KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Human Activity Recognition in Smart Homes Based on a Difference of Convex Programming Problem

  • Ghasemi, Vahid (School of Computer and IT Engineering, Shahrood University of Technology) ;
  • Pouyan, Ali A. (School of Computer and IT Engineering, Shahrood University of Technology) ;
  • Sharifi, Mohsen (School of Computer Engineering, Iran University of Science and Technology)
  • Received : 2016.03.28
  • Accepted : 2016.11.03
  • Published : 2017.01.31
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Abstract

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

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References

  1. G. Singla, D. J. Cook, and M. Schmitter-Edgecombe, "Tracking activities in complex settings using smart environment technologies," International journal of biosciences, psychiatry, and technology (IJBSPT), vol. 1, no. 1, pp. 25-28, January, 2009.
  2. K. Rasch, "Smart assistants for smart homes," Ph.D. Thesis, Royal Institute of Technology, Stockholm, Sweden, 2013.
  3. L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu, "Sensor-based activity recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 6, pp. 790-808, November, 2012. https://doi.org/10.1109/TSMCC.2012.2198883
  4. A. Benmansour, A. Bouchachia, and M. Feham, "Multi-Occupant Activity Recognition in Pervasive Smart Home Environments," ACM Computing Surveys (CSUR), vol. 48, no. 3, p. 34, February, 2016.
  5. N. C. Krishnan and D. J. Cook, "Activity recognition on streaming sensor data," Pervasive and mobile computing, vol. 10, pp. 138-154, February, 2014. https://doi.org/10.1016/j.pmcj.2012.07.003
  6. J. Wan, M. J. O'Grady, and G. M. O'Hare, "Dynamic sensor event segmentation for real-time activity recognition in a smart home context," Personal and Ubiquitous Computing, vol. 19, no. 2, pp. 287-301, February, 2015. https://doi.org/10.1007/s00779-014-0824-x
  7. H. Cho, J. An, I. Hong, and Y. Lee, "Automatic Sensor Data Stream Segmentation for Real-time Activity Prediction in Smart Spaces," in Proc. of the 2015 Workshop on IoT challenges in Mobile and Industrial Systems, pp. 13-18, May 18'th, 2015.
  8. A. Fleury, M. Vacher, and N. Noury, "SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results," IEEE Transactions on Information Technology in Biomedicine, vol. 14, no 2, pp. 274-283, March, 2010. https://doi.org/10.1109/TITB.2009.2037317
  9. I. Fatima, M. Fahim, Y.-K. Lee, and S. Lee, "A unified framework for activity recognition-based behavior analysis and action prediction in smart homes," Sensors, vol. 13, no. 2, pp. 2682-2699, February, 2013. https://doi.org/10.3390/s130202682
  10. E. Kim, S. Helal, C. Nugent, and M. Beattie, "Analyzing Activity Recognition Uncertainties in Smart Home Environments," ACM Trans. Intell. Syst. Technol., vol. 6, no 4, pp. 1-28, Augest, 2015.
  11. B. I. d. P. e. Mesures, C. e. internationale, and O. i. d. normalisation, Guide to the Expression of Uncertainty in Measurement, International Organization for Standardization, 1995.
  12. J. Liao, Y. Bi, and C. Nugent, "Using the Dempster-Shafer theory of evidence with a revised lattice structure for activity recognition," IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 1, pp. 74-82, January, 2011. https://doi.org/10.1109/TITB.2010.2091684
  13. T. Lipp and S. Boyd, "Variations and extension of the convex-concave procedure," Optimization and Engineering, vol. 17, no. 2, pp. 263-287, June, 2016. https://doi.org/10.1007/s11081-015-9294-x
  14. L. Chen, C. Nugent, and G. Okeyo, "An ontology-based hybrid approach to activity modeling for smart homes," Human-Machine Systems, IEEE Transactions on, vol. 44, no. 1, pp. 92-105, February, 2014. https://doi.org/10.1109/THMS.2013.2293714
  15. G. Azkune, A. Almeida, D. Lopez-de-Ipina, and L. Chen, "Extending knowledge-driven activity models through data-driven learning techniques," Expert Systems with Applications, vol. 42, no. 6, pp. 3115-3128, April, 2015. https://doi.org/10.1016/j.eswa.2014.11.063
  16. J. Bilmes, "Dynamic graphical models," Signal Processing Magazine, IEEE, vol. 27, no. 6, pp. 29-42, November, 2010.
  17. D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, MIT press, 2009.
  18. T. v. Kasteren and B. Krose, "Bayesian activity recognition in residence for elders," in Proc. of 3rd IET International Conference on Intelligent Environments, pp. 209-212, September 24-25, 2007.
  19. D. H. Wilson and C. Atkeson, "Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors," in Proc of the 3rd international conference on Pervasive Computing, pp. 62-79, May 8-13 , 2005.
  20. E. Kim, S. Helal, and D. Cook, "Human activity recognition and pattern discovery," Pervasive Computing, IEEE, vol. 9, no. 1, pp. 48-53, January, 2010.
  21. T. van Kasteren, G. Englebienne, and B. Krose, "Human activity recognition from wireless sensor network data: Benchmark and software," Activity Recognition in Pervasive Intelligent Environments, vol. 4, pp. 165-186, May, 2011.
  22. T. van Kasteren, G. Englebienne, and B. J. Krose, "Activity recognition using semi-Markov models on real-world smart home datasets," Journal of ambient intelligence and smart environments, vol. 2, no. 3, pp. 311-325, January, 2010.
  23. P. Natarajan and R. Nevatia, "Coupled hidden semi Markov models for activity recognition," in Proc. of IEEE Workshop on Motion and Video Computing, pp. 10-10, February 23-24, 2007.
  24. L. Wang, T. Gu, X. Tao, H. Chen, and J. Lu, "Recognizing multi-user activities using wearable sensors in a smart home," Pervasive and Mobile Computing, vol. 7, no. 3, pp. 287-298, June, 2011. https://doi.org/10.1016/j.pmcj.2010.11.008
  25. D. Cook, "Learning Setting-Generalized Activity Models for Smart Spaces," IEEE Intelligent Systems, vol. 27, no. 1, pp. 32-38, February, 2012. https://doi.org/10.1109/MIS.2010.112
  26. V. Ghasemi and A. A. Pouyan, "Activity recognition in smart homes using absolute temporal information in dynamic graphical models," in Proc of the 10'th Asian Control Conference, pp. 1-6, May, 2015.
  27. D. H. Hu and Q. Yang, "Cigar: Concurrent and interleaving goal and activity recognition," in Proc of the 23'rd AAAI Conference on Artificial intelligence, pp. 1363-1368, July 13-17, 2008.
  28. Y. Tong and R. Chen, "Latent-Dynamic conditional random fields for recognizing activities in smart homes," Journal of Ambient Intelligence and Smart Environments, vol. 6, no. 1, pp. 39-55, 2014.
  29. T. Gu, Z. Wu, X. Tao, H. K. Pung, and J. Lu, "epsicar: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition," in Proc. of IEEE Int. Conference on Pervasive Computing and Communications, pp. 1-9, March 9-13, 2009.
  30. D. J. Cook, N. C. Krishnan, and P. Rashidi, "Activity discovery and activity recognition: A new partnership," IEEE Transactions on Cybernetics, vol. 43, no. 3, pp. 820-828, June, 2013. https://doi.org/10.1109/TSMCB.2012.2216873
  31. S.-L. Chua, S. Marsland, and H. W. Guesgen, "Unsupervised Learning of Human Behaviours," in Proc of the 25th AAAI Conference on Artificial Intelligence, August 7-11, 2011.
  32. P. Rashidi, D. J. Cook, L. B. Holder, and M. Schmitter-Edgecombe, "Discovering Activities to Recognize and Track in a Smart Environment," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no 4, pp. 527-539, April, 2011. https://doi.org/10.1109/TKDE.2010.148
  33. B. Chikhaoui, S. Wang, and H. Pigot, "A frequent pattern mining approach for ADLs recognition in smart environments," in Proc of IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 248-255, March 22-25, 2011.
  34. L. G. Fahad, A. Khan, and M. Rajarajan, "Activity recognition in smart homes with self-verification of assignments," Neurocomputing, vol. 149, pp. 1286-1298, February, 2015. https://doi.org/10.1016/j.neucom.2014.08.069
  35. F. Sebbak, A. Chibani, Y. Amirat, A. Mokhtari, and F. Benhammadi, "An evidential fusion approach for activity recognition in ambient intelligence environments," Robotics and Autonomous Systems, vol. 61, no. 11, pp. 1235-1245, November, 2013. https://doi.org/10.1016/j.robot.2013.05.010
  36. S. Mckeever, J. Ye, L. Coyle, C. Bleakley, and S. Dobson, "Activity recognition using temporal evidence theory," Journal of Ambient Inteligence and Smart Environments, vol. 2, no. 3, pp. 253-269, January, 2010.
  37. E. Javadi, B. Moshiri, and H. S. Yazdi, "Activity Recognition In Smart Home Using Weighted Dempster-Shafer Theory," International Journal of Smart Homes, vol. 7, no. 6, pp. 23-34, 2013. https://doi.org/10.14257/ijsh.2013.7.6.03
  38. U. M. Fayyad and K. B. Irani, "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning," in Proc. of the 13th International Joint Conference on Artificial Intelligence, pp. 1022-1027, 1993.
  39. C. R. Rao, Linear statistical inference and its applications: 2nd Eddition, John Wiley & Sons Publications, September, 2009.
  40. I. Katz and K. Crammer, "Outlier-Robust Convex Segmentation," arXiv preprint arXiv:1411.4503, 2014.
  41. C. Zhu, H. Xu, C. Leng, and S. Yan, "Convex Optimization Procedure for Clustering: Theoretical Revisit," in Proc. of the Annual Conference on Neural Information Processing Systems (NIPS), pp. 1619-1627, December 8-13, 2014.
  42. E. Nazerfard, B. Das, L. B. Holder, and D. J. Cook, "Conditional random fields for activity recognition in smart environments," in Proc. of the 1st ACM International Health Informatics Symposium, pp. 282-286, November 11-12, 2010.
  43. B. L. Thomas, A. S. Crandall, and D. J. Cook, "A Genetic Algorithm approach to motion sensor placement in smart environments," Journal of Reliable Intelligent Environments, vol. 2, no. 1, pp. 3-16, 2016. https://doi.org/10.1007/s40860-015-0015-1