Abstract
Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naïve Bayes classifier and J48 decision tree.
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References
Aamodt A (2004) Knowledge-intensive case-based reasoning in CREEK Advances in Case-Based Reasoning. Springer, New York, pp 1–15
Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59
Agostini A, Bettini C, Riboni D (2005) Loosely coupling ontological reasoning with an efficient middleware for context-awareness, MobiQuitous. In: Mobile and ubiquitous systems: networking and services. IEEE Computer Society, pp 175–182
Antos K, Jezek B, Vanek J (2009) Crisis health care management system concept with ambient intelligence applied. In: Cech P, Bures V, Nerudová L (eds) AMIF. Ambient intelligence and smart environments, vol 5. IOS, Amsterdam, pp 67–75
Augusto JC, Liu J, McCullagh P, Wang H, Yan JB (2008) Management of uncertainty and spatio-temporal aspects for monitoring and diagnosis in a smart home. Int J Comput Intell Syst 1(4):361–378
Benerecetti M, Bouquet P, Bonifacio M (2001) Distributed context-aware systems. Hum Comput Interact 16(2/4):213–228
Bikakis A, Patkos T, Antoniou G, Plexousakism D (2008) A survey of semantics-based approaches for context reasoning in ambient intelligence. In: Constructing Ambient Intelligence, pp 14–23. Springer, Berlin
Chen L, Nugent CD (2009) Ontology-based activity recognition in intelligent pervasive environments. IJWIS 5(4):410–430
Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: technologies, applications, and opportunities. Pervasive Mobile Comput 5(4):277–298
Corchado JM, Bajo J, Tapia DI, Abraham A (2010) Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare. IEEE Trans Info Tech Biomed 14(2):234–240
Dey KA, Abowd DG, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum Comput Interact 16(2/4):97–166
Dezert J, Tchamova A (2011) On the behavior of Dempster’s rule of combination. http://hal.archives-ouvertes.fr/hal-00577983/en/
Filippaki C, Antoniou G, Tsamardinos I (2011) Using constraint optimization for conflict resolution and detail control in activity recognition. In: Ambient Intelligence. Springer, Berlin, pp 51–60
Gandon FL, Sadeh NM (2004) Semantic web technologies to reconcile privacy and context awareness. J Web Sem 1(3):241–260
Gu T, Pung HK, Zhang D (2004) Toward an OSGi-based infrastructure for context-aware applications. IEEE Pervasive Comput 3(4):66–74
Gu T, Pung HK, Zhang DQ (2004) A Bayesian approach for dealing with uncertain contexts. Austrian Computer Society, Vienna
Gu T, Wang XH, Pung HK, Zhang DQ (2004) An ontology-based context model in intelligent environments. In: Proceedings of communication networks and distributed systems modeling and simulation conference pp 270–275
Guan D, Yuan W, Gavrilov A, Lee S, Lee YK, Han S (2006) Using fuzzy decision tree to handle uncertainty in context deduction. In Computational Intelligence. Springer, Berlin, pp 63–72
Henricksen K, Indulska J (2006) Developing context-aware pervasive computing applications: models and approach. Pervasive Mobile Comput 2(1):37–64
Hong IJ, Landay AJ (2001) An infrastructure approach to context-aware computing. Hum Comput Interact 16(2/4):287–303
Hong X, Nugent CD, Mulvenna MD, McClean SI, Scotney BW, Devlin S (2009) Evidential fusion of sensor data for activity recognition in smart homes. Pervasive Mobile Comput 5(3):236–252
Hu DH, Zheng VW, Yang Q (2011) Cross-domain activity recognition via transfer learning. Pervasive Mobile Comput 7(3):344–358
Knox S, Coyle L, Dobson S (2010) Using ontologies in case-based activity recognition. FLAIRS conference. AAAI, Palo Alto
Kofod-Petersen A (2006) Challenges in case-based reasoning for context awareness in ambient intelligent systems. In: 8th European Conference on Case-Based Reasoning workshop proceedings, Turkey, 287–299
Kofod-Petersen A, Aamodt A (2009) Case-based reasoning for situation-aware ambient intelligence: a hospital ward evaluation study. In: ICCBR. Lecture notes in computer science, vol 5650. Springer, New York, pp 450–464 McGinty L, Wilson DC (eds)
Loke SW (2004) Logic programming for context-aware pervasive computing: language support, characterizing situations, and integration with the web. In: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, pp 44–50
Lowrance H, Garvey T, Strat T (1986) A framework for evidential-reasoning systems. In: Kehler TRS (ed) Proceedings of the 5th national conference on artificial intelligence, vol 2. Morgan Kaufmann, Philadelphia, pp 896–903
