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Das, A., Maitra, K., Roy, S., Ganguly, B., Sengupta, M. and Biswas, S. (2023) Development of a Real Time Vision-Based Hand Gesture Recognition System for Human-Computer Interaction. Proceedings of the 2023 IEEE 3rd Applied Signal Processing Conference (ASPCON), Haldia, 24-25 November 2023, 294-299.
https://doi.org/10.1109/ASPCON59071.2023.10396583

has been cited by the following article:

• TITLE: Gesture Recognition Based on Time-of-Flight Sensor and Residual Neural Network

  AUTHORS: Yuqian Ma, Zitong Fang, Wen Jiang, Chang Su, Yuankun Zhang, Junyu Wu, Zhengjie Wang

  KEYWORDS: Hand Posture Recognition, Human-Computer Interaction, Deep Learning, Gesture Datasets, Real-Time Processing

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.6, June 27, 2024

  ABSTRACT: With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.