Personal and Ubiquitous Computing

, Volume 19, Issue 1, pp 105–121 | Cite as

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

Original Article

Abstract

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 significantly 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.

Keywords

Physical activity monitoring Activity recognition Boosting  Multiclass classification Personalization Feasibility study 

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Attila Reiss
    • 1
  • Gustaf Hendeby
    • 2
    • 3
  • Didier Stricker
    • 4
  1. 1.Chair of Sensor TechnologyUniversity of PassauPassauGermany
  2. 2.Department of Electrical EngineeringLinköping UniversityLinköpingSweden
  3. 3.Department of Sensor and EW SystemsSwedish Defence Research Agency (FOI)LinköpingSweden
  4. 4.Department of Augmented VisionGerman Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany

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