Adaptive activity learning with dynamically available context

Wen, Jiahui, Indulska, Jadwiga and Zhong, Mingyang (2016). Adaptive activity learning with dynamically available context. In: IEEE International Conference on Pervasive Computing and Communications: PerCom 2016. International Conference on Pervasive Computing and Communications, Sydney, Australia, (). 14-18 March 2016. doi:10.1109/PERCOM.2016.7456502


Author Wen, Jiahui
Indulska, Jadwiga
Zhong, Mingyang
Title of paper Adaptive activity learning with dynamically available context
Conference name International Conference on Pervasive Computing and Communications
Conference location Sydney, Australia
Conference dates 14-18 March 2016
Convener IEEE
Proceedings title IEEE International Conference on Pervasive Computing and Communications: PerCom 2016
Journal name 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2016
Sub-type Fully published paper
DOI 10.1109/PERCOM.2016.7456502
Open Access Status Not Open Access
ISBN 9781467387798
Total pages 11
Collection year 2017
Language eng
Abstract/Summary 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.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Mon, 16 May 2016, 19:28:31 EST by Jiahui Wen on behalf of School of Information Technol and Elec Engineering