Centinela: A human activity recognition system based on acceleration and vital sign data

Pervasive and Mobile Computing

Volumn 8, Issue 5, 2012, Pages 717-729

  Lara, Óscar D ^a   Prez, Alfredo J ^a   Labrador, Miguel A ^a   Posada, Jos D ^b  

^a University of South Florida   (United States)

^b Departamento de Ingeniera Electrnica   (Colombia)

Author keywords

Classification; Feature extraction; Subject independent analysis; Transient features

Indexed keywords

CELLULAR TELEPHONE SYSTEMS; CLASSIFICATION (OF INFORMATION);

ACTIVITY RECOGNITION; CLASSIFICATION ACCURACY; DIFFERENT TIME WINDOWS; HUMAN ACTIVITY RECOGNITION SYSTEMS; INDEPENDENT ANALYSIS; OVERALL ACCURACIES; REAL TIME DATA COLLECTIONS; TRANSIENT FEATURES;

FEATURE EXTRACTION;

EID: 84865789171     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2011.06.004     Document Type: Conference Paper

References (31)

1. A.J. Perez, M.A. Labrador, and S.J. Barbeau G-sense: a scalable architecture for global sensing and monitoring IEEE Network 24 4 2010 57 64
2. J. Yin, Q. Yang, and J. Pan Sensor-based abnormal human-activity detection IEEE Transactions on Knowledge and Data Engineering 20 8 2008 1082 1090
3. E. Kim, S. Helal, and D. Cook Human activity recognition and pattern discovery IEEE Pervasive Computing 9 1 2010 48 53
4. P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea Machine recognition of human activities: a survey IEEE Transactions on Circuits and Systems for Video Technology 18 11 2008 1473 1488
5. R.T. Olszewski, C. Faloutsos, D.B. Dot, Generalized feature extraction for structural pattern recognition in time-series data, Tech. rep., In Time-Series Data, Ph.D. dissertation, Carnegie Mellon University, 2001.
6. F. Foerster, M. Smeja, and J. Fahrenberg Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring Computers in Human Behavior 15 5 1999 571 583
7. Z.-Y. He, L.-W. Jin, Activity recognition from acceleration data using ar model representation and svm, in: Proceedings of International Conference on Machine Learning and Cybernetics, vol. 4, 2008, pp. 22452250.
8. Z. He, Z. Liu, L. Jin, L.-X. Zhen, J.-C. Huang, Weightlessness feature; a novel feature for single tri-axial accelerometer based activity recognition, in: Proceedings of the 19th International Conference on Pattern Recognition, 2008, pp. 14.
9. Y. Hanai, J. Nishimura, T. Kuroda, Haar-like filtering for human activity recognition using 3d accelerometer, in: Proceedings of the 13th IEEE Digital Signal Processing Workshop, 2009, pp. 675678.
10. Z. He, L. Jin, Activity recognition from acceleration data based on discrete consine transform and svm, in: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 50415044.
11. Y.-P. Chen, J.-Y. Yang, S.-N. Liou, G.-Y. Lee, and J.-S. Wang Online classifier construction algorithm for human activity detection using a tri-axial accelerometer Applied Mathematics and Computation 205 2 2008 849 860
12. L. Bao, S.S. Intille, Activity recognition from user-annotated acceleration data, in: IEEE Pervasive, 2004, pp. 117.
13. U. Maurer, A. Smailagic, D.P. Siewiorek, M. Deisher, Activity recognition and monitoring using multiple sensors on different body positions, in: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, 2006, pp. 113116. (Pubitemid 44711293)
14. J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, and I. Korhonen Activity classification using realistic data from wearable sensors IEEE Transactions on Information Technology in Biomedicine 10 1 2006 119 128 (Pubitemid 43092325)
15. E.M. Tapia, S.S. Intille, W. Haskell, K. Larson, J. Wright, A. King, R. Friedman, Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor, in: Proceedings of the International Symposium on Wearable Computing, 2007.
16. M. Ermes, J. Parkka, L. Cluitmans, Advancing from offline to online activity recognition with wearable sensors, in: Proceedings of the 30th IEEE Annual International Conference of Engineering in Medicine and Biology Society, 2008, pp. 44514454.
17. T.-P. Kao, C.-W. Lin, J.-S. Wang, Development of a portable activity detector for daily activity recognition, in: Proceedings of IEEE International Symposium on Industrial Electronics, 2009, pp. 115120.
18. L.C. Jatoba, U. Grossmann, C. Kunze, J. Ottenbacher, W. Stork, Context-aware mobile health monitoring: evaluation of different pattern recognition methods for classification of physical activity, in: Proceedings of the 30th Annual IEEE International Conference in Engineering in Medicine and Biology Society, 2008, pp. 52505253.
19. C. Randell, H. Muller, Context awareness by analysing accelerometer data, in: Proceedings of the Fourth International Symposium on Wearable Computers, 2000, pp. 175176. (Pubitemid 32072879)
20. Zephyr Bioharness BT Website, http://www.zephyr-technology.com/ bioharness-bt.html.
21. Sprint Java ADP, http://developer.sprint.com/site/global/home/phome.jsp.
22. G. Dahlquist, and A. Bjorck Numerical methods in scientific computing, vol. 1 2008 SIAM
23. H. Zhang, The optimality of naive Bayes, in: Proceedings of FLAIRS Conference, 2004.
24. P. Antal, Construction of a classifier with prior domain knowledge formalised as Bayesian network, in: Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, vol. 4, 1998, pp. 25272531.
25. I.H. Witten, and E. Frank Data Mining, Practical Machine Learning Tools and Techniques 2nd ed. 2005 Elsevier
26. J. Friedman, T. Hastie, and R. Tibshirani Additive logistic regression: a statistical view of boosting Annals of Statistics 28 1998 2000
27. W.J. Conover Practical Nonparametric Statistics 3rd ed. 1999 Wiley
28. Waikato Environment for Knowledge Analysis (WEKA), http://www.cs.waikato. ac.nz/ml/weka/.
29. S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava Using mobile phones to determine transportation modes ACM Transactions on Sensor Networks 6 2 2010 1 27
30. C. Zhu, W. Sheng, Human daily activity recognition in robot-assisted living using multi-sensor fusion, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2009, pp. 21542159.
31. M. Berchtold, M. Budde, D. Gordon, H. Schmidtke, M. Beigl, Actiserv: Activity recognition service for mobile phones, in: Proceedings of the 2010 International Symposium on Wearable Computers, 2010, pp. 18.