Automatic sensor-based detection and classification of climbing activities

Jérémie Boulanger (1) , Ludovic Seifert (2) , Romain Hérault (3, 4, 5, 6) , Jean-François Coeurjolly (7)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 CETAPS - Centre d’études des transformations des activités physiques et sportives
3 INSA Rouen Normandie - Institut national des sciences appliquées Rouen Normandie
4 DocApp - LITIS - Equipe Apprentissage
5 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
6 NU - Normandie Université
7 FIGAL - Fiabilité et Géométrie Aléatoire

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.

Domaines: Traitement du signal et de l'image [eess.SP]

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