Automatic Sensor-Based Detection and Classification of Climbing Activities

This paper 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...

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Bibliographic Details
Published in:IEEE sensors journal 2016-02, Vol.16 (3), p.742-749
Main Authors: Boulanger, Jeremie, Seifert, Ludovic, Herault, Romain, Coeurjolly, Jean-Francois
Format: Article
Language:English
Subjects:
Acceleration
Angular velocity
Annotations
Classification
Engineering Sciences
Learning
Machine learning
Magnetic sensors
Manuals
Mathematical models
Pelvis
Protocols
Signal and Image processing
Statistical analysis
Wrist
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Summary:This paper 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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2015.2481511