Gesture Recognition and Control based on Visible Light Communication

The evolution of human-computer interaction (HCI), particularly in the era of the Internet of Things (IoT), demands innovative solutions that transcend traditional interfaces like keyboards and touchscreens. This study introduces a visible light communication (VLC) based sensing system for non-contact gesture recognition in HCI. The system utilizes a cost-effective VLC setup optimized for indoor environments, ensuring precise signal transmission and reception. The data acquisition and processing board can accurately capture and analyze light-based hand gesture signals. This process is supported by a comprehensive signal processing framework that includes data preprocessing steps and robust machine learning algorithms. These algorithms are designed to detect subtle light pattern variations due to gestures, ensuring an accurate and responsive recognition process. The system’s effectiveness is confirmed by its high gesture recognition accuracy of $95.7 \%$. The study’s key contributions include the development of a VLC-based system for gesture recognition and control, the integration of diverse signal processing techniques suitable for VLC-based data, and the successful demonstration of the system’s practical application through extensive experimental evaluations. These evaluations have validated the system’s efficacy in real-world scenarios, marking an advancement in VLC-enabled HCI. The research highlights VLC’s potential as a user-friendly and efficient interface in HCI, offering more intuitive and responsive interactions in smart environments.