Digit recognition with visible light using three photodiodes and 3D-preprocessed data

Bachelor thesis (2023)

Authors

G. van de Linde Electrical Engineering, Mathematics and Computer Science

Contributors

Q. Wang Embedded Systems - EEMCS (mentor)
R. Zhu Embedded Systems - EEMCS (mentor)
M. Yang Embedded Systems - EEMCS (mentor)
Ranga Rao Venkatesha Prasad Networked Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Gijs van de Linde
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Published Date

03-07-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper describes the feasibility of digit detection using three photodiodes and an Arduino Nano 33 BLE. This is done using a controlled lighting condition, using a bright lamp. It dives into the process of data collection, preprocessing and model selection for a recurrent neural network to do the classification of gestures into digits. Using a ”ConvLSTM double conv. layer model” with 128 units we were able to achieve an average accuracy of 0.500 ± 0.091 on a 5-Fold cross-validation procedure based on data that was collected in a controlled lighting environment. While this provides a foundation for digit detection using time-series data in a controlled light environment, further suggestions are made for future improvements and expansions in this area.

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