Experience sampling for building predictive user models: a comparative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Extending the growing neural gas classifier for context recognition
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Online labelling strategies for growing neural gas
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Centinela: A human activity recognition system based on acceleration and vital sign data
Pervasive and Mobile Computing
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Activity recognition has recently gained a lot of interest and there already exist several methods to detect human activites based on wearable sensors. Most of the existing methods rely on a database of labelled activities that is used to train an offline activity recognition system. This paper presents an approach to build an online activity recognition system that do not require any a priori labelled data. The system incrementally learns activities by actively querying the user for labels. To choose when the user should be queried, we compare a method based on random sampling and another that uses a Growing Neural Gas (GNG). The use of GNG helps reducing the number of user queries by 20% to 30%.