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.
Domains
Signal and Image processing
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Affordance detection.pdf (1.13 Mo)
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Automatic sensor-based detection and classification of climbing activities.pdf (2.43 Mo)
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Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
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