Revolutionizing Hand Gesture Recognition: A Transfer Learning Approach using Surface Electromyography and Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Revolutionizing Hand Gesture Recognition: A Transfer Learning Approach using Surface Electromyography and Convolutional Neural Networks

Published:2023-08-23 Issue: Volume: Page:
ISSN:
Container-title:2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)
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Author:

Deepa R.¹,Vidyabharathi D.²,Hemlata ³,P.C.D Kalaivaani⁴,Marimuthu M.²,Ismail K Mohamed⁵

Affiliation:

1. R.S.M Nagar,R.M.K Engineering College,Department of Computer Science and Engineering,Thiruvalluvar District,601206

2. Sona College of Technology,Department of Computer Science and Engineering,Salem

3. Manipal University,Department of Computer Science and Engineering,Jaipur

4. Kongu Engineering College,Department of Computer Science and Engineering,Perundurai

5. Agni College of Technology,Department of Electronics and Communication Engineering,Thalambur,Chennai

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10250420/10250450/10250724.pdf?arnumber=10250724

Reference19 articles.

1. The ChaLearn gesture dataset (CGD 2011)

2. Human hand as a powerless and multiplexed infrared light source for information decryption and complex signal generation

3. Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

4. Ms-asl: A large-scale data set and benchmark for understanding american sign language;joze,2018

5. Dynamic hand gesture recognition based on short-term sampling neural networks

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