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Gesture recognition has become the technology of improvement of human-computer interaction (HCI) using natural and touch-free communication interfaces. The paper provides a scientometric study of the world research on gesture recognition using convolutional neural networks (CNNs) and edge artificial intelligence (AI) between 2020 and 2025. The Scopus was used to collect data, and visualize collaboration networks, keyword patterns, and research evolution were done with VOS viewer and Biblioshiny. The research revealed 477 documents from 161 sources, whose annual growth rate stands at 8.45, which signifies the growing academic and industrial concern. The most prolific countries are China and India, and the main publishers are the IEEE Access, Sensors and IEEE Sensors Journal. CNNs, lightweight architectures, and multimodal fusion are emphasized as the main themes of co-occurrence analysis, but privacy-conscious AI systems based on edges and energy-efficient AI systems are becoming future research directions. The results show that there is a globally dispersed network of high author diversity and considerable interdisciplinary integration between computer vision, embedded systems, and artificial intelligence. This scientometric study offers information about intellectual organization, technological concentration, and research dissemination distribution of gesture recognition all over the world, and how CNN-driven and edge-enabled architectures are changing the future of intelligent, adaptable, and privacy conscious HCI applications.

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Scientometric Insights into Gesture Recognition Research Using Convolutional Neural Networks and Edge AI

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Archana Reddy Rondla,
  • Pasha Syed Nawaz,
  • Sai Anjani Manda

摘要

Gesture recognition has become the technology of improvement of human-computer interaction (HCI) using natural and touch-free communication interfaces. The paper provides a scientometric study of the world research on gesture recognition using convolutional neural networks (CNNs) and edge artificial intelligence (AI) between 2020 and 2025. The Scopus was used to collect data, and visualize collaboration networks, keyword patterns, and research evolution were done with VOS viewer and Biblioshiny. The research revealed 477 documents from 161 sources, whose annual growth rate stands at 8.45, which signifies the growing academic and industrial concern. The most prolific countries are China and India, and the main publishers are the IEEE Access, Sensors and IEEE Sensors Journal. CNNs, lightweight architectures, and multimodal fusion are emphasized as the main themes of co-occurrence analysis, but privacy-conscious AI systems based on edges and energy-efficient AI systems are becoming future research directions. The results show that there is a globally dispersed network of high author diversity and considerable interdisciplinary integration between computer vision, embedded systems, and artificial intelligence. This scientometric study offers information about intellectual organization, technological concentration, and research dissemination distribution of gesture recognition all over the world, and how CNN-driven and edge-enabled architectures are changing the future of intelligent, adaptable, and privacy conscious HCI applications.