2014. Building the NINAPRO Database: A Resource for the Biorobotics Community.

4. Bahdanau D Cho K Bengio Y. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

5. Bai S Kolter JZ Koltun V. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

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