Low-Power Gesture Recognition Using Convolutional Neural Networks and Ambient Lighting

This paper presents a study focused on developing an efficient signal processing pipeline and identifying suitable machine learning models for real-time gesture recognition using a testbed consisting of an Arduino Nano 33 BLE and three OPT101 photodiodes. Our research aims to address the challenges of limited computational power whilst...

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

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...

Designing a Software Receiver for Gesture Recognition with Ambient Light

There is a growing need for touch-free interaction with public utilities such as coffeemakers and vending machines that will help prevent the spread of diseases such as COVID-19. One solution is the integration of embedded gesture recognition systems relying on ambient light. However, existing work so far is found to be inefficient in terms of...