Abstract
In the last few years, research on human activity recognition using the built-in sensors of smartphones instead of the body-worn sensors has received much attention. Accelerometer is the most commonly used sensor of smartphone for the application. An important step in activity recognition is feature extraction from the raw acceleration data. In this work, a novel feature extraction method which considers both the distribution and the rate of change of the raw acceleration data is proposed. The raw time series liner acceleration data was collected by a smartphone application developed by ourselves. The proposed feature extraction method is compared with a previously proposed statistics-based feature extraction method using two evaluation methods: (a) distance matrix before clustering, (b) ARI and FM-index after clustering using MCODE. Both results show that the newly proposed feature extraction method is more effective for daily activity recognition than the previously proposed method.
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 subscriptionsReferences
Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28, 976–990 (2010)
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). doi:10.1007/978-3-540-24646-6_1
Kwapisz, J., Weiss, G., Moore, S.: Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newslett. 12(2), 74–82 (2011)
Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proceedings Conference on Smart Objects and Ambient Intelligence, pp. 159–163. ACM, New York (2005)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.: Activity recognition from accelerometer data. Am. Assoc. Artif. Intell. 05, 1541–1546 (2005)
Wei, Y., Liu, L., Zhong, J., Lu, Y., Sun, L.: Unsupervised race walking recognition using smartphone accelerometers. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS (LNAI), vol. 9403, pp. 691–702. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25159-2_63
Bader, G., Hogue, C.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf. 4(1), 2 (2003)
Lawrence, H., Arabic, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)
Fowlkes, E., Mallows, C.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)
He, Y., Li, Y.: Physical activity recognition utilizing the built-in kinematic sensors of a smartphone. Int. J. Distrib. Sens. Netw. 2013, 10 pages (2013). doi:10.1155/2013/481580. Article ID 481580
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grants No. 61272213) and the Fundamental Research Funds for the Central Universities (Grants No. lzujbky-2016-k07). The authors want to thank the volunteers for their time and effort to help us collecting data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, D., Liu, L., Wang, X., Lu, Y. (2016). A Novel Feature Extraction Method on Activity Recognition Using Smartphone . In: Song, S., Tong, Y. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9998. Springer, Cham. https://doi.org/10.1007/978-3-319-47121-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-47121-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47120-4
Online ISBN: 978-3-319-47121-1
eBook Packages: Computer ScienceComputer Science (R0)