Eye tracking-based Sedentary Activity Recognition with Conventional Machine Learning Algorithms

Bachelor thesis (2022)

Authors

V.P. Chatalbasheva Electrical Engineering, Mathematics and Computer Science

Contributors

G. Lan Embedded Systems - EEMCS (mentor)
L. Du Embedded Systems - EEMCS (mentor)
M.T.J. Spaan Algorithmics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Violeta Chatalbasheva
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Published Date

23-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Sedentary activity recognition is an important research field due to its various positive implications in people’s life. This study builds upon previous research which is based on low level features extracted from the gaze signals using a fixation filter and uses a dataset of 24 participants performing 8 different sedentary activities. The main research question are related to extracting features from the raw data and selecting the most relevant ones which improve the classification accuracy. The novelty of this paper is using dynamic thresholds in the fixation filter to ensure the fixation-specific measurements reported by literature as well as contributing to the human activity recognition (HAR) field by developing an additional low-level gaze feature in combination with the fixation dispersion area. The machine learning (ML) models, Random Forest, k-NN (k-Nearest Neighbour) and SVM (Support Vector Machine), used for the classification task are evaluated using the within dataset evaluation protocol, with cross validation and hyperparameter tuning. The overall recognition accuracy of the Random Forest model is 0.94 (f1-score).