Abstract
Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemeted to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject’s trousers. Two classifiers were compared, knn (k nearest neighbours) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates. Real-time recognition on the device was tested by seven subjects, three of which were subjects who had not collected data to train the models. Activity recognition rates on the smartphone were encouracing, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. The real-time recognition accuracy using QDA was as high as 95.8%, while using knn it was 93.9%.
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.
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
ActiGraph, http://www.theactigraph.com/
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)
Brezmes, T., Gorricho, J.-L., Cotrina, J.: Activity Recognition from Accelerometer Data on a Mobile Phone. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 796–799. Springer, Heidelberg (2009)
Ermes, M., Pärkkä, J., Mäntyjärvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine 12(1), 20–26 (2008)
Fix, E., Hodges, J.L.: Discriminatory analysis: Nonparametric discrimination: Consistency properties. Tech. Rep. Project 21-49-004, Report Number 4, USAF School of Aviation Medicine, Randolf Field, Texas (1951)
Frank, J., Mannor, S., Precup, D.: Activity Recognition with Mobile Phones. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 630–633. Springer, Heidelberg (2011)
Haapalainen, E., Laurinen, P., Junno, H., Tuovinen, L., Röning, J.: Feature selection for identification of spot welding processes. In: Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics, pp. 40–46 (2006)
Hand, D.J., Mannila, H., Smyth, P.: Principles of data mining. MIT Press, Cambridge (2001)
Ichikawa, F., Chipchase, J., Grignani, R.: Where’s the phone? a study of mobile phone location in public spaces. In: 2005 2nd International Conference on Mobile Technology, Applications and Systems, pp. 1–8 (2005)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12, 74–82 (2011)
Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., Campbell, A.T.: The Jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys 2010, pp. 71–84 (2010)
Nokia N8, http://europe.nokia.com/find-products/devices/nokia-n8
Peebles, D., Lu, H., Lane, N.D., Choudhury, T., Campbell, A.T.: Community-guided learning: Exploiting mobile sensor users to model human behavior. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15 (2010)
Polar Active, http://www.polaroutdoor.com/en/support/product_support?product=29451
Ryder, J., Longstaff, B., Reddy, S., Estrin, D.: Ambulation: A tool for monitoring mobility patterns over time using mobile phones. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 927–931 (2009)
Siirtola, P., Koskimäki, H., Röning, J.: Periodic quick test for classifying long-term activities. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), pp. 135–140 (2011)
Smartphone shipments, http://www.bgr.com/2011/03/10/berg-smartphone-shipments-grew-74-in-2010/
Sun, L., Zhang, D., Li, B., Guo, B., Li, S.: Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 548–562. Springer, Heidelberg (2010)
Suutala, J., Pirttikangas, S., Röning, J.: Discriminative Temporal Smoothing for Activity Recognition from Wearable Sensors. In: Ichikawa, H., Cho, W.-D., Satoh, I., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 182–195. Springer, Heidelberg (2007)
Van Laerhoven, K., Cakmakci, O.: What shall we teach our pants? In: The Fourth International Symposium on Wearable Computers, pp. 77–83 (2000)
Wang, S., Chen, C., Ma, J.: Accelerometer based transportation mode recognition on mobile phones. In: Asia-Pacific Conference on Wearable Computing Systems, pp. 44–46 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Siirtola, P., Röning, J. (2012). User-Independent Human Activity Recognition Using a Mobile Phone: Offline Recognition vs. Real-Time on Device Recognition. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_75
Download citation
DOI: https://doi.org/10.1007/978-3-642-28765-7_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28764-0
Online ISBN: 978-3-642-28765-7
eBook Packages: EngineeringEngineering (R0)