Automatic sensor-based detection and ...
Document type :
Article dans une revue scientifique
Title :
Automatic sensor-based detection and classification of climbing activities
Author(s) :
Boulanger, Jérémie [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Seifert, Ludovic [Auteur]
Centre d’études des transformations des activités physiques et sportives [CETAPS]
Hérault, Romain [Auteur]
Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes [LITIS]
Equipe Apprentissage [DocApp - LITIS]
Institut national des sciences appliquées Rouen Normandie [INSA Rouen Normandie]
Coeurjolly, Jean-François [Auteur]
Fiabilité et Géométrie Aléatoire [FIGAL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Seifert, Ludovic [Auteur]
Centre d’études des transformations des activités physiques et sportives [CETAPS]
Hérault, Romain [Auteur]
Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes [LITIS]
Equipe Apprentissage [DocApp - LITIS]
Institut national des sciences appliquées Rouen Normandie [INSA Rouen Normandie]
Coeurjolly, Jean-François [Auteur]
Fiabilité et Géométrie Aléatoire [FIGAL]
Journal title :
IEEE Sensors Journal
Pages :
742-749
Publisher :
Institute of Electrical and Electronics Engineers
Publication date :
2016-02-01
ISSN :
1530-437X
HAL domain(s) :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
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. ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Collections :
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- http://arxiv.org/pdf/1508.04153
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