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Deep Learning Model for Dynamic Hand Gesture Recognition for Natural Human-Machine Interface on End Devices

Author

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  • Tsui-Ping Chang

    (National Taichung University of Science and Technology, Taiwan)

  • Hung-Ming Chen

    (National Taichung University of Science and Technology, Taiwan)

  • Shih-Ying Chen

    (National Taichung University of Science and Technology, Taiwan)

  • Wei-Cheng Lin

    (National Taichung University of Science and Technology, Taiwan)

Abstract

As end devices have become ubiquitous in daily life, the use of natural human-machine interfaces has become an important topic. Many researchers have proposed the frameworks to improve the performance of dynamic hand gesture recognition. Some CNN models are widely used to increase the accuracy of dynamic hand gesture recognition. However, most CNN models are not suitable for end devices. This is because image frames are captured continuously and result in lower hand gesture recognition accuracy. In addition, the trained models need to be efficiently deployed on end devices. To solve the problems, the study proposes a dynamic hand gesture recognition framework on end devices. The authors provide a method (i.e., ModelOps) to deploy the trained model on end devices, by building an edge computing architecture using Kubernetes. The research provides developers with a real-time gesture recognition component. The experimental results show that the framework is suitable on end devices.

Suggested Citation

  • Tsui-Ping Chang & Hung-Ming Chen & Shih-Ying Chen & Wei-Cheng Lin, 2022. "Deep Learning Model for Dynamic Hand Gesture Recognition for Natural Human-Machine Interface on End Devices," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 13(10), pages 1-23, June.
    • Handle: RePEc:igg:jismd0:v:13:y:2022:i:10:p:1-23
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