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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.

Original languageEnglish
Title of host publicationDATA 2020 - Proceedings of the 3rd Workshop on Data Acquisition To Analysis
Subtitle of host publicationPart of SenSys 2020, BuildSys 2020
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages31-34
Number of pages4
ISBN (Electronic)9781450381369
DOIs
Publication statusPublished - 2020
Event3rd Workshop on Data Acquisition To Analysis, DATA 2020 - Part of SenSys 2020, BuildSys 2020 - Virtual, Online, Japan
Duration: 16 Nov 202019 Nov 2020

Publication series

NameDATA 2020 - Proceedings of the 3rd Workshop on Data Acquisition To Analysis, Part of SenSys 2020, BuildSys 2020

Conference

Conference3rd Workshop on Data Acquisition To Analysis, DATA 2020 - Part of SenSys 2020, BuildSys 2020
CountryJapan
CityVirtual, Online
Period16/11/2019/11/20

Keywords

  • activity recognition
  • machine learning
  • off-body sensors
  • smart toothbrush

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