Abstract
This paper investigates the combined use of ambient and wearable sensing for inferring changes in patient behaviour patterns. It has been demonstrated that with the use of wearable and blob based ambient sensors, it is possible to develop an effective visualization framework allowing the observation of daily activities in a homecare environment. An effective behaviour modelling method based on Hidden Markov Models (HMMs) has been proposed for highlighting changes in activity patterns. This allows for the representation of sequences in a similarity space that can be used for clustering or data-exploration.
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References
Intille, S.S., et al. Using a live-in laboratory for ubiquitous computing research. in PERVASIVE 2006. Berlin Heidelberg: Springer-Verlag.
Edwards, N., et al., Life-Style Monitoring for Supported Independence. BT Technology Journal, 2000. 18(1): p. 64–65.
Tamura, T. A Smart Home for Emergencies in the Elderly. in ICOST 2006. 2006. Belfast: IOS Press.
Pansiot, J., et al. Towards image-based modeling for ambient sensing. in BSN. 2006.
Lo, B., et al., Real-Time Pervasive Monitoring for Postoperative Care, in BSN 2007. 2007: Aachen.
Yang, G.-Z., et al. From Sensor Networks to Behaviour Profiling: A Homecare Perspective of Intelligent Building. in The IEE Seminar for Intelligent Buildings. 2004: IEE.
Lo, B. and G.-Z. Yang. Architecture for Body Sensor Networks. in The Perspective in Pervasive Computing. 2005. IEE Savoy Place: IEE.
Aziz, O., B. Lo, and G.Z. Yang. Pervasive Body Sensor Network: An Approach to Monitoring the Post-operative Surgical Patient. in BSN 2006.
Oliver, N.M., et al. A comparison of HMMs and dynamic bayesian networks for recognizing office activities. in UM 2005 2005. Edinburgh: Springer, Berlin.
Oliver, N.M., B. Rosario, and A.P. Pentland, A Bayesian computer vision system for modeling human interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2000. 22(8): p. 831–843.
Liao, L., D. Fox, and H. Kautz, Location-based activity recognition using relational Markov networks, in Proc. of the International Joint Conference on Artificial Intelligence. 2005.
Bicego, M., V. Murino, and M.A.T. Figueiredo, Similaritybased classification of sequences using hidden Markov models. Pattern Recognition, 2004. 37(12): p. 2281–2291.
Bicego, M., V. Murino, and M. Figuerido. Similarity based clustering of sequences using Hidden Markov Models. in MLDM. 2003: Springer-Verlag.
Rabiner, L. and B. Juang, An introduction to hidden Markov models. ASSP Magazine, IEEE [see also IEEE Signal Processing Magazine], 1986. 3(1): p. 4–16.
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© 2007 International Federation for Medical and Biological Engineering
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Atallah, L. et al. (2007). Behaviour Profiling with Ambient and Wearable Sensing. In: Leonhardt, S., Falck, T., Mähönen, P. (eds) 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007). IFMBE Proceedings, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70994-7_23
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DOI: https://doi.org/10.1007/978-3-540-70994-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70993-0
Online ISBN: 978-3-540-70994-7
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