Enhancing HCI Through Real-Time Gesture Recognition with Federated CNNs: Improving Performance and Responsiveness

To facilitate smooth human-computer interaction (HCI) in a variety of contexts, from augmented reality to sign language translation, real-time gesture detection is essential. In this paper, researchers leverage federated convolutional neural networks (CNNs) to present a novel strategy that tackles these issues. By utilizing federated learning, authors may cooperatively train a global CNN model on several decentralized devices without sharing raw data, protecting user privacy. Using this concept, researchers create a federated CNN architecture designed for real-time applications including gesture recognition. This federated approach enables continuous model refinement and adaption to various user behaviours and environmental situations by pooling local model updates from edge devices. This paper suggests improvements to the federated learning system to maximize responsiveness and speed. To lessen the probability of privacy violations when aggregating models, this research uses techniques like differential privacy. Additionally, to reduce communication overhead and quicken convergence, To incorporate adaptive learning rate scheduling and model compression techniques research show how federated CNN approach may achieve state-of-the-art performance in real-time gesture detection tasks through comprehensive tests on benchmark datasets. In addition to performing better than centralized learning techniques. This approach guarantees improved responsiveness and adaptability to dynamic contexts. Furthermore, federated learning's decentralized architecture protects user confidentiality and data security, which qualifies it for usage in delicate HCI applications. All things considered, the design to propose a viable path forward for real-time gesture detection system advancement, facilitating more organic and intuitive computer-human interactions while preserving user privacy and data integrity. The proposed federated CNN approach achieves a prediction accuracy in real-time gesture detection tasks, outperforming centralized learning techniques while preserving user privacy and data integrity. The proposed framework that achieves prediction accuracy of 98.70% was implemented in python.

@article{Maragatham2024,
  title     = {Enhancing HCI Through Real-Time Gesture Recognition with Federated CNNs: Improving Performance and Responsiveness},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {R. Stella Maragatham and Yousef A. Baker El-Ebiary and Srilakshmi V and K. Sridharan and Vuda Sreenivasa Rao and Sanjiv Rao Godla},
  doi       = {10.14569/IJACSA.2024.0150489},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150489}
}