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Toothbrushing data and analysis of its potential use in human activity recognition applications: dataset

Published: 16 November 2020 Publication History

Abstract

In this paper, we describe and analyze a time-series dataset from toothbrushing activity using brush-attached and wearable sensors. The data was collected from 17 participants when they brushed their teeth over one week in 5 different locations. The dataset consists of 62 toothbrushing sessions for each of the brush-attached and wearable sensor approaches, using both electric and manual brushes. The average duration of each session is 2 minutes. One sensor device was attached to the handle of the brush while the other was worn by the participants as a wrist-watch. We collected the data from a 3-axis accelerometer and a 3-axis gyroscope at a 200 Hz sampling rate. Most of the data has been labelled. We investigated the characteristics of the data using spectral analysis and performed a pre-processing pipeline in order to generate features used to train a Support Vector Machine Classifier. We were able to identify which part of the jaw was being brushed with 98.6% accuracy.

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Cited By

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  • (2024)Poster: An Automated Method to Detect Tooth Brushing Activity with Smartwatch SensorsProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661417(676-677)Online publication date: 3-Jun-2024
  • (2024)A Study on the Energy Sustainability of Early Exit Networks for Human Activity RecognitionIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33032709:1(61-74)Online publication date: Jan-2024
  • (2024)Dentists who can auscultate: Microphone-based toothbrushing quality monitoring system for electronic toothbrushExpert Systems with Applications10.1016/j.eswa.2024.124817255(124817)Online publication date: Dec-2024
  • Show More Cited By

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cover image ACM Conferences
DATA '20: Proceedings of the Third Workshop on Data: Acquisition To Analysis
November 2020
42 pages
ISBN:9781450381369
DOI:10.1145/3419016
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 November 2020

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Author Tags

  1. activity recognition
  2. machine learning
  3. off-body sensors
  4. smart toothbrush

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Overall Acceptance Rate 74 of 167 submissions, 44%

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Cited By

View all
  • (2024)Poster: An Automated Method to Detect Tooth Brushing Activity with Smartwatch SensorsProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661417(676-677)Online publication date: 3-Jun-2024
  • (2024)A Study on the Energy Sustainability of Early Exit Networks for Human Activity RecognitionIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33032709:1(61-74)Online publication date: Jan-2024
  • (2024)Dentists who can auscultate: Microphone-based toothbrushing quality monitoring system for electronic toothbrushExpert Systems with Applications10.1016/j.eswa.2024.124817255(124817)Online publication date: Dec-2024
  • (2024)Filtering Data from Motion Sensors with Rich Features for Monitoring Brushing BehaviorsProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)10.1007/978-3-031-77571-0_12(112-117)Online publication date: 21-Dec-2024
  • (2023)3D Monitoring of Toothbrushing Regions and Force Using Multimodal Sensors and UnityIEEE Access10.1109/ACCESS.2023.330963811(94753-94771)Online publication date: 2023
  • (2021)Do You Brush Your Teeth Properly? An Off-body Sensor-based Approach for Toothbrushing Monitoring2021 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH52753.2021.00018(59-69)Online publication date: Sep-2021

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