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Michael_2017_Activity_recognition_dataset.zip (8.34 MB)

Activity Recognition with Wearable Sensors on Loose Clothing

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posted on 2021-09-13, 18:33 authored by Brendan Michael
This research (DOI:10.1371/journal.pone.0184642) investigates if motion artefacts inherent in fabric-based sensing systems (e-textiles), can be exploited as an additional source of information in statistical classification tasks. The findings in the corresponding paper suggest that these artefacts can, in fact, be used to distinguish between similar motions, by exploiting additional information provided by the fabric motion. An experimental study is presented whereby factors of both the motion and the properties of the fabric are analysed in the context of motion similarity. The dataset here contains 2-axis acceleration readings from sensorised fabric during a motion task. For more information please see the readme file in the dataset. Related Publication Michael, Brendan and Howard, Matthew, "Activity Recognition with Wearable Sensors on Loose Clothing", PLoS One, 2017, Accepted (DOI:10.1371/journal.pone.0184642)

History

Data collection from date

2016-10-16

Data collection to date

2016-10-16

Collection method

Sensed acceleration data was collected from tri-axel inertial measurement units, streaming to a microcontroller. For full details, see corresponding paper, DOI 10.1371/journal.pone.0184642

Language

English

Copyright owner

Brendan Michael, King's College London

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    Faculty of Natural, Mathematical & Engineering Sciences

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