Detection of activities by wireless sensors for daily life surveillance: Eating and drinking

Sensors

Volumn 9, Issue 3, 2009, Pages 1499-1517

  Zhang, Sen ^a   Ang Jr , Marcelo H ^a   Xiao, Wendong ^b   Tham, Chen Khong ^a,b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Eating and drinking; Euler angle; Feature extraction; HTM; Wireless sensor

Indexed keywords

ACCELEROMETER MEASUREMENT; EATING AND DRINKING; EULER ANGLES; HIERARCHICAL TEMPORAL MEMORY (HTM); HTM; MODELLING CAPABILITIES; THREE-DIMENSIONAL KINEMATICS; WIRELESS SENSOR;

ACCELEROMETERS; EXTENDED KALMAN FILTERS; FEATURE EXTRACTION; SENSORS;

TIME VARYING NETWORKS;

EID: 63849305114     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s90301499     Document Type: Article

References (28)

1. Pentland, A. Healthwear: Medical technology becomes wearable. IEEE Computer 2004, 37, 42-49.
2. Aggarwal, J.K.; Cai, Q. Human motion analysis: A review. Comput. Vis. Image Understand. 1999, 10, 428-440.
3. Najafi, B.; Aminian, K.; Paraschiv-Ionescu, A.; Loew, F.; Bula, C.J.; Robert, P. Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 2003, 50, 711-723.
4. Rhodes, B.; Mase, K. Wearables in 2005. IEEE Pervasive Comput. 2006, 5, 92-95.
5. Zhou, H.Y.; Hu, H.S. A survey - human movement tracking and stroke rehabilitation; University of Essex: Colchester, UK, 2004.
6. Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Proc. 2nd Int. Conf. Pervasive Comput., 2004; Vol. 3001, pp. 1-17.
7. Zheng, H.; Black, N.D.; Harris, N.D. Position-sensing technologies for movement analysis in stroke rehabilitation. Med. Biol. Eng. Comput. 2005, 43, 413-420.
8. Hao, Q.; Brandy, D.J.; Guenther, B.D.; Burchett, J.B.; Shankar, M.; Feller, S. Human Tracking with Wireless Distributed Pyroelectric Sensors. IEEE Sensors J. 2006, 6, 1683-1696.
9. Hu, W.; Tan, T.; Wang, L.; Maybank, S. A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst., Man, Cybern. C, Appl. Rev. 2004, 34, 334-352.
10. Mittal, A.; Davis, L.S. M2Tracker: A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo; Springer-Verlag: Berlin, Germany, 2002; pp. 18-36.
11. Mittal, A.; Davis, L.S. M2Tracker: A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo. Int. J. Comput. Vis. 2004, 51, 189-203.
12. Cai, Q.; Aggarwal, J.K. Tracking human motion in structured environments using a distributedcamera system. IEEE Trans. Patt. Anal. Mach. Int. 1999, 21, 1241-1247.
13. Dejnabadi, H.; Jolles, B.M.; Aminian, K. A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes. IEEE Trans. Biomed. Eng. 2005, 52, 1478-1484.
14. Williamson, R.; Andrews, B. Gait event detection for FES using accelerometers and supervised machine learning. IEEE Trans. Rehab. Eng. 2000, 8, 312-319.
15. Aggarwal, J.K.; Cai, Q. Human Motion Analysis: A Review. In Proc. IEEE Nonrigid and Articulated Motion Workshop, 1997; pp. 90-102.
16. Pavlovic, V.I.; Sharma, R.; Huang, T.S. Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review. IEEE Trans. Patt. Anal. Mach. Int. 1997, 19, 677-695.
17. Boehm, K.; Broll, W.; Sokolewicz, M. Dynamic Gesture Recognition Using Neural Networks: A Fundament for Advanced Interaction Construction. In Proc. SPIE Conference Electronic Imaging Science and Technology, 1994.
18. Ward, J.A.; Lukowicz, P.; Troster, G; Starner, T.E. Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers. IEEE Trans. Patt. Anal. Mach. Int. 2006, 28, 1553-1567.
19. Harville, M. Stereo person tracking with adaptive plan-view statistical templates. In Proc. European Conference on Computer Vision Workshop on Statistical Methods in Video Processing, 2002.
20. Rabiner, L.R. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. IEEE 1989, 77, 257-286.
21. Fine, S.; Singer, Y.; Tishby, N. The Hierarchical Hidden Markov Model: Analysis and Applications. Mach. Learn. 1998, 32, 41-62.
22. Murphy, K.P. Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Thesis, University of California, Berkeley, CA, 2002
23. Burges, C.J.C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discov. 1998, 2, 121-167.
24. Numenta Team. Hierarchical Temporal Memory-Comparison with Existing Models. http://www.numenta.com/for-developers/education/general-overview-htm.php, 2007.
25. Rose, M.E. Elementary Theory of Angular Momentum; Wiley: New York, USA, 1957.
26. Bar-Shalom, Y.; Li, X.R.; Kirubarajan, T. Estimation with Applications to Tracking and Navigation; John Wiley and Sons Inc.: New York, USA, 2001.
27. Numenta Team; Zeta1 Algorithms Reference. http://www.numenta.com/for-developers/education/generaloverview-htm.php, 2007.
28. http://www.xbow.com/Products/productdetails.aspx?sid=160, 2008.