Automatic sensor-based detection and classification of climbing activities - Archive ouverte HAL Access content directly
Journal Articles IEEE Sensors Journal Year : 2016

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.
Fichier principal
Vignette du fichier
Affordance detection.pdf (1.13 Mo) Télécharger le fichier
Automatic sensor-based detection and classification of climbing activities.pdf (2.43 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01225056 , version 1 (09-11-2015)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

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⟩
288 View
928 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More