Faculty Publications

Poster: Automated Tooth Brushing Detection Using Smartwatch

Document Type

Conference

Keywords

activity recognition, machine learning, toothbrushing

Journal/Book/Conference Title

MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services

First Page

676

Last Page

677

Abstract

Oral diseases affect an estimated 3.5 billion people globally, posing significant health challenges. According to the World Health Organization (WHO), adopting self-care practices and maintaining personal oral hygiene can substantially mitigate the prevalence of dental caries. While smartwatches have previously been utilized to track activities of daily living (ADL), their widespread availability has yet to be harnessed for accurately identifying tooth brushing activity among other common ADL. In this Work in Progress (WIP), we demonstrate how motion sensors integrated into smartwatches can effectively distinguish tooth brushing from seven other very similar ADL. We present our initial results that show a promising 94% accuracy with 84% sensitivity.

Department

Department of Computer Science

Original Publication Date

6-3-2024

DOI of published version

10.1145/3643832.3661417

Link to Full Text

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