Abstract
Sensor-based human activity recognition aims to automatically identify human activities from a series of sensor observations, which is a crucial task for supporting wide range applications. Typically, given sufficient training examples for all activities (or activity classes), supervised learning techniques have been applied to build a classification model using sufficient training samples for differentiating various activities. However, it is often impractical to manually label large amounts of training data for each individual activities. As such, semi-supervised learning techniques sound promising alternatives as they have been designed to utilize a small training set L, enhanced by a large unlabeled set U. However, we observe that directly applying semi-supervised learning techniques may not produce accurate classification. In this paper, we have designed a novel dynamic temporal extension technique to extend L into a bigger training set, and then build a final semi-supervised learning model for more accurate classification. Extensive experiments demonstrate that our proposed technique outperforms existing 7 state-of-the-art supervised learning and semi-supervised learning techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In: The 23rd International Conference on Architecture of Computing Systems (ARCS), pp. 1–10 (2010)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92–100 (1998)
Cao, H., Nguyen, M.N., Phua, C., Krishnaswamy, S., Li, X.: An integrated framework for human activity classification. In: Ubicomp (2012)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-supervised learning, vol. 2. MIT Press, Cambridge (2006)
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., et al.: The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Magazine, Spec. Issue on Activity-Based Computing 7(2), 32–41 (2008)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1–38 (1977)
Guan, D., Yuan, W., Lee, Y.K., Gavrilov, A., Lee, S.: Activity recognition based on semi-supervised learning. In: 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 469–475 (2007)
Joachims, T.: Making large-scale svm learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods-support Vector Learning (1999)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration
Kidd, C.D., Orr, R., Abowd, G.D., Atkeson, C.G., Essa, I.A., MacIntyre, B., Mynatt, E., Starner, T.E., Newstetter, W.: The aware home: A living laboratory for ubiquitous computing research. In: CoBuild, pp. 191–198 (1999)
Lara, O., Labrador, M.: A survey on human activity recognition using wearable sensors. IEEE Communications Surveys Tutorials PP(99), 1–18 (2002)
Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)
Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: IJCAI, pp. 587–592 (2003)
Li, X.-L., Liu, B.: Learning from positive and unlabeled examples with different data distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 218–229. Springer, Heidelberg (2005)
Li, X.-L., Liu, B., Ng, S.-K.: Learning to classify documents with only a small positive training set. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 201–213. Springer, Heidelberg (2007)
Li, X., Liu, B., Ng, S.: Learning to identify unexpected instances in the test set. In: IJCAI, pp. 2802–2807 (2007)
Li, X., Liu, B., Ng, S.: Negative training data can be harmful to text classification. In: EMNLP, pp. 218–228 (2010)
Liu, B., Lee, W., Yu, P., Li, X.: Partially supervised classification of text documents. In: ICML, pp. 387–394 (2002)
Liu, B., Yang, D., Li, X., Lee, W., Yu, P.: Building text classifiers using positive and unlabeled examples. In: ICDM, pp. 179–186 (2003)
Longstaff, B., Reddy, S., Estrin, D.: Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In: 4th International Conference on Pervasive Computing Technologies for Healthcare, pp. 1–7 (2010)
Nguyen, M., Li, X., Ng, S.: Positive unlabeled learning for time series classification. In: IJCAI, pp. 1421–1426 (2011)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Learning to classify text from labeled and unlabeled documents. In: AAAI, pp. 792–799 (1998)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: AAAI, vol. 20, p. 1541. AAAI Press, MIT Press, Menlo Park, Cambridge (1999, 2005)
Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al.: Collecting complex activity datasets in highly rich networked sensor environments. In: 7’th International Conference on Networked Sensing Systems, pp. 233–240 (2010)
Sagha, H., Digumarti, S.T., del Millan, J., Chavarriaga, R., Calatroni, A., Roggen, D., Troster, G.: Benchmarking classification techniques using the opportunity human activity dataset. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 36–40 (2011)
Stikic, M., Van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 12th IEEE International Symposium on Wearable Computers, pp. 81–88 (2008)
Vail, D.L., Veloso, M.M., Lafferty, J.D.: Conditional random fields for activity recognition. In: Proceedings of 6th International Joint Conference on Autonomous Agents and Multiagent Systems, p. 235 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Davoudi, H., Li, XL., Nhut, N.M., Krishnaswamy, S.P. (2014). Activity Recognition Using a Few Label Samples. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-06608-0_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06607-3
Online ISBN: 978-3-319-06608-0
eBook Packages: Computer ScienceComputer Science (R0)