Confidence-based multiclass AdaBoost for physical activity monitoring
2013 (English)In: ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers, 2013, 13-20 p.Conference paper (Refereed)
Physical activity monitoring has recently become an important topic in wearable computing, motivated by e.g. healthcare applications. However, new benchmark results show that the difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. The proposed algorithm is a variant of the AdaBoost.M1 that incorporates well established ideas for confidence based boosting. The method is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository and it is also 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.
Place, publisher, year, edition, pages
2013. 13-20 p.
Signal Processing Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-97524DOI: 10.1145/2493988.2494325ISBN: 978-1-4503-2127-3OAI: oai:DiVA.org:liu-97524DiVA: diva2:648293
17th Annual International Symposium on Wearable Computers,Zurich, Switzerland, September 8-12, 2013