SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 3, Pages 1,525-1,533, DOI: https://doi.org/10.21307/ijssis-2017-818
License : (CC BY-NC-ND 4.0)
Received Date : 01-April-2015 / Accepted: 15-July-2015 / Published Online: 01-September-2015
Complex human activity recognition suffers from ambiguity of interpretation problem. A novel neutrosophic formal concept analysis method has been proposed to quantify non-determinism leading to ambiguity of interpretation and utilize it in activity recognition. The method works by penalizing performance of non-deterministic activities and rewarding the deterministic ones. Thus, non- deterministic activities are identified during testing due to significantly reduced performance and contexts can be redesigned to improve their description. The proposed method has been implemented on benchmark dataset having both types of activities. Our approach successfully identified non-determinism in activities description without compromising recognition performance of deterministic activities. It has also been shown that other approaches fail to identify non deterministic activities. Overall accuracy of activity recognition of our approach was comparable to other approaches.
1. S. Katz, "Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living." Journal of the American Geriatrics Society, Vol. 31, No. 12,pp: 721-727, 1983
2. S. C. Mukhopadhyay, “Wearable Sensors for Human Activity Monitoring: A Review”, IEEE Sensors Journal, Vol. 15, No. 3, March 2015, pp. 1321-1330.
3. S. Mittal, K. Gopal, S.L. Maskara, "Preprocessing methods for context extraction from multivariate wireless sensors data - An evaluation," Annual IEEE India Conference (INDICON) 2013, pp:1-6, Dec. 2013, doi: 10.1109/INDCON.2013.6725984
4. S. Mittal, A. Aggarwal and S.L. Maskara, “Application of Bayesian Belief Networks for Context Extraction from Wireless Sensors Data”, in Proceedings of 14th International Conference on Advanced Communications Technology, South Korea, pp: 410-415, Feb 2012
5. K. Eunju, S. Helal, and D. Cook, "Human activity recognition and pattern discovery" IEEE Pervasive Computing ,Vol 9, No.1, pp: 48-53, 2010
6. S. Mittal, K. Gopal and S.L. Maskara, “A Versatile Lattice Based Model for Situation Recognition from Dynamic Ambient Sensors”, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 1, pp. 403-432, Feb 2013
7. D. Roggen, et al. "Collecting complex activity data sets in highly rich networked sensor environments", In Proceedings of IEEE Seventh International Conference on Networked Sensing Systems, pp: 233-240, June 2010,Kassel, Germany
8. S. Mahmoud, A. Lotfi and C. Langensiepen, "Behavioural pattern identification and prediction in intelligent environments" Applied Soft Computing, Vol. 13 No.4, pp: 1813-1822, 2013
9. H. Jin, H. Li and J. Tan, "Real-time Daily Activity Classification with Wireless Sensor Networks using Hidden Markov Model," IEEE 29th Annual International Conference of the Engineering in Medicine and Biology Society, pp.3192-3195, 22-26 Aug. 2007
10. H. Kautz, L. Arnstein, G. Borriello, O. Etzioni, and D. Fox. "An overview of the assisted cognition project." In AAAI-2002 Workshop on Automation as Caregiver: The Role of Intelligent Technology in Elder Care, pp. 60-65. 2002
11. O. Brdiczka, J. Crowley, and P. Reignier, “Learning situation models in a smart home,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 1, pp. 56–63, Feb. 2009
12. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, “Activity recognition from accelerometer data,” in Proc. 17th Conf. Innovative Applied Artificial Intelligence, pp. 1541–1546, 2005
13. N.K.Suryadevara, M.T.Quazi and S.C.Mukhopadhyay, Intelligent Sensing Systems for measuring Wellness Indices of the Daily Activities for the Elderly, proceedings of the 2012 Eighth International Conference on Intelligent Environments, Mexico, June 1-3, 2012, pp. 347-350
14. N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.
15. N.K. Suryadevara and S.C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.
16. D. Riboni and C. Bettini, “OWL 2 modeling and reasoning with complex human activities,” Pervasive Mobile Computing, Vol. 7, no. 3, pp. 379–395, 2011
17. M. M. Kokar, C. J. Matheus, and K. Baclawski, "Ontology-based situation awareness." Information fusion, Vol. 10 No. 1, pp: 83-98, 2009
18. J.L. Salmeron,"Fuzzy cognitive maps for artificial emotions forecasting," Applied Soft Computing, Vol. 12 No. 12, pp: 3704-3710, 2012
19. F. Smarandache,”A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set and Logic”. Rehoboth: American Research Press, 1998
20. A Q Ansari, R. Biswas, and S. Aggarwal. "Neutrosophic classifier: An extension of fuzzy classifier." Applied Soft Computing, Vol 13, no. 1 pp: 563-573, 2013
21. S. O. Kuznetsov and S. A. Obiedkov. "Comparing performance of algorithms for generating concept lattices." Journal of Experimental & Theoretical Artificial Intelligence, Vol. 14 No.2-3 pp: 189-216, 2002
22. C Carpineto, G Romano, “Concept Data Analysis: Theory and Applications”, John Wiley, 2004.
23. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition
24. H. Sagha et al. "Benchmarking classification techniques using the Opportunity human activity dataset", In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp: 36-40, 2011.
26. A. Manzoor et al, “Identifying important action primitives for high level activity recognition”, In Proceedings of the 5th European conference on Smart sensing and context (EuroSSC'10), pp: 149-162, 2010.
27. H. Ghayvat, S. Mukhopadhyay, X. Gui and N. Suryadevara, “WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings”, Sensors 2015, Vol. 15, pp. 10350-10379; www.mdpi.com/journal/sensors, doi:10.3390/s150510350.