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Automatic sensor-based detection and classification of climbing activities

Abstract : This article presents a novel application of a machine learning method to automatically detect and classify climbing activities using inertial measurement units (IMUs) attached to the wrists, feet and pelvis of the climber. This detection/classification can be useful for research in sport science to replace manual annotation where IMUs are becoming common. Detection requires a learning phase with manual annotation to construct statistical models. Full-body activity is then classified based on the detection of each IMU.
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https://hal.archives-ouvertes.fr/hal-01225056
Contributor : Jeremie Boulanger Connect in order to contact the contributor
Submitted on : Monday, November 9, 2015 - 2:23:48 PM
Last modification on : Saturday, December 18, 2021 - 3:05:27 AM
Long-term archiving on: : Wednesday, February 10, 2016 - 10:10:40 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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Jérémie Boulanger, Ludovic Seifert, Romain Hérault, Jean-François Coeurjolly. Automatic sensor-based detection and classification of climbing activities. IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2016, 16 (3), pp.742-749. ⟨10.1109/JSEN.2015.2481511⟩. ⟨hal-01225056⟩

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