Activity recognition using hybrid generative/discriminative models on home environments using binary sensors

Sensors (Switzerland)

Volumn 13, Issue 5, 2013, Pages 5460-5477

  Javier Ordóñez, Fco ^a   de Toledo, Paula ^a   Sanchis, Araceli ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Activity recognition; Hidden Markov model; Hybrid schemes; Wireless sensor networks

Indexed keywords

ACTIVITIES OF DAILY LIVING; ACTIVITY RECOGNITION; ANN (ARTIFICIAL NEURAL NETWORK); DISCRIMINATIVE MODELS; GENERATIVE/DISCRIMINATIVE MODELS; HYBRID SCHEME; RECOGNITION PERFORMANCE; SIGNIFICANCE LEVELS;

DATA PROCESSING; HIDDEN MARKOV MODELS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; WIRELESS SENSOR NETWORKS;

PATTERN RECOGNITION;

EID: 84877683576     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s130505460     Document Type: Article

References (47)

1. Zola, I.K. Living at Home: The Convergence of Aging and Disability. In Staying Put-Adapting the Places Instead of the People; Lanspery, S., Hyde, J., Eds.; Baywood Publishing: Amityville, NY, USA, 1997; pp. 25-39.
2. Pynoos, J. Neglected Areas in Gerontology: Housing Adaptation. Presentation at the Annual Scientific Meeting of the Gerontological Society of America; Boston, 20-24 November 2002.
3. World Health Organization (WHO). Global Age-Friendly Cities: A Guide; WHO: Geneva, Switzerland, 2007.
4. Milenkovic, A.; Otto, C.; Jovanov, E. Wireless sensor networks for personal health monitoring: Issues and an implementation. Comput. Commun. 2006, 29, 2521-2533.
5. García-Vázquez, J.P.; Rodríguez, M.D.; Andrade, A.G.; Bravo, J. Supporting the strategies to improve elders' medication compliance by providing ambient aids. Pers. Ubiquitous Comput. 2011, 15, 389-397.
6. Turaga, P.; Chellappa, R.; Subrahmanian, V.S.; Udrea, O. Machine recognition of human activities: A survey. IEEE Trans. Circuit. Syst. Video Technol. 2008, 18, 1473-1488.
7. Duong, T.; Phung, D.; Bui, H.; Venkatesh, S. Efficient duration and hierarchical modeling for human activity recognition. Artif. Intell. 2009, 173, 830-856.
8. Patterson, D.J.; Fox, D.; Kautz, H.; Philipose, M. Fine-Grained Activity Recognition by Aggregating Abstract Object Usage. In Proceedings of the Ninth IEEE International Symposium on Wearable Computers, Osaka, Japan, 18-21 October 2005; pp. 44-51.
9. Huynh, T.; Schiele, B. Towards Less Supervision in Activity Recognition from Wearable Sensors. In Proceedings of the 2006 10th IEEE International Symposium on Wearable Computers, Montreux, Switzerland, 11-14 October 2006; pp. 3-10.
10. Corchado, J.M.; Bajo, J.; Tapia, D.I.; Abraham, A. Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare. Trans. Inf. Tech. Biomed. 2010, 14, 234-240.
11. Hervás, R.; Bravo, J.; Fontecha, J. Awareness marks: Adaptive services through user interactions with augmented objects. Pers. Ubiquitous Comput. 2011, 15, 409-418.
12. Jafari, R.; Encarnacao, A.; Zahoory, A.; Dabiri, F.; Noshadi, H.; Sarrafzadeh, M. Wireless Sensor Networks for Health Monitoring. In Proceedings of the Second Annual International Conference on the Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous 2005), San Diego, CA, USA, 17-21 July 2005; pp. 479-481.
13. Tapia, E.M.; Intille, S.S.; Larson, K. Activity recognition in the home using simple and ubiquitous sensors. Pervasive Comput. 2004, 3001, 158-175.
14. Wilson, D.; Atkeson, C. Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors Pervasive Computing; Springer: Berlin/Heidelberg, Germany, 2005; Volume 3468, pp. 329-334.
15. Cook, D.J.; Schmitter-Edgecombe, M. Assessing the quality of activities in a smart environment. Methods Inf. Med. 2009, 48, 480-485.
16. de Ipiña, D.L.; de Sarralde, I.D.; García-Zubia, J. An ambient assisted living platform integrating RFID Data-on-Tag care annotations and twitter. J. Univers. Comput. Sci. 2010, 16, 1521-1538.
17. Van Kasteren, T.L.M.; Noulas, A.; Englebienne, G.; Kröse, B.J. Accurate Activity Recognition in a Home Setting. In Proceedings of the Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Seoul, South Korea, 21-24 September 2007; pp. 1-9.
18. Vail, D.L.; Veloso, M.M.; Lafferty, J.D. Conditional Random Fields for Activity Recognition. In Proceedings of the Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Honolulu, HI, USA, 14-18 May 2007; pp. 235:1-235:8.
19. Ordoñez, F.J.; Iglesias, J.A.; de Toledo, P.; Ledezma, A.; Sanchis, A. Online activity recognition using evolving classifiers. Expert Syst. Appl. 2013, 40, 1248-1255.
