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  • https://doi.org/10.1109/lpt.2026.3678436Copy DOI Icon

An Integrated Visible-Light Communication/Gesture-Recognition System Enabled by Machine Learning

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Abstract

Integrating high-speed visible light communication (VLC) with natural human–machine interaction is important for smart lighting and secure indoor Internet of Things (IoT) systems, yet most gesture interfaces rely on cameras or extra sensors, raising privacy concerns and system complexity. This paper presents a waveform-sharing, dual-task VLC receiver that jointly demodulates data and recognizes gestures from the same photoelectric waveform without additional sensing hardware, enabling camera-free, privacy-preserving interactive VLC. Machine learning (ML) is employed for end-to-end compensation and classification: a convolutional neural network (CNN)-based post-equalizer mitigates time-varying distortions from lightemitting diode (LED) nonlinearity, multipath propagation, and gesture occlusion, boosting the raw rate of a 1 m indoor blue-LED link from 30 Mb/s to 46 Mb/s in the communication-only setting, while spectro-temporal features with a lightweight classifier achieve ~98% accuracy over eight dynamic gestures. With gesture inference enabled, the net payload rate remains ~26 Mb/s with negligible added latency, demonstrating that interaction can be overlaid with limited throughput loss and reduced power, cost, and privacy risk.

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