Abstract
Human activity recognition aims to infer the actions of one or more persons from a set of observations captured by sensors. Usually, this is performed by following a fixed length sliding window approach for the features extraction where two parameters have to be fixed: the size of the window and the shift. In this paper we propose a different approach using dynamic windows based on events. Our approach adjusts dynamically the window size and the shift at every step. Using our approach we have generated a model to compare both approaches. Experiments with public datasets show that our method, employing simpler models, is able to accurately recognize the activities, using fewer instances, and obtains better results than the approaches used by the datasets authors.
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
Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)
Chawla, N.: Data mining for imbalanced datasets: An overview. In: Data Mining and Knowledge Discovery Handbook, pp. 875–886 (2010)
Frank, E., Witten, I.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151. Citeseer (1998)
Gu, T., Wu, Z., Tao, X., Pung, H., Lu, J.: epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition. In: Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications, pp. 1–9. IEEE Computer Society, Los Alamitos (2009)
Hall, M., Smith, L.: Practical feature subset selection for machine learning. Computer Science 98, 4–6 (1998)
Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, p. 163. ACM, New York (2005)
Junker, H., Amft, O., Lukowicz, P., Tröster, G.: Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition 41(6), 2010–2024 (2008)
Kasteren, T., Athanasios, N., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: UbiComp 2008: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM, New York (2008)
Logan, B., Healey, J., Philipose, M., Tapia, E., Intille, S.: A long-term evaluation of sensing modalities for activity recognition. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 483–500. Springer, Heidelberg (2007)
Modayil, J., Bai, T., Kautz, H.: Improving the recognition of interleaved activities. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 40–43. ACM, New York (2008)
Ortiz, J., García, A., Borrajo, D.: A Relational Learning Approach to Activity Recognition from Sensor Readings. In: 4th International IEEE Conference Intelligent Systems, IS 2008, vol. 3 (2008)
Patterson, D., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: Ninth IEEE International Symposium on Wearable Computers, pp. 44–51 (2005)
Quinlan, J.: C4.5: programs for machine learning. Morgan Kaufmann, San Francisco (2003)
Stikic, M., Huynh, T., Van Laerhoven, K., Schiele, B.: ADL recognition based on the combination of RFID and accelerometer sensing. In: 2nd International Conference on Pervasive Computing Technologies for Healthcare (2008)
Tapia, E., Intille, S., Larson, K.: Activity Recognition in the Home Setting Using Simple and Ubiquitous Sensors. LNCS, pp. 158–175 (2004)
Van Kasteren, T., Englebienne, G., Kröse, B.: Towards a Consistent Methodology for Evaluating Activity Recognition Model Performance
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ortiz Laguna, J., Olaya, A.G., Borrajo, D. (2011). A Dynamic Sliding Window Approach for Activity Recognition. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-22362-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22361-7
Online ISBN: 978-3-642-22362-4
eBook Packages: Computer ScienceComputer Science (R0)