Designing an adaptable and low-cost system for gesture recognition using visible light

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Abstract

This paper describes the design of an adaptable, low-cost, and energy efficient gesture detection system. The system leverages the ambient light available in the environment to perform Visible Light Sensing (VLS) using powerful Convolutional Neural Networks. The focus lies on designing a system that is capable of robust gesture detection in any environment while only utilizing a limited number (3) of photodiode sensors. The research conducted in this paper contributes to two crucial aspects of VLS gesture recognition systems. First, it shows how the photodiode sensors can be automatically fine-tuned using four resistors to achieve excellent sensing performance in a wide range of lighting environments from 50 Lux up to 150k Lux. Secondly it is shown that a photodiode arrangement consisting of an equilateral triangle with sides of 5cm facilitates the highest performance and robustness using Dynamic Time Warping for general and user-friendly gestures.