Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning

Applied Sciences

Abolfazl Mehbodniya¹  Ahmed Arafa¹  Ahmed Alwakeel¹  Julian Webber²  Rui Teng³ 

[1] Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, 7th Ring Road, Doha 20185145, Kuwait;Graduate School of Engineering Science, Osaka University, Toyonaka 560-8531, Japan;Organization for Research Initiatives and Development, Doshisha University, Kyoto 610-0394, Japan;

关键词: visible light communications (VLC);    gesture recognition (GR);    human-computer interaction (HCI);    human activity recognition (HAR);    machine learning (ML);    neural network;   

DOI  :  10.3390/app112411582

来源: DOAJ

【 摘 要 】

Gesture recognition (GR) has many applications for human-computer interaction (HCI) in the healthcare, home, and business arenas. However, the common techniques to realize gesture recognition using video processing are computationally intensive and expensive. In this work, we propose to task existing visible light communications (VLC) systems with gesture recognition. Different finger movements are identified by training on the light transitions between fingers using the long short-term memory (LSTM) neural network. This paper describes the design and implementation of the gesture recognition technique for a practical VLC system operating over a distance of 48 cm. The platform uses a single low-cost light-emitting diode (LED) and photo-diode sensor at the receiver side. The system recognizes gestures from interruptions in the direct light transmission, and is therefore suitable for high-speed communication. Gesture recognition accuracies were conducted for five gestures, and results demonstrate that the proposed system is able to accurately identify the gestures in up to 88% of cases.

【 授权许可】

Unknown