Projects per year
Abstract
Maintaining oral hygiene is very important for a healthy life. Poor toothbrushing is one of the leading causes of tooth decay and other gum problems. Many people do not brush their teeth properly. There is very limited technology available to help in assessing the quality of toothbrushing. Human Activity Recognition (HAR) applications have seen a tremendous growth in recent years. In this work, we treat the adherence to standard toothbrushing practice as an activity recognition problem. We investigate this problem and collect experimental data using a brush-attached and a wearable sensor when the users brush their teeth. In this paper, we extend our previous dataset [1] for toothbrushing activity by including more experiments and adding a new sensor. We discuss and analyse the collection of the dataset. We use an Inertial Measurement Unit (IMU) sensor to collect the time-series data for toothbrushing activity. We recruited 22 healthy participants and collected the data in two different settings when they brushed their teeth in five different locations using both electric and manual brushes. In total, we have recorded 120 toothbrushing sessions using both brush-attached sensor and the wearable sensor.
Original language | English |
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Article number | 107248 |
Pages (from-to) | 1-7 |
Number of pages | 7 |
Journal | Data in Brief |
Volume | 37 |
DOIs | |
Publication status | Published - Aug 2021 |
Bibliographical note
Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Activity recognition
- Machine learning
- Sensor
- Smart toothbrush
- Toothbrushing
Fingerprint
Dive into the research topics of 'Dataset for toothbrushing activity using brush-attached and wearable sensors'. Together they form a unique fingerprint.Projects
- 3 Finished
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UNSW led : Robust Preference Inference from Spatial-Temporal Interaction Networks
Yao, L., Sheng, M. & Benatallah, B.
1/04/21 → 31/03/24
Project: Research
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UNSW led: Context and Activity Recognition for Personalised Behaviour Recommendation
Yao, L., Benatallah, B., Sheng, M. & Zhang, Y.
7/07/20 → 6/07/23
Project: Research
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A Large-Scale Distributed Experimental Facility for the Internet of Things
Sheng, M., Bouguettaya, A., Loke, S., Li, X., Liang, W., Benattalah, B., Ali Babar, M., Yang, J., Zomaya, A. Y., Wang, Y., Zhou, W., Yao, L., Taylor, K. & Bergmann, N.
1/01/18 → 31/12/20
Project: Research