Abstract
In order to promote an effective and personalized interaction, smart environments should be endowed with the capability of understanding what the user is doing. To this aim we developed a system called WoMan that, using a process mining approach, is able to incrementally learn user’s activities and daily routines as workflow models. In order to test its efficacy in a real-world setting, we set up a smart office environment, SOFiA, equipped with a sensor network based on Arduino. Then we collected an annotated dataset of 45 days and from this dataset we learned the workflow models of the user daily routines and of the activities performed in the office. Then we performed some experiments that show how our approach perform in learning and recognizing activities and routines. In particular, we achieve in average the accuracy of 82% for tasks and the accuracy of 98% for the transitions among tasks. Moreover we test the real-time performance of the approach with sensor data coming from the SOFiA sensors and the system started to make a correct prediction since the fourth execution in 82% of the cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
http://www.arduino.cc/ (last consulted on the August 19, 2014)
Anonymized
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)
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)
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)
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)
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)
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)
Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Computing 9, 48–53 (2010)
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)
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)
Riboni, D., Bettini, C.: OWL 2 modeling and reasoning with complex human activities. Pervasive and Mobile Computing 7(3), 379–395 (2011)
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)
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)
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aided Eng. 10(2), 151–162 (2003)
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)
Anonymized
Anonymized
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
De Carolis, B., Ferilli, S., Mallardi, G. (2014). Learning and Recognizing Routines and Activities in SOFiA. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-14112-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14111-4
Online ISBN: 978-3-319-14112-1
eBook Packages: Computer ScienceComputer Science (R0)
