A Human Activity Recognition System Based On Sensory Data Related to Object Usage
Authors: M. Abdullah-Al-Wadud
Sensor-based Activity Recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336424Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 M. Shoyaib, A.M.J. Sarkar, A.M. Khan, O. Chae and Y.K. Lee, "Active tool for human activity data collection,” Electronics letters, Vol. 47(25), (2011), p. 1370-1372.
 J. Sarkar, Y.K. Lee, and S. Lee, "GPARS: a general-purpose activity recognition system,” Applied Intelligence, Vol. 35(2), (2011), p. 242-259.
 E. M. Tapia, "Activity Recognition in the Home Using Simple and Ubiquitous Sensors,” Pervasive, Vienna, Austria, (2004).
 E. M. Tapia: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. S.M. Thesis, Massachusetts Institute of Technology, 2003.
 Exploratory research projects. Available at http://techresearch.intel.com/articles/Exploratory/1435.htm
 Smart medical home research laboratory. Available at http://www.futurehealth.rochester.edu/smart_home/
 The aware home research initiative. Available at http://awarehome.imtc.gatech.edu/
 Mit house_n. Available at http://architecture.mit.edu/house_n/
 Smart houses info. Available at http://gero-tech.net/ smart-homes.html
 E. M. Tapia, S. S. Intille, K. Larson, "Activity recognition in the home using simple and ubiquitous sensors”, In: Ferscha A, Mattern F (eds) Pervasive. Lecture notes in computer science, vol 3001. Springer, Berlin, pp 158–175, 2004.
 V. T. Kasteren, A. Noulas, G. Englebienne, B. Kröse, "Accurate activity recognition in a home setting”, In Proc UbiComp. ACM, New York, pp 1–9, 2008. doi:10.1145/1409635.1409637
 M. Perkowitz, M. Philipose, K. Fishkin, D. J. Patterson, "Mining models of human activities from the web”, In WWW ’04: Proceedings of the 13th international conference on World Wide Web ACM, New York, pp. 573 – 582, (2004). doi:10.1145/988672.988750
 D. Wyatt, M. Philipose, T. Choudhury, "Unsupervised activity recognition using automatically mined common sense”, In: Veloso MM, Kambhampati S (eds) Proc AAAI. AAAI Press/The MIT Press, Menlo Park, pp 21–27, 2005. http://www.informatik.uni-trier.de/~ ley/db/conf/aaai/aaai2005.html#WyattPC05
 S. S. Intille, K. Larson, E. M. Tapia, J. Beaudin, P. Kaushik, J. Nawyn, R. Rockinson, "Using a live-in laboratory for ubiquitous computing research”, In: Fishkin KP, Schiele B, Nixon P, Quigley AJ (eds) Pervasive. Lecture notes in computer science, vol 3968. Springer, Berlin, pp 349–365, (2006).
 A. M. J. Sarkar, Y. K. Lee, S. Lee, "A smoothed Naïve Bayes based classifier for activity recognition. IETE Tech Rev 27(2):107–119, (2010). doi:10.4103/0256-4602.60164
 F. Jelinek, R. L. Mercer, "Interpolated estimation of Markov source parameters from sparse data”, In: Gelsema ES, Kanal LN (eds) Proceedings, workshop on pattern recognition in practice. North Holland, Amsterdam, pp 381–397, (1980).
 D. H. Hu, X. X. Zhang, J. Yin, V. W. Zheng, Q. Yang, "Abnormal activity recognition based on hdp-hmm models. http://www.aaai. org/ocs/index.php/IJCAI/IJCAI-09/paper/view/521
 M. Buettner, R. Prasad, M. Philipose, D. Wetherall, "Recognizing daily activities with rfid-based sensors”, In: UbiComp’09: Proceedings of the 11th international conference on Ubiquitous computing. ACM, New York, pp 51–60, (2009). doi:10.1145/ 1620545.1620553
 C. C., J. Y. J. Hsu, "Chatting activity recognition in social occasions using factorial conditional random fields with iterative classification. In: AAAI’08: Proceedings of the 23rd national conference on Artificial intelligence. AAAI Press, Menlo Park, pp 1814–1815, (2008).