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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 6 , ISSUE 1 (February 2013) > List of articles


Sangeeta Mittal * / Krishna Gopal * / S.L. Maskara *

Keywords : Wireless Sensor Networks, Ambient Intelligence, Formal Concept Analysis, Situation Modeling, Activity Recognition, Lattice based Classification.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 1, Pages 403-432, DOI: https://doi.org/10.21307/ijssis-2017-547

License : (CC BY-NC-ND 4.0)

Received Date : 09-October-2012 / Accepted: 10-February-2013 / Published Online: 20-February-2013



Recent advances in acquisition, storage, and transmission of data from sensors in digital format has increased the need of tools to support users effectively in retrieving, understanding, and mining the information contained in such data.Extraction of domain specific actionable information like occurrence of one of the predefined “situations” is desirable. Major difficulties in achieving this extraction are 1) Source of Data, that is, number and type of sensors deployed is highly variable even
for one type of application, 2)Availability of domain specific labeled training data is critical for computation of situations.In this paper, we propose a versatile method based on formal concept analysis to overcome these difficulties in modelingsensor based situations.Our method, making use of contexts as intermediate form of sensors data, works on any number and type of sensors. It is alsoinstance-independent and eliminates need of training, when applied to various instances of similar application.For illustration, we model and perform real time recognition of activity of a person in indoor home environment with ambientsensors.The embedded sensors captureusage and proximity of human beings to objects.We apply the model learnt from one house, foractivity recognition of new persons across different new houses. The recognition results obtained have high precision and recall.

