Three-layer activity recognition combining domain knowledge and meta-classification

Journal of Medical and Biological Engineering

Volumn 33, Issue 4, 2013, Pages 406-414

  Kozina, Simon ^a   Gjoreski, Hristijan ^a   Gams, Matjaž ^a   Luštrek, Mitja ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

Activity recognition; Ambient intelligence; Intelligent healthcare; Machine learning; Meta classification; Multi layer activity recognition

Indexed keywords

ACTIVITY RECOGNITION; AMBIENT INTELLIGENCE; ARBITRARY NUMBER; DOMAIN KNOWLEDGE; HIERARCHICAL STRUCTURES; META-CLASSIFICATION; META-CLASSIFIERS; SENSOR PLACEMENT;

ACCELEROMETERS; ARCHITECTURE; HEALTH CARE; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; SENSORS;

PATTERN RECOGNITION;

ACCELERATION; ACCELEROMETER; ACCURACY; ALGORITHM; ARTICLE; CLINICAL CLASSIFICATION; HIDDEN MARKOV MODEL; HUMAN; KNOWLEDGE; PREDICTION; PROBABILITY; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; THREE LAYER ACTIVITY RECOGNITION;

EID: 84883828367     PISSN: 16090985     EISSN: None     Source Type: Journal    
DOI: 10.5405/jmbe.1321     Document Type: Article

References (38)

