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Improving Accuracy of Hand Gesture Recognition using Recurrent Neural Networks
- Authors
- Park, A.G.; Chandrasegar, B.V.K.; Koh, C.J.
- Issue Date
- 9-Aug-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- 2021 International Conference on Electromagnetics in Advanced Applications, ICEAA 2021, pp 361
- Indexed
- SCOPUS
- Journal Title
- 2021 International Conference on Electromagnetics in Advanced Applications, ICEAA 2021
- Start Page
- 361
- ISSN
- 0000-0000
- Abstract
- In human-device communications, human gestures are crucial. Furthermore, hand-activated communication helps control without physical contact [1]. While the importance of hand gesture recognition techniques is rising, hand gesture recognition evidence has a low degree of reliability. To identify gestures, first and foremost, a wirelessly recognizable system is needed. Cameras, radar, and other options are available. Cameras, on the other hand, are impossible to use in environments with no sun, rain, or where personal privacy can not be violated. As a result, cameras used to be the chosen system, but due to different constraints, they now tend to use radar. ? 2021 IEEE.
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