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[1] S.Katz et al, "Progress in development of the index of ADL",The Gerontologist, pp 20-30Vol. 10, Issue 1,1970.
[2] T.Gu, L.Wang, W. Zhanqing,T. Xianping and L. Jian,"A pattern mining approach to sensorbased human activity recognition", IEEE Transactions on Knowledge and Data Engineering, pp 1359-1372, Vol. 23, Issue 9, September 2011.
[3] D.Guan , T.Ma,W.Yuan, Y. Lee , AM JehadSarkar, “Review of Sensor-based Activity Recognition Systems”, pp. 418-33 Vol. 28, Issue 5, IETE Tech Rev,Oct 2011.
[4] A. Bauer and Y. Fischer, “Task-oriented Situation Recognition”, In Proc. of Cyber Security, Situation Management and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II, SPIE Vol. 7709,2010.
[5] J. Ye, S. Dobson and S. McKeever, “Situation Identification Techniques in Pervasive
Computing: A Review”, Pervasive Mobile Computing, pp 36-66, Vol. 8, Issue 1, February 2012.
[6] A. Dahlbom, L. Niklasson,G. Falkman and A. Loutfi, "Towards Template-Based Situation Recognition," in Proc. of Intelligent Sensing, Situation Management, Impact Assessment and Cyber-Sensing, SPIE Vol. 7352, 2009.
[7] C Carpineto, G Romano, “Concept Data Analysis: Theory and Applications”, John Wiley,2004.
[8] T VKasteren, G Englebienne and B Kröse, "Transferring Knowledge of Activity Recognition Across Sensor Networks", In Proceedings of the 8th international conference on Pervasive Computing, pp 283-300, 2010.
[9] S Mittal and S L Maskara, “A Review of Bayesian Belief Network Structure Learning Algorithms”, In Proceedings of 8th International Conference on Information, Communications and Signal Processing (ICICS) , 13 -16 Dec, Singapore.
[10] S Mittal, AAggarwal, S L Maskara, “Application of Bayesian Belief Networks for Context Extraction from Wireless Sensors Data”, 14th International Conference on Advanced Communication Technology (ICACT2012), pp 410 -415, Korea, 2012.
[11] R Cardell-Oliver and Liu Wei, "Representation and Recognition of Situations in Sensor Networks,” IEEE Communications Magazine, Vol.48, Issue 3, pp.112-117, March 2010.
[12] Y Oh, J Han and W Woo, “A context management architecture for large-scale smart environments,” IEEE Communications Magazine, Vol. 48, Issue 3, March 2010
[13] A. Dahlbom, L. Niklasson, G. Falkman and A. Loutfi, "Towards template-based situation recognition," in Proceedings of SPIE on Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing, Vol. 7352. 2009.
[14] J Ye, L Coyle, S Dobson and P Nixon, "Representing and manipulating situation hierarchies using situation lattices,”Revue d'intelligenceartificielle, Vol. 22, Issue 5, pp 647-667, 2008.
[15]K. Thirunarayan, C. Henson and A. Sheth, "Situation awareness via Abductive Reasoning from Semantic Sensor data: A preliminary report," International Symposium on Collaborative Technologies and Systems, pp.111-118, May 2009.
[16] A Srivastav, W Yicheng, E Hendrick,I Chattopadhyay, A Ray and S Phoha,"Information Fusion for Object &Situation Assessment in Sensor Networks," In Proceedings ofAmerican Control Conference (ACC), pp.1274-1279, 2011.
[17] Lu-An Tang, Yu Xiao, Kim Sangkyum et al., “Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach,” International Journal of Distributed Sensor Networks, Vol. 2012, Article ID 724846, 19 pages, 2012.
[18] J Fogarty and S E. Hudson, “Toolkit Support for Developing and Deploying Sensor-Based Statistical Models of Human Situations”, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp 135-144, 2007 , ACM, New York,USA.
[19] S. Yau, D. Huang, H. Gong andY. Yao, “Support for Situation Awareness in Trustworthy UbiquitousComputing Application Software,” Software: Practice and Experience, pp 893-921,Vol. 36, No. 9,2006.
[20] P. Costa, G. Guizzardi, J. Almeida, L. Pires and M. Van Sinderen, “Situations inConceptual Modeling of Context,”In Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference Workshops, pp 6-16, October, 2006.Hong Kong.
[21]S. W. Loke, “Representing and Reasoning with Situations for Context-Aware Pervasive Computing: ALogic Programming Perspective”, Knowledge Engineering Review, pp 213-233,Vol. 19 Issue 3, 2004.
[22]G. Thomson, S.Terzis and P. Nixon, “Situation Determination with Reusable Situation Specifications,” InProceedings of 4th IEEE International Conference on Pervasive Computing and Communications, pp 620-623, March 2006.
[23] T. Emmanuel, S. Intille and K. Larson, "Activity Recognition in the Home Using Simple and Ubiquitous Sensors", In Proceedings of 2nd International Conference on Pervasive Computing in LNCS, Springer, Vol. 3001, pp 158-175,2004.
[24] N. Bicocchi, M. Lasagni andF. Zambonelli, "Bridging Vision and Commonsense forMultimodal Situation Recognition in Pervasive Systems," 12thIEEE International Conference on Pervasive Computing and Communications,pp48-56, March 2012.
[25]L. Atallah et al., “Distributed Inferencing with Ambient and Wearable Sensors”, Wireless Communications and Mobile Computing, pp 117-131, Vol. 12, Issue 1, 2010.
[26] Y. Fischer, A. Bauer and J.Beyerer, "A Conceptual Framework for Automatic Situation Assessment," in Proceedings of First International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp.234-239, 2011.
[27]A. Mahajan, C. Oesch, H. Padmanaban, L. Utterback, S. Chitikeshi and F. Figueroa, “Physical and Virtual Intelligent Sensors for Integrated Health Management Systems”,International Journal on Smart Sensing and Intelligent Systems, pp 559 – 575, Vol. 5, No. 3,September 2012.
[28] B.Ganter and R.Wille, “Formal Concept Analysis: Mathematical Foundations”, Springer Verlag, 1999.
[29] K. Dalkir, “Knowledge Management in Theory and Practice”, The MITPress, 2011.
[30] http://conexp.sourceforge.net/
[31]J. Poelmans et al., "Formal Concept Analysis in Knowledge Discovery: ASurvey", In Proceedings of 18th International Conference on Conceptual Structures: From Information to Intelligence,pp 139-153, 2010.
[32]J. Herethet al.,"Conceptual Knowledge Discovery and Data Analysis", In Proceedings of 8th International Conference onConceptual Structures: Logical, Linguistic, and Computational Issues, pp 421-437, 2000.
[33]Y. Zhao, K. George and U. Fayyad, “Hierarchical Clustering Algorithms for Document Datasets”, Data Mining and Knowledge Discovery, pp 141-168, Vol.10, Issue 2, March 2005.
[34] A. Mannini and A. Sabatini, “Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers”, Sensors,pp. 1154-1175,Vol. 10,Issue 2, February 2010.