Abstract
Hidden Markov Models (HMMs) are widely used in activity recognition. Ideally, the current activity should be determined using the vector of all sensor readings; however, this results in an exponentially large space of observations. The current fix to this problem is to assume conditional independence between individual sensors, given an activity, and factorizing the emission distribution in a naive way. In several cases, this leads to accuracy loss. We present an intermediate solution, viz., determining a mapping between each activity and conjunctions over a relevant subset of dependent sensors. The approach discovers features that are conjunctions of sensors and maps them to activities. This does away the assumption of naive factorization while not ruling out the possibility of the vector of all the sensor readings being relevant to activities. We demonstrate through experimental evaluation that our approach prunes potentially irrelevant subsets of sensor readings and results in significant accuracy improvements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wilson, D.H.: Assistive Intelligent Environments for Automatic Health Monitoring. PhD Thesis, Carnegie Mellon University (2005)
Gibson, C.H.S., van Kasteren, T.L.M., Krose, B.J.A.: Monitoring Homes with Wireless Sensor Networks. In: Proceedings of the International Med-e-Tel Conference (2008)
Wang, S., Pentney, W., Popescu, A.-M., Choudhury, T., Philipose, M.: Common sense based joint training of human activity recognizers. In: 20th International Joint Conference on Artifical Intelligence (2007)
van Kasteren, T., Noulas, A., Englebienne, G., Krose, B.: Accurate activity recognition in a home setting. In: 10th International Conference on Ubiquitous Computing (2008)
Rabiner, R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: International Conference on Machine Learning (2001)
Landwehr, N., Passerini, A., De Raedt, L., Frasconi, P.: KFOIL: Learning Simple Relational Kernels. In: 21st National Conference on Artificial Intelligence (2006)
Gutmann, B., Kersting, K.: TildeCRF: Conditional Random Fields for Logical Sequences. In: 15th European Conference on Machine Learning (2006)
Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F.: Feature Construction for Relational Sequence Learning. Technical Report, arXiv:1006.5188 (2010)
Getoor, L., Taskar, B.: Statistical Relational Learning. MIT Press, Cambridge (2006)
Forney, G.D.: The viterbi algorithm. Proceedings of IEEE 61(3), 268–278 (1973)
Srinivasan, A.: The Aleph Manual. Technical Report, University of Oxford (2007)
Landwehr, N., Gutmann, B., Thon, I., De Raedt, L., Philipose, M.: Relational Transformation-based Tagging for Activity Recognition. Progress on Multi-Relational Data Mining 89(1), 111–129 (2009)
Binsztok, H., Artieres, T., Gallinari, P.: A model-based approach to sequence clustering. In: European Conference on Artificial Intelligence (2004)
McCallum, A.: Efficiently Inducing Features of Conditional Random Fields. In: Nineteenth Conference on Uncertainty in Artificial Intelligence (2003)
Siegel, S.: Nonparametric statistics for the behavioural sciences. McGraw-Hill, New York (1956)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nair, N., Ramakrishnan, G., Krishnaswamy, S. (2011). Enhancing Activity Recognition in Smart Homes Using Feature Induction. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2011. Lecture Notes in Computer Science, vol 6862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23544-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-23544-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23543-6
Online ISBN: 978-3-642-23544-3
eBook Packages: Computer ScienceComputer Science (R0)