ABSTRACT
One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this application. It is essentially a temporal classification problem which has been modeled in the past by naïve Bayes classifiers and hidden Markov models (HMMs). In this paper, we describe the use of another probabilistic model: Conditional Random Fields (CRFs), which is currently gaining popularity for its remarkable performance for activity recognition. Our focus is on using CRFs to recognize activities performed by an inhabitant in a smart home environment and our goal is to validate the claim of its higher or similar performance by comparing CRFs with HMMs using data collected in a real smart home.
- Wadley, V., Okonkwo, O., Crowe, M., Ross-Meadows, L. A. 2007. Mild Cognitive Impairment and everyday function: Evidence of reduced speed in performing instrumental activities of daily living. American Journal of Geriatric Psychiatry, 16(5): 416--424.Google ScholarCross Ref
- Maurer, U., Smailagic, A., Siewiorek, D., Deisher, M. 2006. Activity recognition and monitoring using multiple sensors on different body positions. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks. Google ScholarDigital Library
- Orr, R. J. and Abowd, G. D. 2000. The Smart Floor: A Mechanism for Natural User Identification and Tracking. Proc. Conf. Human Factors in Computing Systems (CHI 00). Google ScholarDigital Library
- Singla, G., Cook, D. and Schmitter-Edgecombe, M. 2010. Recognizing independent and joint activities among multiple residents in smart environments. Ambient Intelligence and Humanized Computing Journal, 1(1): 57--63.Google ScholarCross Ref
- Patterson, D. J., Fox, D., Kautz, H. A., and Philipose, M. 2005. Fine-grained activity recognition by aggregating abstract object usage. In proc. of ISWC, 44--51, Osaka, Japan. Google ScholarDigital Library
- Wilson, D. and Atkeson, C. 2005. Simultaneous tracking and activity recognition using many anonymous binary sensors. In Pervasive Computing, 3rd International Conference, 62--79, Munich. Germany. Google ScholarDigital Library
- Landwehr, N., Gutmann, B., Thon, I., Philipose, M. and Raedt, L. 2007. Relational transformation-based tagging for human activity recognition. Proceedings of the 6th International Workshop on Multi-relational Data Mining (MRDM07), 81--92, Warsaw. Poland.Google Scholar
- Fogarty, J., Au, C., and Hudson, S. E. 2006. Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition. In UIST 06: Proceedings of the 19th annual ACM symposium on User interface software and technology, 91--100, New York, NY. USA. Google ScholarDigital Library
- Rabiner, L. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE. 77(2): 257--286.Google ScholarCross Ref
- Lafferty, J., McCallum, A. and Pereira, F. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. 18th International Conference on Machine Learning. 282--289. Morgan Kaufmann. San Francisco. CA. Google ScholarDigital Library
- Sukthankar, G. and Sycara, K. 2006. Robust recognition of physical team behaviors using spatio-temporal models. In Proceedings of Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). Google ScholarDigital Library
- Singla, G., Cook, D. and Schmitter-Edgecombe, M. 2009. Tracking activities in complex settings using smart environment technologies. International Journal of BioSciences, Psychiatry and Technology. 1(1): 25--35.Google Scholar
- Vail, D. L., Veloso, M. M., and Lafferty, J. 2007. Conditional random fields for activity recognition. In proceedings of the conference on Autonomous Agents and Multi Agent Systems (AAMAS), ACM New York, NY, USA. Google ScholarDigital Library
- Kasteren, T., Noulas, A., Englebienne, G. and Krose, B. 2008. Accurate Activity Recognition in a Home Setting. Proceedings of the Tenth International Conference on Ubiquitous Computing. Seoul. Korea, 1--9. Google ScholarDigital Library
- Rabiner, L. and Juang, B. H. 1986. An introduction to hidden Markov models. IEEE Acoust. Speech Signal Proc., Mag. 3, 4--16Google Scholar
- Wallach, H. 2004. Conditional random Fields: An Introduction. Technical Report. University of Pennsylvania CIS.Google Scholar
- Wallach, H. 2002. Efficient Training of Conditional Random Fields. Master's Thesis. University of Edinburgh.Google Scholar
- Pietra, S. D., Pietra, V. D. and Lafferty, J. 1997. Inducing features of random fields. IEEE transactions on pattern analysis and machine intelligence. 19(4): 380--393. Google ScholarDigital Library
- Darroch, J. and Ratcliff, D. 1972. Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics. 43: 1470--1480.Google ScholarCross Ref
- Sutton, C., Pal, C., and McCallum A. 2005. Sparse forward-backward for fast training of conditional random fields. NIPS Workshop on Structured Learning for Text and Speech Processing.Google Scholar
Index Terms
- Conditional random fields for activity recognition in smart environments
Recommendations
Conditional random fields for activity recognition
AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systemsActivity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we compare two models for temporal classification: hidden Markov models (HMMs)...
Latent-Dynamic Conditional Random Fields for recognizing activities in smart homes
Ambient and Smart Component Technologies for Human Centric ComputingAs the number of elderly people in our society increases, the need of assistive technologies in home becomes urgent. Existing techniques allow elderly people to be better assisted through monitoring what goes on in smart homes and inferring their ...
Activity recognition using conditional random field
iWOAR '15: Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and InteractionActivity Recognition is an integral component of ubiquitous computing. Recognizing an activity is a challenging task since activities can be concurrent, interleaved or ambiguous and can consist of multiple actors (which would require parallel activity ...
Comments