Automatic recognition of daily living activities based on a hierarchical classifier

Authors:
Oresti Banos;Miguel Damas;Hector Pomares;Ignacio Rojas
Affiliations:
Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

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Abstract

Physical activity recognition has become an increasing research area specially on health-related fields. The amount of different postures, movements and exercises in addition to the difficulty of the individuals particular execution style determine that extremely robust efficient knowledge inference systems are extremely necessary, being classification process one of the most crucial steps. Considering the power of binary classification in contrast to direct multiclass approaches, and the capabilities offered by multi-sense environments, we define a novel classification schema based on hierarchical structures composed by weighted decision makers. Remarkable accuracy results are obtained for a particular activity recognition problem in contrast to a traditional multiclass majority voting algorithm.