Learning and recognizing routines and activities in SOFiA

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8850, Issue , 2014, Pages 191-204

  De Carolis, Berardina ^a   Ferilli, Stefano ^a   Mallardi, Giulio ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

DAILY ROUTINES; NOCV1; PROCESS MINING; REAL TIME PERFORMANCE; REAL WORLD SETTING; SENSOR DATA; SMART ENVIRONMENT; SMART OFFICES; WORKFLOW MODELS;

SENSOR NETWORKS;

EID: 84996590432     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-14112-1_16     Document Type: Article

References (21)

1. Ramos, C., Marreiros, G., Santos, R., Freitas, F.C.: Smart Offices and Intelligent Decision Rooms. In: Handbook Ambient Intelligence Smart Environ VII, pp. 851-880. Springer (2010)
2. http://www.arduino.cc/ (last consulted on the August 19, 2014)
3. Anonymized
4. Marsa-Maestre, I., de la Hoz, E., Alarcos, B., Velasco, J.R.: A hierarchical, agent-based approach to security in smart offices. In: Proceedings of the First International Conference on Ubiquitous Computing (ICUC-2006) (2006)
5. Fiore, L., Fehr, D., Bodor, R., Drenner, A., Somasundaram, G., Papanikolopoulos, N.: Multi-Camera Human Activity Monitoring. Journal of Intelligent and Robotic Systems 52(1), 5-43 (2008)
6. Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine 10(1), 119-128 (2006)
7. Patterson, D.J., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: Proc. of IEEE International Symposium on Wearable Computers, pp. 44-51 (2005)
8. Gu, T., Wang, L., Wu, Z., Tao, X., Lu, J.: A Pattern Mining Approach to Sensor-Based Human Activity Recognition. IEEE Transactions on Knowledge and Data Engineering 23(9), 1359-1372 (2011)
9. van Kasteren, T.L.M., Englebienne, G., Kröse, B.: Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. In: Activity Recognition in Pervasive Intelligent Environments, Atlantis Ambient and Pervasive Intelligence series. Atlantis Press (2010)
10. Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Computing 9, 48-53 (2010)
11. Hu, D.H., Yang, Q.: Cigar: Concurrent and interleaving goal and activity recognition. In: 23rd AAAI Conference on Artificial Intelligence (AAAI’08), pp. 1363-1368 (2008)
12. Chen, L., Nugent, C., Mulvenna, M., Finlay, D., Hong, X.: A Logical Framework for Behaviour Reasoning and Assistance in a Smart Home. Int’l J. Assistive Robotics and Mechatronics 9(4), 20-34 (2008)
13. Riboni, D., Bettini, C.: OWL 2 modeling and reasoning with complex human activities. Pervasive and Mobile Computing 7(3), 379-395 (2011)
14. Helaoui, R., Riboni, D., Stuckenschmidt, H.: A probabilistic ontological framework for the recognition of multilevel human activities. In: International Conference on Ubiquitous Computing (UbiComp2013), pp. 345-354 (2013)
15. Aztiria, A., Augusto, J.C., Basagoiti, R., Izaguirre, A., Cook, D.J.: Learning Frequent Behaviors of the Users in Intelligent Environments. IEEE T. Systems, Man, and Cybernetics: Systems 43(6), 1265-1278 (2013)
16. Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from eventbased data using little thumb. Integr. Comput.-Aided Eng. 10(2), 151-162 (2003)
17. Serral, E., Valderas, P., Pelechano, V.: Supporting runtime system evolution to adapt to user behaviour. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 378-392. Springer, Heidelberg (2010)
18. Anonymized
19. Anonymized
20. Cook, D.J., Das, S.K.: How smart are our environments? an updated look at the state of the art. Pervasive Mobile Computing 3(2), 53-73 (2007)
21. van der Aalst, W., Weijters, T., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Trans. on Knowl. and Data Eng. 16(9), 1128-1142 (2004)