Hand Gesture Recognition on Arduino Using Recurrent Neural Networks and Ambient Light

Abstract

Touching physical buttons to interact with public electronic devices has raised some concerns regrading disease transmission following the COVID-19 pandemic. The use of hand gestures as a touchless replacement sounds appealing, but comes with the challenge of recognizing which gesture is being performed by the user, with only the processing power of a microcontroller. This paper explores the use of recurrent neural networks (RNNs) and their derivatives to recognize hand gestures on an Arduino Nano 33 BLE. The neural networks receive input from 3 OPT101 photodiodes, which emit a voltage that increases with the intensity of light that hits them, meaning they can effectively track hand shadows cast by the user’s hand under ambient light. After testing various RNN-based neural network architectures, CNN-LSTMs produced the highest validation accuracy. However, due to issues with the testing setup, the highest validation accuracy measured for a CNN-LSTM was only 43%, indicating that further work is required.