Print Email Facebook Twitter Hand Gesture Recognition on Arduino Using Recurrent Neural Networks and Ambient Light Title Hand Gesture Recognition on Arduino Using Recurrent Neural Networks and Ambient Light Author Lipski, Matthew (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Yang, M. (mentor) Zhu, R. (mentor) Wang, Q. (mentor) Lofi, C. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 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. Subject Recurrent Neural NetworkEmbedded AIAmbient LightHand Gesture RecognitionArduino To reference this document use: http://resolver.tudelft.nl/uuid:366e8529-e282-42e5-9173-18443fb7504e Part of collection Student theses Document type bachelor thesis Rights © 2022 Matthew Lipski Files PDF Hand_Gesture_Recognition_ ... _Light.pdf 549.96 KB Close viewer /islandora/object/uuid:366e8529-e282-42e5-9173-18443fb7504e/datastream/OBJ/view