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...

A TinyML system for gesture detection using 3D pre-processed data

Visible light sensing is a field of research that creates new possibilities for human-computer interaction. This research shows the viability of designing a system for detecting hand gestures using a cost-effective detection circuit employing 3 light-sensitive photodiodes. The way this research shows viability is by developing a machine-learning...

Touchless Hand Gesture-Based Digit Recognition: using Light-Sensors, Convolutional Neural Networks and a Microcontroller

Touchless interaction with computers has become more important in recent years, especially in the context of the COVID-19 pandemic.<br/>Applications include situations where touch input is not possible or not desirable, e.g. for hygienic purposes in a public setting or a medical setting.<br/>Practical examples for touchless interaction include...

Towards a low-cost air-written character recognition system: Designing an embedded machine learning system to recognise the first 10 letters of the Latin alphabet

This study introduces a novel system that leverages three photodiodes and ambient light to identify air-written characters on a resource-constrained device. Through experimentation, suitable methods of data preprocessing, machine learning and model compression were selected to recognize the first 10 characters of the Latin alphabet. The final...

Recognising Gestures Using Ambient Light and Convolutional Neural Networks: Adapting Convolutional Neural Networks for Gesture Recognition on Resource-constrained Microcontrollers

This paper presents how a convolutional neural network can be constructed in order to recognise gestures using photodiodes and ambient light. A number of candidates are presented and evaluated, with the most performant being adopted for in-depth analysis. This network is then compressed in order to be ran on an Arduino Nano 33 BLE...

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...

Constructing A Dataset For Gesture Recognition Using Ambient Light

Public technology has been shown to have a strong dependence on physical touch, which increases the transmission of diseases. Gesture recognition helps to reduce this transmission, as the dependence on physical touch is removed. Furthermore, the use of visible light for gesture recognition would reduce the power consumption of public technology,...