Abstract
Automated activity recognition systems that use probabilistic models require labeled data sets in training phase for learning the model parameters. The parameters are different for every person and every environment. Therefore, for every person or environment, training is needed to be performed from scratch. Obtaining labeled data requires much effort therefore poses challenges on the large scale deployment of activity recognition systems. Active learning can be a solution to this problem. It is a machine learning technique that allows the algorithm to choose the most informative data points to be annotated. Because the algorithm selects the most informative data points, the amount of the labeled data needed for training the model is reduced. In this study, we propose using active learning methods for activity recognition. We use three different informativeness measures for selecting the most informative data points and evaluate their performances using three real world data sets recorded in a home setting. We show through experiments that the required number of data points is reduced by 80% in House A, 73% in House B, and 66% in House C with active learning.
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References
Altun, K., Barshan, B., Tunc, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition 43, 3605–3620 (2010)
Anderson, B., Siddiqi, S., Moore, A.: Sequence selection for active learning (2006)
Atallah, L., Lo, B., Ali, R., King, R., Yang, G.Z.: Real-time activity classification using ambient and wearable sensors. IEEE Transactions on Information Technology in Biomedicine 13(6), 1031–1039 (2009)
Biswas, J., Tolstikov, A., Jayachandran, M., Foo, V., Aung, A., Wai, P., Phua, C., Huang, W., Shue, L.: Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia. Annals of Telecommunications 65, 505–521 (2010)
Buettner, M., Prasad, R., Philipose, M., Wetherall, D.: Recognizing daily activities with RFID-based sensors. In: 11th International Conference on Ubiquitous Computing, pp. 51–60. ACM (2009)
Cheng, B.C., Tsai, Y.A., Liao, G.T., Byeon, E.S.: HMM machine learning and inference for Activities of Daily Living recognition. The Journal of Supercomputing 54, 29–42 (2010)
Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing 5(4), 277–298 (2009)
Fletcher, R.R., Dobson, K., Goodwin, M.S., Eydgahi, H., Wilder-Smith, O., Fernholz, D., Kuboyama, Y., Hedman, E.B., Poh, M.Z., Picard, R.W.: iCalm: wearable sensor and network architecture for wirelessly communicating and logging autonomic activity. IEEE Transactions on Information Technology in Biomedicine 14(2), 215–223 (2010)
He, J., Li, H., Tan, J.: Real-time daily activity classification with wireless sensor networks using Hidden Markov Model. In: International Conference of the IEEE Engineering in Medicine and Biology Society 2007, pp. 3192–3195 (2007)
Ho, Y., Lu, C., Chen, I., Huang, S., Wang, C., Fu, L.: Active-learning assisted self-reconfigurable activity recognition in a dynamic environment. In: IEEE International Conference on Robotics and Automation, pp. 813–818 (2009)
Hong, Y.J., Kim, I.J., Ahn, S.C., Kim, H.G.: Mobile health monitoring system based on activity recognition using accelerometer. Simulation Modelling Practice and Theory 18(4), 446–455 (2010)
Ince, N.F., Min, C.H., Tewfik, A., Vanderpool, D.: Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors. EURASIP Journal on Advances in Signal Processing 2008, 1–12 (2008)
Kao, T.P., Lin, C.W., Wang, J.S.: Development of a portable activity detector for daily activity recognition. In: IEEE International Symposium on Industrial Electronics, pp. 115–120 (2009)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity Recognition using Cell Phone Accelerometers. In: 4th International Workshop on Knowledge Discovery from Sensor Data, pp. 10–18 (2010)
Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: 11th International Conference on Machine Learning, pp. 148–156 (1994)
Liu, R., Chen, T., Huang, L.: Research on human activity recognition based on active learning. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 285–290 (2010)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Conference on Empirical Methods in Natural Language Processing - EMNLP 2008 (2008)
Stikic, M., van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 12th IEEE International Symposium on Wearable Computers (ISWC 2008), pp. 81–88 (2008)
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Transferring Knowledge of Activity Recognition across Sensor Networks. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive Computing. LNCS, vol. 6030, pp. 283–300. Springer, Heidelberg (2010)
van Kasteren, T.L.M., Noulas, A., Englebienne, G., Kröse, B.J.A.: Accurate activity recognition in a home setting. In: 10th International Conference on Ubiquitous Computing - UbiComp 2008 (2008)
Van Rijsbergen, C.J.: Information Retrieval. Butterworth–Heinemann (1979)
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Alemdar, H., van Kasteren, T.L.M., Ersoy, C. (2011). Using Active Learning to Allow Activity Recognition on a Large Scale. In: Keyson, D.V., et al. Ambient Intelligence. AmI 2011. Lecture Notes in Computer Science, vol 7040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25167-2_12
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DOI: https://doi.org/10.1007/978-3-642-25167-2_12
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