Paper Info
Title
Toothbrushing data and analysis of its potential use in human activity recognition applications: dataset
Abstract
ABSTRACTIn 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.
Year
DOI
Venue
2020
10.1145/3419016.3431489
SENSYS
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
5
5
Name
Order
Citations
PageRank
Zawar Hussain151.49
David Waterworth200.68
Murtadha Aldeer302.70
Wei Emma Zhang400.34
Quan Z. Sheng53520301.77