Abstract
Research has not found its way yet to the track cycling madison discipline. Currently, training files are collected from cycling computers, after which the data is interpreted in a mainly subjective manner, based on the domain knowledge of a coach. The goal of this paper is twofold. Starting with the automated detection of madison handslings from cadence, acceleration and gyroscope data, all other data corresponding to a single handsling can easily be obtained. The second goal concerns the calculation of statistics on rider performances during a handsling. We present two madison handsling performance assessment use cases. The first use case exposes imbalances within a madison rider pair, whereas the second use case employs power data to monitor the effort a single rider puts into the handsling.
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Steyaert, M., De Bock, J., Verstockt, S. (2022). Sensor-Based Performance Monitoring in Track Cycling. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds) Machine Learning and Data Mining for Sports Analytics. MLSA 2021. Communications in Computer and Information Science, vol 1571. Springer, Cham. https://doi.org/10.1007/978-3-031-02044-5_14
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DOI: https://doi.org/10.1007/978-3-031-02044-5_14
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