Spatio-Temporal Reasoning and Context Awareness

  • Hans W. Guesgen
  • Stephen Marsland


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.


Hide Markov Model Smart Home Ambient Intelligence Context Awareness Novelty Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Agrawal R, Srikant R (1995) Mining sequential patterns. In: Yu P, Chen A (eds) Proceedings of the Eleventh International Conference on Data Engineering, pp 3–14Google Scholar
  2. [2]
    Allen J (1983) Maintaining knowledge about temporal intervals. Communications of the ACM 26:832–843MATHCrossRefGoogle Scholar
  3. [3]
    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 778–782Google Scholar
  4. [4]
    Balbiani P, Condotta JF, del Cerro L (1998) A model for reasoning about bidimensional temporal relations. In: Proc. KR-98, Trento, Italy, pp 124–130Google Scholar
  5. [5]
    de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (1997) Computational Geometry. SpringerGoogle Scholar
  6. [6]
    Bhatt M, Loke S (2008) Modelling dynamic spatial systems in the situation calculus. Spatial Cognition and Computation 8(1&2):86–130Google Scholar
  7. [7]
    Bishop C (1995) Neural Networks for Pattern Recognition. Clarendon Press, OxfordGoogle Scholar
  8. [8]
    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, MAGoogle Scholar
  9. [9]
    Cook D (2006) Health monitoring and assistance to support aging in place. Journal of Universal Computer Science 12(1):15–29Google Scholar
  10. [10]
    Egenhofer M, Franzosa R (1991) Point-set topological spatial relations. International Journal of Geographical Information Systems 5(2):161–174CrossRefGoogle Scholar
  11. [11]
    Elman J (1990) Finding structure in time. Cognitive Science 14:179–211CrossRefGoogle Scholar
  12. [12]
    Fine S, Singer Y, Tishby N (1998) The hierarchical hidden markov model: Analysis and applications. Machine Learning 32:41–62MATHCrossRefGoogle Scholar
  13. [13]
    Gopalratnam K, Cook D (2004) Active LeZi: An incremental parsing algorithm for sequential prediction. International Journal of Artificial Intelligence Tools 14(1–2):917–930Google Scholar
  14. [14]
    Guesgen H (1989) Spatial reasoning based on Allen’s temporal logic. Technical Report TR-89-049, ICSI, Berkeley, CaliforniaGoogle Scholar
  15. [15]
    Han J, Pei J, Mao R (2004) Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8:53–87CrossRefMathSciNetGoogle Scholar
  16. [16]
    Heierman E, Cook D (2003) Improving home automation by discovering regularly occurring device usage patterns. In: Proceedings of the International Conference on Data MiningGoogle Scholar
  17. [17]
    Jakkula V, Cook D (2008) Anomaly detection using temporal data mining in a smart home environment. Methods of Information in MedicineGoogle Scholar
  18. [18]
    Jordan M (ed) (1999) Learning in Graphical Models. The MIT Press, Cambridge, MAGoogle Scholar
  19. [19]
    Kalman R (1960) A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82:34–45Google Scholar
  20. [20]
    Kiecolt-Glaser J, Glaser R (1999) Chronic stress and mortality among older adults. Journal of the American Medical Association 282:2259–2260CrossRefGoogle Scholar
  21. [21]
    Kohonen T (1993) Self-Organization and Associative Memory, 3rd edn. Springer, BerlinGoogle Scholar
  22. [22]
    Kohonen T, Oja E (1976) Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements. Biological Cybernetics 25:85–95CrossRefMathSciNetGoogle Scholar
  23. [23]
    Kremer SC (2001) Spatiotemporal connectionist networks: A taxonomy and review. Neural Computation 13:249–306MATHCrossRefGoogle Scholar
  24. [24]
    Magnusson M (2000) Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods, Instruments, and Computers 32(1):93–110Google Scholar
  25. [25]
    Marsland S (2003) Novelty detection in learning systems. Neural Computing Surveys 3:157–195Google Scholar
  26. [26]
    Marsland S (2009) Machine Learning: An Algorithmic Perspective. CRC Press, Boca Raton, FloridaGoogle Scholar
  27. [27]
    Marsland S (2009) Using habituation in machine learning. Neurobiology of Learning and Memory In pressGoogle Scholar
  28. [28]
    Marsland S, Shapiro J, Nehmzow U (2002) A self-organising network that grows when required. Neural Networks 15(8-9):1041–1058CrossRefGoogle Scholar
  29. [29]
    Martin G (1998) Human Neuropsychology. Prentice Hall, London, EnglandGoogle Scholar
  30. [30]
    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 463–502Google Scholar
  31. [31]
    Mozer M (2005) Lessons from an adaptive house. In: Cook D, Das R (eds) Smart environments: Technologies, protocols, and applications, pp 273–294Google Scholar
  32. [32]
    Mukerjee A, Joe G (1990) A qualitative model for space. In: Proc. AAAI-90, Boston, Massachusetts, pp 721–727Google Scholar
  33. [33]
    Newell A, Simon H (1976) Computer science as empirical inquiry: Symbols and search. Communications of the ACM 19(3):113–126CrossRefMathSciNetGoogle Scholar
  34. [34]
    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 955–960Google Scholar
  35. [35]
    O’Keefe J, Nadel L (1977) The Hippocampus as a Cognitive Map. Oxford University Press, Oxford, UKGoogle Scholar
  36. [36]
    Preparata F, Shamos M (1985) Computational Geometry: An Introduction. Springer, Berlin, GermanyGoogle Scholar
  37. [37]
    Quinn M, Johnson M, Andress E, McGinnis P, Ramesh M (1999) Health characteristics of elderly personal care home residents. Journal of Advanced Nursing 30:410–417CrossRefGoogle Scholar
  38. [38]
    Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2):257–286CrossRefGoogle Scholar
  39. [39]
    Randell D, Cui Z, Cohn A (1992) A spatial logic based on regions and connection. In: Proc. KR-92, Cambridge, Massachusetts, pp 165–176Google Scholar
  40. [40]
    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 pressGoogle Scholar
  41. [41]
    Rissanen J (1989) Stochastic Complexity in Statistical inquiry. World Scientific Publishing CompanyGoogle Scholar
  42. [42]
    Rivera-Illingworth F, Callaghan V, Hagras H (2007) Detection of normal and novel behaviours in ubiquitous domestic environments. The Computer JournalGoogle Scholar
  43. [43]
    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 318–362Google Scholar
  44. [44]
    Stanley J (1976) Computer simulation of a model of habituation. Nature 261:146–148CrossRefGoogle Scholar
  45. [45]
    Stein RB (1967) The frequency of nerve action potentials generated by applied currents. Proceedings of the Royal Society, B 167:64–86CrossRefGoogle Scholar
  46. [46]
    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 8:693–723Google Scholar
  47. [47]
    Sutton R, Barto A (1998) Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MAGoogle Scholar
  48. [48]
    Tavenard R, Salah A, Pauwels E (2007) Searching for temporal patterns in ami sensor data. In: Proceedings of AmI2007, pp 53–62Google Scholar
  49. [49]
    Thompson R, Spencer W (1966) Habituation: A model phenomenon for the study of neuronal substrates of behavior. Psychological Review 73(1):16–43CrossRefGoogle Scholar
  50. [50]
    Vilain M, Kautz H (1986) Constraint propagation algorithms for temporal reasoning. In: Proc. AAAI-86, Philadelphia, Pennsylvania, pp 377–382Google Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand
  2. 2.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

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