Manifold learning and recognition of human activity using body-area sensors

Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011

Volumn 2, Issue , 2011, Pages 7-13

  Zhang, Mi ^a   Sawchuk, Alexander A ^a  

^a University of Southern California   (United States)

Author keywords

assisted living technologies; body area sensors; human activity recognition; manifold learning

Indexed keywords

ACTIVITY RECOGNITION; HUMAN ACTIVITIES; HUMAN ACTIVITY RECOGNITION; INPUT SPACE; INTERPOLATION TECHNIQUES; INTRINSIC STRUCTURES; LOCALLY LINEAR EMBEDDING; MANIFOLD LEARNING; MAPPING FUNCTIONS; MOTION SENSORS; NEAREST-NEIGHBORS; NONLINEAR DIMENSIONALITY REDUCTION; NONLINEAR MANIFOLDS;

SENSORS;

LEARNING SYSTEMS;

EID: 84863237688     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2011.92     Document Type: Conference Paper

References (18)

1. Mi Zhang and Alexander A. Sawchuk. A customizable framework of body area sensor network for rehabilitation. In International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), pages 1-6, Bratislava, Slovak Republic, November 2009.
2. Ling Bao and Stephen S. Intille. Activity recognition from user-annotated acceleration data. In International Conference on Pervasive Computing, pages 1-17, Linz/Vienna, Austria, 2004.
3. Thomas Stiefmeier, Daniel Roggen, and Gerhard Tröster. Gestures are strings: efficient online gesture spotting and classification using string matching. In International Conference on Body Area Networks (BodyNets), pages 16:1-16:8, Florence, Italy, 2007.
4. Mi Zhang and Alexander A. Sawchuk. A feature selection-based framework for human activity recognition using wearable multimodal sensors. In International Conference on Body Area Networks (BodyNets), Beijing, China, November 2011.
5. Mi Zhang and Alexander A. Sawchuk. A bag-of-feature-based framework for human activity representation and recognition. In ACM International Conference on Ubiquitous Computing (Ubicomp) Workshop on Situation, Activity and Goal Awareness, Beijing, China, September 2011.
6. Joshua B. Tenenbaum, Vin de Silva, and John C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290(5500):2319-2323, 2000.
7. Sam T. Roweis and Lawrence K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323, 2000.
8. Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15:1373-1396, 2002.
9. Ming-Hsuan Yang. Extended isomap for pattern classification. In Eighteenth National Conference on Artificial Intelligence (AAAI), pages 224-229, Edmonton, Alberta, Canada, 2002.
10. Christoph Bregler and Stephen M. Omohundro. Nonlinear manifold learning for visual speech recognition. In nternational Conference on Computer Vision (ICCV), pages 494-499, Cambridge, Massachusetts, USA, 1995.
11. Qiang Wang, Guangyou Xu, and Haizhou Ai. Learning object intrinsic structure for robust visual tracking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 227-233, Madison, Wisconsin, USA, 2003.
12. Ahmed M. Elgammal and Chan-Su Lee. Inferring 3D body pose from silhouettes using activity manifold learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 681-688, Washington, DC, USA, 2004.
13. Jaron Blackburn and Eraldo Ribeiro. Human motion recognition using isomap and dynamic time warping. In Workshop on Human Motion: Understanding, Modeling, Capture and Animation, pages 285-298, Rio de Janeiro, Brazil, 2007.
14. Liang Wang and David Suter. Analyzing human movements from silhouettes using manifold learning. In IEEE International Conference on Video and Signal Based Surveillance (AVSS), Sydney, New South Wales, Australia, 2006.
15. Preben Fihl, Michael B. Holte, Thomas B. Moeslund, and Lars Reng. Action recognition using motion primitives and probabilistic edit distance. In International Conference on Articulated Motion and Deformable Objects (AMDO), pages 375-384, Mallorca, Spain, 2006.
16. http://www.motionnode.com.
17. Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, and Daniel D. Lee. Spectral methods for dimensionality reduction. Semisupervised Learning. MIT Press: Cambridge, MA, USA, 2006.
18. Lawrence K. Saul and Sam T. Roweis. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 4:119-155, December 2003.