Abstract
Activity-Event-Detector (AED) digraphs can describe relations between human activities, activity-representing pattern events from sensors, and distributed detector nodes. AED graphs have been successfully used to perform network adaptations, including reconfiguring networks to reduce recognition complexity and network energy needs. In this paper, we present an approach to infer AED graph configurations from distributed sensor data. We utilise a non-parametric clustering procedure and derive all relevant information about the AED graph structure, including the detector-specific activity grouping and activity-detector relations from measured data. We analysed our approach using a previously published dataset and compared our inferred AED graph with those designed by an expert. The system based on the inferred AED graph yielded a performance boost of 15% in the final classification accuracy and reduced computational complexity of detectors. These results indicate that our approach is viable to automate the configuration of distributed activity recognition sensor-detector networks.
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
Rashidi, P., Mihailidis, A.: A survey on ambient assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics 17(3), 579–590 (2013)
Schmidt, A.: Context-Aware Computing: Context-Awareness, Context-Aware User Interfaces, and Implicit Interaction (2013), http://www.interaction-design.org/encyclopedia/context-aware_computing.html
Amft, O., Lombriser, C.: Modelling of distributed activity recognition in the home environment. In: EMBC 2011: Proceedings of the 33th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1781–1784. IEEE (2011)
Lombriser, C., Amft, O., Zappi, P., Benini, L., Tröster, G.: Benefits of Dynamically Reconfigurable Activity Recognition in Distributed Sensing Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4, pp. 261–286. World Scientific Publishing Co. (2010)
Aggarwal, J., Ryoo, M.: Human activity analysis: A review. ACM Comput. Surv. 43(3), 16:1–16:43 (2011)
Aloimonos, Y.: Sensory grammars for sensor networks. Journal of Ambient Intelligence and Smart Environments 1(1), 15–21 (2009)
Zappi, P., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Troster, G.: Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness. In: 3rd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 281–286 (2007)
Sarkar, A.M.J., Hasan, K., Lee, Y.-K., Lee, S., Zabir, S.: Distributed activity recognition using key sensors. In: 11th International Conference on Advanced Communication Technology, ICACT 2009, vol. 03, pp. 2245–2250 (2009)
Storf, H., Kleinberger, T., Becker, M., Schmitt, M., Bomarius, F., Prueckner, S.: An event-driven approach to activity recognition in ambient assisted living. In: Tscheligi, M., de Ruyter, B., Markopoulus, P., Wichert, R., Mirlacher, T., Meschterjakov, A., Reitberger, W. (eds.) AmI 2009. LNCS, vol. 5859, pp. 123–132. Springer, Heidelberg (2009)
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Hierarchical activity recognition using automatically clustered actions. In: Keyson, D.V., et al. (eds.) AmI 2011. LNCS, vol. 7040, pp. 82–91. Springer, Heidelberg (2011)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L.: Some infinity theory for predictor ensembles. Tech. rep. (2001)
Zahn, C.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers C-20(1), 68–86 (1971)
Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)
Ulrike, G.: Variable importance assessment in regression: Linear regression versus random forest. The American Statistician 63(4), 308–319 (2009)
Amft, O., Lombriser, C., Stiefmeier, T., Tröster, G.: Recognition of user activity sequences using distributed event detection. In: European Conference on Smart Sensing and Context (EuroSSC), pp. 126–141 (2007)
von Luxburg, U.: Clustering stability: An overview. Foundations and Trends in Machine Learning 2(3), 235–274 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Casale, P., Amft, O. (2013). Inferring Model Structures from Inertial Sensor Data in Distributed Activity Recognition. In: Augusto, J.C., Wichert, R., Collier, R., Keyson, D., Salah, A.A., Tan, AH. (eds) Ambient Intelligence. AmI 2013. Lecture Notes in Computer Science, vol 8309. Springer, Cham. https://doi.org/10.1007/978-3-319-03647-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-03647-2_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03646-5
Online ISBN: 978-3-319-03647-2
eBook Packages: Computer ScienceComputer Science (R0)
