Abstract
Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the rich sensory data that is available on today’s smart phones and other wearable sensors. The state of the art in mobile activity recognition research has focused on traditional classification learning techniques. In this paper, we propose the Mobile Activity Recognition System (MARS) where for the first time the classifier is built on-board the mobile device itself through ubiquitous data stream mining in an incremental manner. The advantages of on-board data stream mining for mobile activity recognition are: i) personalisation of models built to individual users; ii) increased privacy as the data is not sent to an external site; iii) adaptation of the model as the user’s activity profile changes. In our extensive experimental results using a recent benchmarking activity recognition dataset, we show that MARS can achieve similar accuracy when compared with traditional classifiers for activity recognition, while at the same time being scalable and efficient in terms of the mobile device resources consumption. MARS has been implemented on the Android platform for empirical evaluation.
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.
Similar content being viewed by others
References
Bao, L., Intille, S.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)
Gomes, J.B., Krishnaswamy, S., Gaber, M., Sousa, P.A.C., Menasalvas, E.: Mars: a personalised mobile activity recognition system. In: Proceedings of the International Conference on Mobile Data Management, MDM 2012, Bengaluru, India, July 23-26, IEEE (2012)
Bartolo Gomes, J., Menasalvas, E., Sousa, P.A.C.: Situation-aware data stream mining service for ubiquitous applications. In: 2010 Eleventh International Conference on Mobile Data Management (MDM), pp. 360–365. IEEE (2010)
Gama, J., Sebastiao, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338. ACM, New York (2009)
Krishnaswamy, S., Gama, J., Gaber, M.M.: Advances in data stream mining for mobile and ubiquitous environments. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2607–2608. ACM (2011)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2011)
Preece, S., Goulermas, J., Kenney, L., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors – a review of classification techniques. Physiological Measurement 30(4), R1–R33 (2009)
Sagha, H., Digumarti, S.T., Millán, J.d.R., Lozano, R.C., Calatroni, A., Roggen, D., Tröster, G.: Benchmarking classification techniques using the Opportunity human activity dataset. IEEE International Conference on Systems, Man, and Cybernetics (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gomes, J.B., Krishnaswamy, S., Gaber, M.M., Sousa, P.A.C., Menasalvas, E. (2012). Mobile Activity Recognition Using Ubiquitous Data Stream Mining. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-32584-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32583-0
Online ISBN: 978-3-642-32584-7
eBook Packages: Computer ScienceComputer Science (R0)
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.