Tactile Demographics: Predicting Demographic Information Using Touch Data from Mobile Devices

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Baylea Williams (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: The research conducted in this thesis was to serve as a baseline on which human demographics are most likely to be able to be predicted through touch screen interactions. In addition, it served as a way of finding which machine learning models are best suited to be applied to a larger scale experiment of this phenomena. We were able to reliably predict both age and race of participants and in the meantime show that the best machine learning models used was Random Forest Decision Trees and Naïve Bayes producing a higher classifier of accuracy than other classifiers tested. While the sample size used during this study was small, due to the ongoing Covid-19 pandemic, the results of this study indicate that research in this area is worthy of significant exploration.

Additional Information

Publication

Thesis

Language: English

Date: 2023

Subjects

mobile devices;demographics;gaming;demographics;psychology;predictive

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