Abstract
Smart homes provide many research challenges, but some of the most interesting ones are in dealing with data that monitors human behaviour and that is inherently both spatial and temporal in nature. This means that context becomes all important: a person lying down in front of the fireplace could be perfectly normal behaviour if it was cold and the fire was on, but otherwise it is unusual. In this example, the context can include temporal resolution on various scales (it is winter and therefore probably cold, it is not nighttime when the person would be expected to be in bed rather than the living room) as well as spatial (the person is lying in front of the fireplace) and extra information such as whether or not the fire is lit. It could also include information about how they reached their current situation: if they went from standing to lying very suddenly there would be rather more cause for concern than if they first knelt down and then lowered themselves onto the floor. Representing all of these different temporal and spatial aspects together is a major challenge for smart home research. In this chapter we will provide an overview of some of the methodologies that can be used to deal with these problems. We will also outline our own research agenda in the Massey University Smart Environments (MUSE) group.
Preview
Unable to display preview. Download preview PDF.
References
Agrawal R, Srikant R (1995) Mining sequential patterns. In: Yu P, Chen A (eds) Proceedings of the Eleventh International Conference on Data Engineering, pp 3–14
Allen J (1983) Maintaining knowledge about temporal intervals. Communications of the ACM 26:832–843
Augusto J, Nugent C (2004) The use of temporal reasoning and management of complex events in smart homes. In: Proc. ECAI-04, Valencia, Spain, pp 778–782
Balbiani P, Condotta JF, del Cerro L (1998) A model for reasoning about bidimensional temporal relations. In: Proc. KR-98, Trento, Italy, pp 124–130
de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (1997) Computational Geometry. Springer
Bhatt M, Loke S (2008) Modelling dynamic spatial systems in the situation calculus. Spatial Cognition and Computation 8(1&2):86–130
Bishop C (1995) Neural Networks for Pattern Recognition. Clarendon Press, Oxford
Campbell C, Bennett K (2000) A linear programming approach to novelty detection. In: Leen T, Diettrich T, Tresp V (eds) Proceedings of Advances in Neural Information Processing Systems 13 (NIPS’00), MIT Press, Cambridge, MA
Cook D (2006) Health monitoring and assistance to support aging in place. Journal of Universal Computer Science 12(1):15–29
Egenhofer M, Franzosa R (1991) Point-set topological spatial relations. International Journal of Geographical Information Systems 5(2):161–174
Elman J (1990) Finding structure in time. Cognitive Science 14:179–211
Fine S, Singer Y, Tishby N (1998) The hierarchical hidden markov model: Analysis and applications. Machine Learning 32:41–62
Gopalratnam K, Cook D (2004) Active LeZi: An incremental parsing algorithm for sequential prediction. International Journal of Artificial Intelligence Tools 14(1–2):917–930
Guesgen H (1989) Spatial reasoning based on Allen’s temporal logic. Technical Report TR-89-049, ICSI, Berkeley, California
Han J, Pei J, Mao R (2004) Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8:53–87
Heierman E, Cook D (2003) Improving home automation by discovering regularly occurring device usage patterns. In: Proceedings of the International Conference on Data Mining
Jakkula V, Cook D (2008) Anomaly detection using temporal data mining in a smart home environment. Methods of Information in Medicine
Jordan M (ed) (1999) Learning in Graphical Models. The MIT Press, Cambridge, MA
Kalman R (1960) A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82:34–45
Kiecolt-Glaser J, Glaser R (1999) Chronic stress and mortality among older adults. Journal of the American Medical Association 282:2259–2260
Kohonen T (1993) Self-Organization and Associative Memory, 3rd edn. Springer, Berlin
Kohonen T, Oja E (1976) Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements. Biological Cybernetics 25:85–95
