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2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
Sydney, Australia
March 14, 2016 to March 19, 2016
ISBN: 978-1-4673-8778-1
pp: 1-11
Jiahui Wen , The University of Queensland, Australia School of Information Technology and Electrical Engineering, National ICT Australia (NICTA)
Jadwiga Indulska , The University of Queensland, Australia School of Information Technology and Electrical Engineering, National ICT Australia (NICTA)
Mingyang Zhong , The University of Queensland, Australia School of Information Technology and Electrical Engineering, National ICT Australia (NICTA)
ABSTRACT
Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.
INDEX TERMS
Context, Context modeling, Adaptation models, Sensor systems, Hidden Markov models, Data models
CITATION

J. Wen, J. Indulska and M. Zhong, "Adaptive activity learning with dynamically available context," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-11.
doi:10.1109/PERCOM.2016.7456502
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