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.
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Banos, O., Damas, M., Pomares, H., Rojas, I. (2011). Automatic Recognition of Daily Living Activities Based on a Hierarchical Classifier. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_24
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DOI: https://doi.org/10.1007/978-3-642-21498-1_24
Publisher Name: Springer, Berlin, Heidelberg
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