Activity recognition with the aid of unlabeled samples

ABSTRACT

Activity recognition is an important topic in ubiquitous computing. In activity recognition, supervised learning techniques have been widely applied to learn the activity models. However, most of them can only utilize labeled samples for learning even though a large amount of unlabeled samples exist. In our previous work, we have proposed a semi-supervised learning method which can utilize both labeled and unlabeled samples for learning. As an alternative, a new learning method is proposed in this work. It makes use of the unlabeled samples to remove the noises from labeled samples, so that the learning performance is improved. Experimental results show the effectiveness of our method.

REFERENCES

• 1: Vince Stanford, Using Pervasive Computing to Deliver Elder Care, IEEE Pervasive Computing, v.1 n.1, p.10-13, January 2002 [doi>10.1109/MPRV.2002.993139]
• 2: Cory D. Kidd , Robert Orr , Gregory D. Abowd , Christopher G. Atkeson , Irfan A. Essa , Blair MacIntyre , Elizabeth D. Mynatt , Thad Starner , Wendy Newstetter, The Aware Home: A Living Laboratory for Ubiquitous Computing Research, Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture, p.191-198, October 01-02, 1999
• 3: Donghai Guan , Weiwei Yuan , Young-Koo Lee , Andrey Gavrilov , Sungyoung Lee, Activity Recognition Based on Semi-supervised Learning, Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, p.469-475, August 21-24, 2007 [doi>10.1109/RTCSA.2007.17]
• 4: Avrim Blum , Tom Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory, p.92-100, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279962]
• 5: S. S. Intille , J. W. Davis , A. F. Bobick, Real-time closed-world tracking, Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), p.697, June 17-19, 1997
• 6: Stillman, S., Tanawongsuwan, R., Essa, I. 1999. A system for tracking and recognizing multiple people with multiple cameras. In Proceedings of the Second International Conference on Audio-Vision-based Person Authentication (Washington, DC. April, 1999).
• 7: I. Haritaoglu , D. Harwood , L. S. Davis, W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People, Proceedings of the 3rd. International Conference on Face & Gesture Recognition, p.222, April 14-16, 1998
• 8: Makikawa, M., Iizumi, H. 1995. Development of an ambulatory physical activity monitoring device and its application for categorization of actions in daily life. In Proceedings of the 8^th World Congress on Medical Informatics: MEDINFO 95 (North-Holland, Amsterdam, 1995) 747--750.
• 9: Aminian, K., Robert, P., Jequier, E., Schutz, Y. 1995. Estimation of speed and incline of walking using neural network. IEEE Transactions on Instrumentation and Measurement, 44, 3 (Jun 1995), 743--746. DOI=http://doi.ieeecomputersociety.org/10.1109/19.387322
• 10: Bao, L. 2003. Physical activity recognition from acceleration data under seminaturalistic conditions. M. Eng. thesis, EECS, Massachusetts Institute of Technology, 2003.
• 11: Abowd, G. D. 2002. Director of the AwareHome initiative. Georgia Insitute of Technology, 2002.
• 12: Barger, T., Alwan, M., Kell, S., Turner, B., Wood, S., Naidu, A. 2002. Objective remote assessment of activities of daily living: Analysis of meal preparation patterns. Poster presentation, Medical Automation Research Center, University of Virginia Health System, 2002.
• 13: Mozer, M. 1998. The neural network house: an environment that adapts to its inhabitants. In Proceedings of the AAAI Spring Symposium on Intelligent Environments, Technical Report SS-98-02, AAAI Press, Menlo Park, CA, 1998 (110--114).
• 14: Kevin Patrick Murphy , Stuart Russell, Dynamic bayesian networks: representation, inference and learning, University of California, Berkeley, 2002
• 15: Stephen S. Intille , Aaron F. Bobick, Recognizing planned multiperson action, Computer Vision and Image Understanding, v.81 n.3, p.414-445, March 2001 [doi>10.1006/cviu.2000.0896]
• 16: Kautz, H., Etziono, O. Fox, D., Weld, D. 2003. Foundations of assisted cognition systems. Technical report cse-02-ac-01, University of Washington, Department of Computer Science and Engineering, 2003.
• 17: Wilson, D. L. 1972. Asymptotic properties of nearest neighbor rules using edited data, IEEE Transaction on Systems, Man, and Cybernetics, 2, 3 431--433.
• 18: Guan, D. H., Yuan, W. W., Lee, Y. K., Lee, S. Y. 2008. In Proceedings of IEEE International Joint Conference on Neural Networks (HongKong, 2008). 1183--1187.
• 19: Kristof Van Laerhoven , Hans-Werner Gellersen, Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition, Proceedings of the Eighth International Symposium on Wearable Computers, p.142-149, October 31-November 03, 2004 [doi>10.1109/ISWC.2004.40]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications

General Terms:
Algorithms

Keywords:
activity recognition, noise filtering, semi-supervised learning, supervised learning