@inproceedings{7713bd8c82e945e19063f281d29f4ff5,
title = "Toothbrushing data and analysis of its potential use in human activity recognition applications",
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.",
keywords = "activity recognition, machine learning, off-body sensors, smart toothbrush",
author = "Zawar Hussain and David Waterworth and Murtadha Aldeer and Zhang, \{Wei Emma\} and Sheng, \{Quan Z.\}",
year = "2020",
doi = "10.1145/3419016.3431489",
language = "English",
series = "DATA 2020 - Proceedings of the 3rd Workshop on Data Acquisition To Analysis, Part of SenSys 2020, BuildSys 2020",
publisher = "Association for Computing Machinery, Inc",
pages = "31--34",
booktitle = "DATA 2020 - Proceedings of the 3rd Workshop on Data Acquisition To Analysis",
note = "3rd Workshop on Data Acquisition To Analysis, DATA 2020 - Part of SenSys 2020, BuildSys 2020 ; Conference date: 16-11-2020 Through 19-11-2020",
}