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http://dx.doi.org/10.3745/KTSDE.2013.2.1.049

Design of an Activity Recognition System using Smartphone Accelerometer  

Kim, Joo-Hee (경기대학교 컴퓨터과학과)
Nam, Sang-Ha (경기대학교 컴퓨터과학과)
Heo, Se-Kyeong (경기대학교 컴퓨터과학과)
Kim, In-Cheol (경기대학교 컴퓨터과학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.1, 2013 , pp. 49-54 More about this Journal
Abstract
Activity recognition using smartphone accelerometer suffers from the user dependency problem that acceleration patterns of one user differ from those of others for the same activity. Moreover, it also suffers from the position dependency problem since a smartphone may be placed in any pockets or hands. In order to overcome these problems, this paper proposes an effective activity recognition method which is less dependent with both specific users and specific positions of the smartphone. Based on the proposed method, we implement a real-time activity recognition system working on an Android smartphone. Throughout some experiments with 6642 examples collected from different users and different positions, we investigate the performance of our activity recognition system.
Keywords
Activity Recognition; Smartphone; Accelerometer; SVM;
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1 O. W. H. Wu, A. a T. Bui, M. a Batalin, L. K. Au, J. D. Binney, and W. J. Kaiser, "MEDIC: Medical Embedded Device for Individualized Care", Artificial intelligence in Medicine, Vol.42, No.2, pp.137-52, Feb., 2008.   DOI   ScienceOn
2 Y. Chiang, Y. Tsao, and J. Hsu, "A Framework for Activity Recognition in a Smart Home", Proceedings of the International Conference on Technologies and Applications of Artificial Intelligence (TAAI). 2010.
3 G. Bieber, A. Luthardt, C. Peter, and B. Urban, "The Hearing Trousers Pocket: Activity Recognition by Alternative Sensors", Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), 2011.
4 C. Qin and X. Bao, "TagSense: A Smartphone-based Approach to Automatic Image Tagging", Proceedings of the 9th International Conference on Mobile Systems, Applications, and Sevices(MobiSys), pp.1-14, 2011.
5 X. Long, B. Yin, and R. M. Aarts, "Single- Accelerometer-Based Daily Physical Activity Classification", Conference Proceeding of IEEE Engineering in Medicine and Biology Society, pp.6107-6110, 2009.
6 L. Bao and S. S. Intille, "Activity Recognition from User-Annotated Acceleration Data", Proceedings of the International Conference on Pervasive Computing, Lecture Notes in Computer Science, Vol.3001, pp.1-17, 2004.
7 A. M. Khan, Y. K. Lee, S. Y. Lee, T. S. Kim, "Human Activity Recognition via An Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis", Proceedings of the 5th International Conference on Future Information Technology (FutureTech), pp.1-6, 2010.
8 T. S. Saponas, J. Lester, J. Froehlich, J. Fogarty, J. Landay, "iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones", University of Washington CSE Technical Report UW-CSE-08-04-02, 2008.
9 L. Sun, D. Zhang, B. Li, B. Guo, and S. Li, "Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations", Proceedings of the International Conference on Ubiquitous Intelligence and Computing, Lecture Notes in Computer Science, Vol.6406, pp.548-562, 2010.
10 J. R. Kwapisz, G. M. Weiss, S. A. Moore, "Activity Recognition using Cell Phone Accelerometers", ACM SIGKDD Explorations Newsletter, Vol.12, No.2, pp.74-82, 2010.
11 M. F. A. bin Abdullah, A. F. P. Negara, M. S. Sayeed, D. Choi, K. S. Muthu, "Classification Algorithms in Human Activity Recognition using Smartphones", International Journal of Computer and Information Engineering, Vol.6, pp.77-84, 2012.