Abstract
This paper presents the application of Markov Logic Networks(MLN) for the the recognition of Activities of Daily Living (ADL) in a smart home. We describe a procedure that uses raw data from non visual and non wearable sensors in order to create a classification model leveraging logic formal representation and probabilistic inference. SVM and Naive Bayes methods were used as baselines to compare the performance of our implementation, as they have proved to be highly efficient in classification tasks. The evaluation was carried out on a real smart home where 21 participants performed ADLs. Results show not only the appreciable capacities of MLN as a classifier, but also its potential to be easily integrable into a formal knowledge representation framework.
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References
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. Pervasive Computing 2, 158–175 (2004)
Storf, H., Becker, M., Riedl, M.: Rule-based activity recognition framework: Challenges, technique and learning. In: Pervasive Computing Technologies for Healthcare, London, UK, pp. 1–7 (2009)
van Kasteren, T., Krose, B.: Bayesian activity recognition in residence for elders. In: 3rd IET International Conference on Intelligent Environments, Ulm, Germany, pp. 209–212 (2007)
Coutaz, J., Crowley, J.L., Dobson, S., Garlan, D.: Context is key. Communications of the ACM 48(3), 49–53 (2005)
Aggarwal, J., Ryoo, M.: Human activity analysis: A review. ACM Comput. Surv. 43, 1–43 (2011)
Portet, F., Vacher, M., Golanski, C., Roux, C., Meillon, B.: Design and evaluation of a smart home voice interface for the elderly — acceptability and objection aspects. Personal and Ubiquitous Computing (to appear)
Helaoui, R., Niepert, M., Stuckenschmidt, H.: A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems. In: Augusto, J.C., Corchado, J.M., Novais, P., Analide, C. (eds.) ISAmI 2010. AISC, vol. 72, pp. 247–254. Springer, Heidelberg (2010)
Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G.: Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 17–33. Springer, Heidelberg (2008)
Fleury, A., Vacher, M., Noury, N.: SVM-based multi-modal classification of activities of daily living in health smart homes: Sensors, algorithms and first experimental results. IEEE Transactions on Information Technology in Biomedicine 14(2), 274–283 (2010)
Duong, T., Phung, D., Bui, H., Venkatesh, S.: Efficient duration and hierarchical modeling for human activity recognition. Artificial Intelligence 173(7-8), 830–856 (2009)
Naeem, U., Bigham, J.: Activity recognition using a hierarchical framework. In: Pervasive Computing Technologies for Healthcare, pp. 24–27 (2008)
Chen, L., Nugent, C.D.: Ontology-based activity recognition in intelligent pervasive environments. IJWIS 5(4), 410–430 (2009)
Augusto, J.C., Nugent, C.D.: The use of temporal reasoning and management of complex events in smart homes. In: European Conference on Artificial Intelligence, pp. 778–782 (2004)
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. The MIT Press (2007)
Natarajan, S., Bui, H.H., Tadepalli, P., Kersting, K., Wong, W.: Logical Hierarchical Hidden Markov Models for Modeling User Activities. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 192–209. Springer, Heidelberg (2008)
Liao, L.: Location-based Activity Recognition. PhD thesis, University of Washington (2006)
Tran, S.D., Davis, L.S.: Event Modeling and Recognition Using Markov Logic Networks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 610–623. Springer, Heidelberg (2008)
Edwards, W., Grinter, R.: At Home with Ubiquitous Computing: Seven Challenges. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 256–272. Springer, Heidelberg (2001)
Klein, M., Schmidt, A., Lauer, R.: Ontology-centred design of an ambient middleware for assisted living: The case of soprano. In: Kirste, T., König-Ries, B., Salomon, R. (eds.) Towards Ambient Intelligence: Methods for Cooperating Ensembles in Ubiquitous Environments (AIM-CU), 30th Annual German Conference on Artificial Intelligence (KI 2007), Osnabrück, September 10 (2007)
Katz, S.: Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society 31(12), 721–727 (1983)
Vacher, M., Fleury, A., Portet, F., Serignat, J.F., Noury, N.: Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living, pp. 645–673. Intech Book (2010)
Chahuara, P., Portet, F., Vacher, M.: Fusion of audio and temporal multimodal data by spreading activation for dweller localisation in a smart home. In: STAMI, Space, Time and Ambient Intelligence, Barcelona, Spain, July 16-22, pp. 17–22 (2011)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2), 107–136 (2006)
Vacher, M., Portet, F., Fleury, A., Noury, N.: Development of audio sensing technology for ambient assisted living: Applications and challenges. International Journal of E-Health and Medical Communications 2(1), 35–54 (2011)
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Chahuara, P., Fleury, A., Portet, F., Vacher, M. (2012). Using Markov Logic Network for On-Line Activity Recognition from Non-visual Home Automation Sensors. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds) Ambient Intelligence. AmI 2012. Lecture Notes in Computer Science, vol 7683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34898-3_12
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DOI: https://doi.org/10.1007/978-3-642-34898-3_12
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