1. United Nations 2009, World population ageing, Report.
2. A. Bourouis, M. Feham and A. Bouchachia, "A new architecture of a ubiquitous health monitoring system: a prototype of cloud mobile health monitoring system," The Comput. Res. Repository, 2012.
3. M. Lustrek, B. Kaluza, B. Cvetkovic, E. Dovgan, H. Gjoreski, V. Mirchevska and M. Gams, "Confidence: ubiquitous care system to support independent living," Eur. Conf. Artif. Intell. (demo track), 1013-1014, 2012.
4. D. A. Gregory, K. D. Anind, J. B. Peter, D. Nigel, S. Mark and S. Pete, "Towards a better understanding of context and context-awareness," Proc. 1st Int. Symp. Handheld Ubiquitous Comput., 304-307, 1999.
5. N. Vyas, J. Farringdon, D. Andre and J. I. Stivoric, "Machine learning and sensor fusion for estimating continuous energy expenditure," Proc. Innovative Appl. Artif. Intell. Conf., 1613-1620, 2012.
6. H. Gjoreski, M. Luštrek and M. Gams, "Accelerometer placement for posture recognition and fall detection," Proc. Intell. Environ. Conf., 47-54, 2011.
7. PAMSys-The physical activity monitoring system, 2012. Available: http://www.biosensics.com/pamsys.html
8. The European FP7 Confidence project, 2012. Available: http:// www.confidence-eu.org
9. CHIRON project (Cyclic and person-centric health management: Integratedapproachforhome,mobileandclinical environments), 2012. Available: http:// www.chiron-project.eu/
10. H. Gjoreski, M. Luštrek and M. Gams, "Context-based fall detection using inertial and location sensors," Proc. Int. Joint Conf. Ambient Intell., 1-16, 2012.
11. E. Dovgan, M. Luštrek, B. Pogorelc, A. Gradišek, H. Burger and M. Gams, "Intelligent elderly-care prototype for fall and disease detection," Zdravstveni Vestnik, 80: 824-831, 2011.
12. B. Kaluža and M. Gams, "Analysis of daily-living dynamics," J. Ambient Intell. Smart Environ., 4: 403-413, 2012.
13. H. Gjoreski, "Adaptive human activity recognition and fall detection using wearable sensors," Msc Thesis, Jozef Stefan Int. Postgraduate School, 2011.
14. G. Sukthankar and K. Sycara, "A cost minimization approach to human behavior recognition," Proc. Int. Conf. Autonom. Agents, 1067-1074, 2005.
15. J. R. Kwapisz, G. M. Weiss and S. A. Moore, "Activity recognition using cell phone accelerometers," SIGKDD Explor. Newsl., 12: 74-82, 2010.
16. H. Wu, E. D. Lemaire and N. Baddour, "Activity change-of-state identification using a Blackberry smartphone," J. Med. Biol. Eng., 32: 265-272, 2012.
17. C. Lai, Y. M. Huang, J. H. Park and H. C. Chao, "Adaptive body posture analysis for elderly-falling detection with multisensors," IEEE Intell. Syst., 25: 20-30, 2010.
18. O. Banos, M. Damas, H. Pomares, F. Rojas, B. Delgado-Marquez and O. Valenzuela, "Human activity recognition based on a sensor weighting hierarchical classifier," Soft Comput., 17: 333-343, 2013.
19. J. Lester, T. Choudhury and G. Borriello, "A practical approach to recognizing physical activities," Lecture Notes in Comput. Sci., 3968: 1-16, 2006.
20. J. Lester, T. Choudhury, N. Kern, G. Borriello and B. Hannaford, "A hybrid discriminative/generative approach for modeling human activities," Proc. Inter. Joint Conf. Artif. Intell., 766-772, 2005.
21. N. Ravi, N. Dandekar, P. Mysore and M. L. Littman, "Activity recognition from accelerometer data," Proc. 17th Conf. Innovative Appl. Artif. Intell., 3: 1541-1546, 2005.
22. P. Zappi, T. Stiefmeier, E. Farella, D. Roggen, L. Benini and G. Troster, "Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness," Proc. Int. Conf. Intell Sens., Sens. Networks Inf., 281-286, 2007.
23. T. G. Dietterich, "Ensemble methods in machine learning," Proc. 1st Int. Workshop Multiple Classifier Syst., 1-15, 2000.
24. B. Kaluža, "Reducing spurious activity transitions in a sequence of movement," Proc. 18th Int. Electrotechnical Comput. Sci. Conf., 163-166, 2009.
25. E. M. Tapia, "Using machine learning for real-time activity recognition and estimation of energy expenditure," Ph.D. Thesis, Massachusetts Institute of Technology, 2008.
26. J. Hausmann and K. Wac, "Activity level estimator on a commercial mobile phone: a feasibility study," Proc. Int. Workshop Front. Act. Recognition using Pervasive Sens., 42-47, 2011.
27. I. Witten and E. Frank, Data mining: practical machine learning tools and techniques (Third edition), San Francisco: Morgan Kaufmann, 2011.
28. M. Gams, Weak intelligence: through the principle and paradox of multiple knowledge, New York: Nova Science Publishers, 2001.
29. L. Breiman, "Random forests," Mach. Learn., 45: 5-32, 2001.
30. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, 77: 257-286, 1989.
31. L. E. Baum, T. Petrie, G. Soules and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains," Ann. Math. Stat., 41: 164-171, 1970.
32. J. Viterbi, "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm," IEEE Trans. Inf. Theory, 13: 260-269, 1967.
33. Shimmer sensor platform. Available: http://www.shimmer -research.com/
34. M. Žbogar, H. Gjoreski, S. Kozina and M. Luštrek, "Improving accelerometer based activity recognition," Proc. 15th Int. Multiconf. Inf. Soc., 167-170, 2012.
35. Weka application programming interface (API), 2012. Available: http://weka.sourceforge.net/doc/
36. M. Maier, M. Hein and U. von Luxburg, "Optimal construction of k-nearest neighbor graphs for identifying noisy clusters," Theor. Comput. Sci., 410: 1749-1764, 2009.
37. Jahmm (Java implementation of HMM related algorithms), 2012. Available:http://www.run.montefiore.ulg.ac.be/~francois/ software/jahmm
38. T. L. M. van Kasteren, H. Alemdar and C. Ersoy, "Effective performance metrics for evaluating activity recognition methods," Proc. Int. Conf. Archit. Comput. Syst., 301-310, 2011.