Recognising Gestures Using Ambient Light and Convolutional Neural Networks
Adapting Convolutional Neural Networks for Gesture Recognition on Resource-constrained Microcontrollers
W. Narchi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Qing Wang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Mingkun Yang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Ran Zhu – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Christoph Lofi – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
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 microcontroller to present its feasibility in embedded operation. The final utilised network was observed to have accuracies between 75.4% and 86.8% depending on the testing conditions. Further, all candidates were found to be sufficiently compact and low-latency for real-time operation.