Lu CH, Fu LC (2009) Robust location-aware activity recognition using wireless sensor network in an attentive home. IEEE Trans Automat Sci Eng 6(4):598–609
Mal-Sarkar S, Sikder IU, Yu C, Konangi VK (2009) Uncertainty-aware wireless sensor networks. IJMC 7(3):330–345
McKeever S, Ye J, Coyle L, Bleakley CJ, Dobson S (2010) Activity recognition using temporal evidence theory. JAISE 2(3):253–269
Murphy CK (2000) Combining belief functions when evidence conflicts. Decis Support Syst 29(1):1–9
Ranganathan A, Al-Muhtadi J, Campbell RH (2004) Reasoning about uncertain contexts in pervasive computing environments. IEEE Pervasive Comput 3(2):62–70
Rashid J (2010) Towards the development of an ubiquitous networked robot systems for ambient assisted living. In:SUTC/UMC. IEEE Computer Society, pp 359–366
Román M, Hess C, Cerqueira R, Ranganathan A, Campbell RH, Nahrstedt K (2002) A middleware infrastructure for active spaces. IEEE Pervasive Comput 1(4):74–83
Sarkar AJ, Lee YK, Lee S (2010) A smoothed naive Bayes-based classifier for activity recognition. IETE Tech Rev 27(2):107–119
Sentz K, Ferson S (2002) Combination of evidence in Dempster Shafer theory. Technical report SAND2002-0835. Sandia National Laboratories Albuquerque
Shafer G (1976) A mathematical theory of evidence. Princeton University press, Princeton
Stanford V (2002) Using pervasive computing to deliver elder care. IEEE Pervasive Comput 1:10–13
Storf H, Becker M, Riedl M (2009) Rule-based activity recognition framework: challenges, technique and learning. In: Third international conference on pervasive computing technologies for healthcare. IEEE, pp 1–7
Strat TM (1987) The generation of explanations within evidential reasoning systems. In: IJCAI, pp 1097–1104
Tapia DI, Corchado JM (2009) An ambient intelligence based multi-agent system for Alzheimer health care. IJACI 1(1):15–26
Toninelli A, Montanari R, Kagal L, Lassila O (2006) A semantic context-aware access control framework for secure collaborations in pervasive computing environments. In: International semantic web conference. Lecture notes in computer science, vol 4273. Springer, Berlin, pp 473–486
van Kasteren TV, Noulas AK, Englebienne G, Kröse BJA (2008) Accurate activity recognition in a home setting. In: UbiComp, ACM international conference proceeding series, vol 344. ACM, New York, pp 1–9
Voorbraak F (1991) On the justification of Dempster’s rule of combination. Artif Intell 48:171–197
Wang L, Gu T, Tao X, Lu J (2009) Sensor-based human activity recognition in a multi-user scenario. Ambient Intelligence. Springer, New York, pp 78–87
Wu H (2003) Sensor fusion for context-aware computing using Dempster-Shafer theory. CMU Robotics Institute, Pittsburgh
Yang Y, Calmet J (2005) Ontobayes: an ontology-driven uncertainty model. In: CIMCA/IAWTIC. IEEE Computer Society, pp 457–463
Ye J (2009) Exploiting semantics with situation lattices in pervasive computing. PhD thesis, University College Dublin
Ye J, Clear AK, Coyle L, Dobson S (2009) On using temporal features to create more accurate human-activity classifiers. Artificial Intelligence and Cognitive Science. Springer, New York, pp 273–282
Zadeh L (1979) On the validity of Dempster’s rule of combination of evidence. Memorandum UCB/ERL-M. Electronics Research Laboratory, Univ. of California
Zhang D, Guo M, Zhou J, Kang D, Cao J (2010) Context reasoning using extended evidence theory in pervasive computing environments. Futur Gener Comp Syst 26(2):207–216
Zimmermann A (2003) Context-awareness in user modelling: Requirements analysis for a case-based reasoning application. Case-Based Reasoning Research and Development. Springer, New York, pp 718–732
Acknowledgments
The authors would like to thank Dr. Susan McKeever for her help and for providing us source codes that we have used for comparison purposes. Additionally, the authors thank the anonymous reviewers for their useful comments and suggestions, which are very helpful to improve the technical quality of this paper.
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Sebbak, F., Benhammadi, F., Chibani, A. et al. Dempster–Shafer theory-based human activity recognition in smart home environments. Ann. Telecommun. 69, 171–184 (2014). https://doi.org/10.1007/s12243-013-0407-2
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DOI: https://doi.org/10.1007/s12243-013-0407-2