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Issue No.02 - March/April (2008 vol.23)
pp: 50-57
Jes? Favela , Cicese
ABSTRACT
A main challenge in developing ambient-intelligence environments is context recognition. Systems can easily recognize some contextual variables, such as location, but activity recognition is much more complex. The authors describe an approach for estimating activities in a working environment. They used data representing 196 hours of detailed observation of hospital workers to train and test a hidden Markov model. The results indicate that an HMM can correctly estimate user activity 92.6 percent of the time and can outperform neuronal networks and even human observers familiar with the work practices. These results can help augment ambient-intelligence environments in hospitals. This article is part of a special issue on ambient intelligence.
INDEX TERMS
activity recognition, ambient intelligence, pervasive healthcare
CITATION
Monica Tentori, Jes? Favela, "Activity Recognition for the Smart Hospital", IEEE Intelligent Systems, vol.23, no. 2, pp. 50-57, March/April 2008, doi:10.1109/MIS.2008.18
REFERENCES
1. J. Camacho et al., "MobileSJ: Managing Multiple Activities in Mobile Collaborative Working Environments," Int'l J. E-collaboration, vol. 4, no. 1, 2008, pp. 60–73.
2. M.D. Rodriguez et al., "Agent-Based Ambient Intelligence for Healthcare," AI Comm., vol. 18, no. 3, 2005, pp. 201–216.
3. E.B. Moran et al., "Mobility in Hospital Work: Towards a Pervasive Computing Hospital Environment," Int'l J. Electronic Healthcare, vol. 3, no. 1, 2006, pp. 72–89.
4. V. Stanford, "Beam Me Up, Doctor McCoy," IEEE Pervasive Computing, vol. 2, no. 3, 2003, pp. 13–18.
5. J. Favela et al., "Activity Recognition for Context-Aware Hospital Applications: Issues and Opportunities for the Deployment of Pervasive Networks," Mobile Networks and Applications, vol. 12, nos. 2–3, 2007, pp. 155–171.
6. D. Sánchez, M. Tentori, and J. Favela, "Hidden Markov Models for Activity Recognition in Ambient Intelligence Environments," Proc. 8th Mexican Int'l Conf. Current Trends in Computer Science (ENC 07), IEEE CS Press, 2007, pp. 33–40.
7. N. Oliver, A. Garg, and E. Horvitz, "Layered Representations for Learning and Inferring Office Activity from Multiple Sensory Channels," Computer Vision and Image Understanding, vol. 96, no. 2, 2004, pp. 163–180.
8. D. Zhang et al., "Modeling Individual and Group Actions in Meeting with Layered HMMs," IEEE Trans. Multimedia, vol. 8, no. 3, 2006, pp. 509–520.
9. A.L. Friedman, "Medication Errors Common, Difficult to Detect among Transplant Patients," Archives of Surgery,22 Mar. 2007.
10. G. Alvarez and E. Coiera, "Interruptive Communication Patterns in the Intensive Care Unit Ward Round," Int'l J. Medical Informatics, vol. 74, no. 10, 2005, pp. 779–781.
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