Kremer SC (2001) Spatiotemporal connectionist networks: A taxonomy and review. Neural Computation 13:249–306
Magnusson M (2000) Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods, Instruments, and Computers 32(1):93–110
Marsland S (2003) Novelty detection in learning systems. Neural Computing Surveys 3:157–195
Marsland S (2009) Machine Learning: An Algorithmic Perspective. CRC Press, Boca Raton, Florida
Marsland S (2009) Using habituation in machine learning. Neurobiology of Learning and Memory In press
Marsland S, Shapiro J, Nehmzow U (2002) A self-organising network that grows when required. Neural Networks 15(8-9):1041–1058
Martin G (1998) Human Neuropsychology. Prentice Hall, London, England
McCarthy J, Hayes P (1969) Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer B, Michie D (eds) Machine Intelligence, vol 4, Edinburgh University Press, Edinburgh, Scotland, pp 463–502
Mozer M (2005) Lessons from an adaptive house. In: Cook D, Das R (eds) Smart environments: Technologies, protocols, and applications, pp 273–294
Mukerjee A, Joe G (1990) A qualitative model for space. In: Proc. AAAI-90, Boston, Massachusetts, pp 721–727
Newell A, Simon H (1976) Computer science as empirical inquiry: Symbols and search. Communications of the ACM 19(3):113–126
Nguyen NT, Phung DQ, Venkatesh S, Bui H (2005) Learning and detecting activities from movement trajectories using the hierarchical hidden markov model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 955–960
O’Keefe J, Nadel L (1977) The Hippocampus as a Cognitive Map. Oxford University Press, Oxford, UK
Preparata F, Shamos M (1985) Computational Geometry: An Introduction. Springer, Berlin, Germany
Quinn M, Johnson M, Andress E, McGinnis P, Ramesh M (1999) Health characteristics of elderly personal care home residents. Journal of Advanced Nursing 30:410–417
Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2):257–286
Randell D, Cui Z, Cohn A (1992) A spatial logic based on regions and connection. In: Proc. KR-92, Cambridge, Massachusetts, pp 165–176
Rankin C, Abrams T, Barry R, Bhatnagar S, Clayton D, Colombo J, Coppola G, Geyer M, Glanzman D, Marsland S, McSweeney F, Wilson D, Wu CF, Thompson R (2009) Habituation revisited: An updated and revised description of the behavioral characteristics of habituation. Neurobiology of Learning and Memory In press
Rissanen J (1989) Stochastic Complexity in Statistical inquiry. World Scientific Publishing Company
Rivera-Illingworth F, Callaghan V, Hagras H (2007) Detection of normal and novel behaviours in ubiquitous domestic environments. The Computer Journal
Rumelhart D, Hinton G, Williams R (1986) Learning internal representations by error propagation. In: Rumelhart D, McClelland JL (eds) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, The MIT Press, Cambridge, MA, vol 1, pp 318–362
Stanley J (1976) Computer simulation of a model of habituation. Nature 261:146–148
Stein RB (1967) The frequency of nerve action potentials generated by applied currents. Proceedings of the Royal Society, B 167:64–86
Sutton CA, Rohanimanesh K, McCallum A (2007) Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. Journal of Machine Learning Research 8:693–723
Sutton R, Barto A (1998) Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MA
Tavenard R, Salah A, Pauwels E (2007) Searching for temporal patterns in ami sensor data. In: Proceedings of AmI2007, pp 53–62
Thompson R, Spencer W (1966) Habituation: A model phenomenon for the study of neuronal substrates of behavior. Psychological Review 73(1):16–43
Vilain M, Kautz H (1986) Constraint propagation algorithms for temporal reasoning. In: Proc. AAAI-86, Philadelphia, Pennsylvania, pp 377–382
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Guesgen, H.W., Marsland, S. (2010). Spatio-Temporal Reasoning and Context Awareness. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds) Handbook of Ambient Intelligence and Smart Environments. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-93808-0_23
Download citation
DOI: https://doi.org/10.1007/978-0-387-93808-0_23
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-93807-3
Online ISBN: 978-0-387-93808-0
eBook Packages: Computer ScienceComputer Science (R0)