Recognising Gestures Using Ambient Light and Convolutional Neural Networks

Adapting Convolutional Neural Networks for Gesture Recognition on Resource-constrained Microcontrollers

Bachelor thesis (2022)

Authors

W. Narchi Electrical Engineering, Mathematics and Computer Science

Contributors

Q. Wang Embedded Systems - EEMCS (mentor)
M. Yang Embedded Systems - EEMCS (mentor)
R. Zhu Embedded Systems - EEMCS (mentor)
C. Lofi Web Information Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 William Narchi
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Published Date

22-07-2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.