Hand Gesture Recognition on Arduino Using Recurrent Neural Networks and Ambient Light
M.S. Lipski (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Mingkun Yang – Mentor (TU Delft - Embedded Systems)
R. Zhu – Mentor (TU Delft - Embedded Systems)
Q. Wang – Mentor (TU Delft - Embedded Systems)
Christoph Lofi – Graduation committee member (TU Delft - Web Information Systems)
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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.