A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

Attila Reiss; Gustaf Hendeby; Didier Stricker

In: Gail R. Casper; Anna M. McDaniel; Daniel Gatica-Perez; Daniel Roggen; Masaaki Fukumoto. Personal and Ubiquitous Computing. Pages 105-121, ISBN 1617-4917, Springer, 9/2014.


This paper addresses one of the main challenges in physical activity monitoring, as indicated by recent benchmark results: The difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. This algorithm is a variant of the AdaBoost.M1 that incorporates well-established ideas for confidence-based boosting. ConfAdaBoost.M1 is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository. Moreover, it is evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm signifi- cantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks. Finally, two empirical studies are designed and carried out to investigate the feasibility of ConfAdaBoost.M1 for physical activity monitoring applications in mobile systems.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence