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Ihianle, Isibor Kennedy; Naeem, Usman; Tawil, Abdel-Rahman (2016)
Publisher: Elsevier BV
Journal: Procedia Computer Science
Languages: English
Types: Unknown
Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. World Population Ageing. Retrieved April 4, 2016 from http://www.un.org/en/development/desa/population/ publications/pdf
    • 2. S. Consolvo, P. Roessler, B. Shelton, A. LaMarca, B. Schilit, Bly, Technology for care networks of elders. Pervasive Computing 2004.
    • 3. K.Z. Haigh, J. Phelps, C.W. Geib, An open agent architecture for assisting elder independence, In: The First International Joint Conference on Autonomous Agents and Multi Agent Systems. AAMAS, 2002; p 578-586.
    • 4. Kasteren, Tim, Van. G. Englebienne and B. J. A. Krose. Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. In: Activity Recognition in Pervasive Intelligent Environments, Atlantis Press Book; 2010, p. 165-185.
    • 5. T. Hoffman. Probabilistic Latent Semantic Indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR99), 1999.
    • 6. J. Ordonez, P. Toledo and A. Sanchis. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environment Using Binary Sensors. Sensors.MDPI Sensor, Open Access 2013, p. 5460-5477.
    • 7. U. Naeem, J. Bigham and W. Jinfu, Recognising Activities of Daily Life Using Hierarchical Plans, In: European Conference on Smart Sensing and Context; 2007.
    • 8. L. Chen, C. D. Nugent and H. Wang, A Knowledge-Driven Approach to Activity Recognition in Smart Homes, In: IEEE Transactions on Knowledge and Data Engineering 2012.
    • 9. S. Xing, T. Hanghang and J. Ping, Activity Recognition with Smartphone Sensors, In: IEEE Xplore; 2014.
    • 10. D. Huynh, Human Activity Recognition with Wearable Sensors, Technische Universtat Darmstadt, PhD Thesis; 2008.
    • 11. B. Ling and S. I. Stephen, Activity recognition from user-annotated acceleration data, In: International Conference on Pervasive Computing,Linz/Vienna, Austria; 2004.
    • 12. D. J. Patterson, D. Fox, H. Kautz and M. Philipose, Fine-grained activity recognition by aggregating abstract object usage, In: Ninth IEEE International Symposium on Wearable Computers ; 2005.
    • 13. T. Huynh, F. Mario and S. Bernt, Discovery of activity patterns using topic models, In: 10th international conference on Ubiquitous computing UbiComp '08, New York, NY, USA; 2008.
    • 14. E. M. Tapia, S. S. Intille and L. Kent, Activity Recognition in the Home Using Simple and Ubiquitous Sensors, In: Pervasive Computing, 2004, p. 158-175.
    • 15. H. A. Kautz, A Formal Theory of Plan Recognition and its Implementation, In: Reasoning about plans, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc; 1991, p. 69-124.
    • 16. G. Okeyo, L. Chen, H. Wang and R. Sterriot, A Hybrid Ontological and Temporal Approach for Composite Activity Modelling, In: IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 2012
    • 17. A. N. Tuan, R. Andrea and A. Marco, Ontology-based office activity recognition with applications for energy savings, Journal of Ambient Intelligence and Humanized Computing; 2014, vol. 5, no. 5, p. 667-681.
    • 18. T. Kasteren, N. Athanasios, E. Gwenn and K. Ben, Accurate Activity Recognition in a Home Setting, In: 10th Iinternational Conference on Ubiquitous Computing, New York, NY, USA; 2008.
    • 19. M. Philipose, K.P. Fishkin, M. Perkowitz, D.J. Patterson, D. Fox, H. Kautz, D. Hhnel, Inferring activities from interactions with objects, IEEE Pervasive Computing 2004, p. 50-57.
    • 20. J. Zheng and M. N. Lionel, An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data, In: ACM Conference on Ubiquitous Computing UbiComp '12, New York, USA; 2012
    • 21. R. Hammid, S. Maddi, A. Johnson, A. Bobick, I. Essa and C. I. Lee, Unsupervised Activity Discovery and Characterization from EventStreams, In: 21st Conference on Uncertainty in Artificial Intelligence (UAI2005); 2005
    • 22. I. K. Ihianle, U. Naeem and A. R. Tawil, A Dynamic Segmentation Based Activity Discovery through Topic Modelling, In IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015), London; 2015
    • 23. F. Katayoun and D. Gatica-Perez, Discovering Routine from Large-Scale Human Locations using Probabilistic Topic Models, In: ACM Transactions on Intelligent Systems and Technology (TIST); 2011, vol. 2, no. 1
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