The application of spatio-temporal feature and multi-sensor in home medical devices

International Journal of Digital Content Technology and its Applications

Volumn 4, Issue 7, 2010, Pages

  Gao, Zan ^a,c   Detyniecki, Marcin ^b   Chen, Mingyu ^c   Hauptmann, Alexander G ^c   Wactlar, Howard D ^c   Cai, Anni ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

^c Carnegie Mellon University   (United States)

Author keywords

Event recognition; HMM; Medical devices; Multi sensor; Spatio temporal feature; SVM

Indexed keywords

EVENT RECOGNITION; HMM; MEDICAL DEVICES; MULTI SENSOR; SPATIO-TEMPORAL; SVM;

BIOMEDICAL ENGINEERING; CAMERAS; COMPUTER VISION; DRUG INFUSION; ENCODING (SYMBOLS); INFORMATION USE; PUMPS; SENSORS;

FEATURE EXTRACTION;

EID: 78651540887     PISSN: 19759339     EISSN: None     Source Type: Journal    
DOI: 10.4156/jdcta.vol4.issue7.7     Document Type: Article

References (14)

1. X. Li, "HMM based action recognition using oriented histograms of optical flow field," Electronics Letters, 43(10):560-561, 2007.
2. M. Brand, N. Oliver, and A. Pentland, "Coupled hidden markov models for complex action recognition," CVPR, 1996.
3. N. Oliver, B. Rosario, and A. Pentland, "A bayesian computer vision system for modeling human interactions," T-PAMI, 22(8):831-843, 2000.
4. I. Laptev and T. Lindeberg, "Space-time interest points," ICCV, pp. 432-439, 2003.
5. Schuldt, C. Laptev, and B. I. Caputo, "Recognizing human actions: a local SVM approach," ICPR (17), pp. 32-36, 2004.
6. I. Laptev, M. Marszałek, C. Schmid, and B. Rozenfeld, "Learning realistic human actions from movies," CVPR, pp 1-8, 2008.
7. M-Y. Chen, and A. Hauptmann, "MoSIFT: Recognizing Human Actions in Surveillance Videos," CMU-CS-09-161, Carnegie Mellon University, 2009.
8. P. Dollár, V. Rabaud, G. Gottrell, and S. Belongie, "Behavior Recognition via Sparse Spatio-Temporal Features," VS-PETS 2005, page 65-72
9. A. Gilbert, J. Illingworth, and R. Bowden, "Scale invariant action recognition using compound features mined from dense spatio-temporal corners," ECCV, pages 222-233, 2008.
10. D.G. Lowe, "Distinctive image features from scale-invariant keypoints," IJCV, pp.91-110, 2004.
11. J. Niebles, H. Wang, and F. Li, "Unsupervised learning of human action categories using spatial temporal words," BMVC, 2005.
12. Y. Ke, R. Sukthankar, and M. Hebert, "Efficient visual eventdetection using volumetric features," ICCV, 2005.
13. J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid, "Local features and kernels for classification of texture and object categories: A comprehensive study," IJCV, 73(2):213-238, 2007.
14. B. Meier, F.D.A, "Steps up Oversight of Infusion Pumps," New York Times, p. B1, April 23, 2010