Dataset for toothbrushing activity using brush-attached and wearable sensors
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 Act...
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doaj-228e7665ce7f49c894c008b3e96e10352021-08-26T04:35:06ZengElsevierData in Brief2352-34092021-08-0137107248Dataset for toothbrushing activity using brush-attached and wearable sensorsZawar Hussain0David Waterworth1Adnan Mahmood2Quan Z. Sheng3Wei Emma Zhang4Corresponding author.; Department of Computing, Macquarie University, Sydney, AustraliaDepartment of Computing, Macquarie University, Sydney, AustraliaDepartment of Computing, Macquarie University, Sydney, AustraliaDepartment of Computing, Macquarie University, Sydney, AustraliaSchool of Computer Science, The University of Adelaide, AustraliaMaintaining 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.http://www.sciencedirect.com/science/article/pii/S2352340921005321ToothbrushingSmart toothbrushActivity recognitionMachine learningSensor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zawar Hussain David Waterworth Adnan Mahmood Quan Z. Sheng Wei Emma Zhang |
spellingShingle |
Zawar Hussain David Waterworth Adnan Mahmood Quan Z. Sheng Wei Emma Zhang Dataset for toothbrushing activity using brush-attached and wearable sensors Data in Brief Toothbrushing Smart toothbrush Activity recognition Machine learning Sensor |
author_facet |
Zawar Hussain David Waterworth Adnan Mahmood Quan Z. Sheng Wei Emma Zhang |
author_sort |
Zawar Hussain |
title |
Dataset for toothbrushing activity using brush-attached and wearable sensors |
title_short |
Dataset for toothbrushing activity using brush-attached and wearable sensors |
title_full |
Dataset for toothbrushing activity using brush-attached and wearable sensors |
title_fullStr |
Dataset for toothbrushing activity using brush-attached and wearable sensors |
title_full_unstemmed |
Dataset for toothbrushing activity using brush-attached and wearable sensors |
title_sort |
dataset for toothbrushing activity using brush-attached and wearable sensors |
publisher |
Elsevier |
series |
Data in Brief |
issn |
2352-3409 |
publishDate |
2021-08-01 |
description |
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. |
topic |
Toothbrushing Smart toothbrush Activity recognition Machine learning Sensor |
url |
http://www.sciencedirect.com/science/article/pii/S2352340921005321 |
work_keys_str_mv |
AT zawarhussain datasetfortoothbrushingactivityusingbrushattachedandwearablesensors AT davidwaterworth datasetfortoothbrushingactivityusingbrushattachedandwearablesensors AT adnanmahmood datasetfortoothbrushingactivityusingbrushattachedandwearablesensors AT quanzsheng datasetfortoothbrushingactivityusingbrushattachedandwearablesensors AT weiemmazhang datasetfortoothbrushingactivityusingbrushattachedandwearablesensors |
_version_ |
1721196068602904576 |