20. Van Kasteren, T. Activity recognition for health monitoring elderly using temporal probabilistic models. Ph.D. thesis, University of Amsterdam, Amsterdam, The Netherlands, 27 April 2011.
21. Rynkiewicz, J. Hybrid HMM/MLP models for time series prediction. In Proceedings of 7th European Symposium on Artificial Neural Networks (ESANN 1999), Bruges, Belgium, 21-23 April 1999; pp. 455-462.
22. Bengio, Y. A connectionist approach to speech recognition. Int. J. Pattern Recognit. Artif. Intell. 1993, 7, 647-667.
23. Rigoll, G.; Neukirchen, C. A New Approach to Hybrid HMM/ANN Speech Recognition Using Mutual Information Neural Networks. In Advances in Neural Information Processing Systems 9, NIPS*96; The MIT Press: Cambridge, MA, USA, 1996, pp. 772-778.
24. Bengio, Y.; Lecun, Y.; Nohl, C.; Burges, C. LeRec: A NN/HMM hybrid for on-line handwriting recognition. Neural Comput. 1995, 7, 1289-1303.
25. Marukatat, S.; Artires, T.; Gallinari, P.; Dorizzi, B. Sentence Recognition through Hybrid Neuro-Markovian Modeling. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), Seattle, WA, USA, 10-13 September 2001; pp. 731-735.
26. Cosi, P. Hybrid HMM-NN Architectures for Connected Digit Recognition. In Proceedings of IEEE-INNS-ENNS International Joint Conference on the Neural Networks, Como, Italy, 24-27 July 2000; Volume 5, p. 5085.
27. Stadermann, J.; Rigoll, G. A hybrid SVM/HMM acoustic modeling approach to automatic speech recognition. In Proceedings of the 8th International Conference on Spoken Language Processing (ICSLP), Jeju Island, Korea, 4-8 October 2004.
28. Ganapathiraju, A.; Hamaker, J.; Picone, J. Hybrid SVM/HMM Architectures for Speech Recognition. In Proceedings of the International Conference on Spoken Language Process, Beijing, China, 16-20 October 2000; pp. 504-507.
29. Markov, K.; Nakamura, S. Using hybrid HMM/BN acoustic models: Design and implementation issues. IEICE Trans. Inf. Syst. 2006, E89-D, 981-988.
30. Ordóñez, F.J.; Duque, A.; de Toledo, P.; Sanchís, A. A hybrid HMM/ANN model for activity recognition in the home using binary sensors. Ambient Assisted Living and Home Care - 4th International Workshop, WAAL 2012, Vitoria-Gasteiz, Spain, 3-5 December 2012; pp. 98-105.
31. Lester, J.; Choudhury, T.; Borriello, G. A Practical Approach to Recognizing Physical Activities. In Proceedings of the 4th International Conference on Pervasive Computing, Dublin, Ireland, 7-10 May 2006; pp. 1-16.
32. Van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J. An activity monitoring system for elderly care using generative and discriminative models. Pers. Ubiquitous Comput. 2010, 14, 489-498.
33. Atallah, L.; Yang, G.Z. The use of pervasive sensing for behaviour profiling-A survey. Pervasive Mobile Comput. 2009, 5, 447-464.
34. van Kasteren, T.L.M. Datasets for Activity Recognition. Available online: http://sites.google.com/site/tim0306/ (accessed on 3 April 2013).
35. Rabiner, L.R. A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 1989, 77, 257-286.
36. Schölkopf, B.; Burges, C.; Smola, A. Advances in Kernel Methods: Support Vector Learning; MIT Press: Cambridge, MA, USA, 1998.
37. Bishop, CM. Neural Networks for Pattern Recognition; Oxford University Press: Oxford, UK, 1995.
38. Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 1999,10, 61-74.
39. Espana-Boquera, S.; Castro-Bleda, M.J.; Gorbe-Moya, J.; Zamora-Martinez, F. Improving offline handwritten text recognition with hybrid HMM/ANN models. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 767-779.
40. Bourlard, H.A.; Morgan, N. Connectionist Speech Recognition: A Hybrid Approach; Kluwer Academic Publishers: Norwell, MA, USA, 1993.
41. Brush, A.; Krumm, J.; Scott, J. Activity Recognition Research: The Good, the Bad, and the Future. In Proceedings of the Pervasive 2010 Workshop on How to do Good Research in Activity Recognition, Helsinki, Finland, 17-20 May 2010.
42. Japkowicz, N.; Stephen, S. The class imbalance problem: A systematic study. Intell. Data Anal. 2002, 6, 429-449.
43. Demsar, J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 2006, 7, 1-30.
44. Bao, L.; Intille, S.S. Activity Recognition from User-Annotated Acceleration Data. In Pervasive Computing; Springer: Berlin/Heidelberg, Germany, 2004; pp. 1-17.
45. Quinlan, J.R. C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning), 1st ed.; Morgan Kaufmann: San Francisco, CA, USA, 1992.
46. Cohen, W.W. Fast Effective Rule Induction. In Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, USA, 9-12 July 1995; pp. 115-123.
47. Aha, D.W.; Kibler, D. Instance-based learning algorithms. Mach. Learn. 1991, 6, 37-66.