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Hand Gesture Recognition using Deep learning Method
- Authors
- Park, G.; Chandrasegar, V.K.; Koh, J.
- Issue Date
- 2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- deep learning; Fourier Transform; Human gestures interaction; Recognition technology; RNN
- Citation
- 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings, pp.1347 - 1348
- Indexed
- SCOPUS
- Journal Title
- 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings
- Start Page
- 1347
- End Page
- 1348
- ISSN
- 0000-0000
- Abstract
- In human-device interaction, hand signals are extremely significant. Despite the growing relevance of human hand signals, hand gesture recognition technology is still unreliable due to complicated hand gesture feedback or environmental influences. We suggest Recurrent Neural Networks (RNN) as a technology to solve this issue in this article. RNN has the benefit of being able to learn time series data, which significantly improves the efficiency of human-device interaction. We used a Sinuous antenna with a bandwidth of 6.0-8.5 GHz and a gain of 8 dB to collect data from the five hand signals and conditioned them using RNN. The proposed RNN's classification result was accurate to the tune of 80% ? 2021 IEEE.
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