Abstract
We propose a novel human activity recognizer for an application for mobile phones. Since such applications should not consume too much electric power, our method should have not only high accuracy but also low electric power consumption by using just a single three-axis accelerometer. In feature extraction with the wavelet transform, we employ the Haar mother wavelet that allows low computational complexity. In addition, we reduce dimensions of features by using the singular value decomposition. In spite of the complexity reduction, we discriminate a user’s status into walking, running, standing still and being in a moving train with an accuracy of over 90%.
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References
Iso, T., Yamazaki, K.: Gait analyzer based on a cell phone with a single three-axis accelerometer. In: Proc. MobileHCI 2006, pp. 141–144 (2006)
Cho, K., Iketani, N., Setoguchi, H., Hattori, M.: Human Activity Recognizer for Mobile Devices with Multiple Sensors. In: Proc. ATC 2009, pp. 114–119 (2009)
Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. In: Proc. IEEE SMC 2001, vol. 2, pp. 747–752 (2001)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. In: Proc. IEEE Transactions on Information Theory, pp. 961–1005 (1990)
Le, T.P., Argou, P.: Continuous wavelet transform for modal identification using free decay response. Journal of Sound and Vibration 277, 73–100 (2004)
Kim, Y.Y., Kim, E.H.: Effectiveness of the continuous wavelet transform in the analysis of some dispersive elastic waves. Journal of the Acoustical Society of America 110, 86–94 (2001)
Shao, X., Pang, C., Su, Q.: A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform. Fresenius, J. Anal. Chem. 367, 525–529 (2000)
Struzik, Z., Siebes, A.: The Haar wavelet transform in the time series similarity paradigm. In: Proc. Principles Data Mining Knowl. Discovery, pp. 12–22 (1999)
Van Loan, C.F.: Generalizing the singular value decomposition. SIAM J. Numer. Anal. 13, 76–83 (1976)
Stewart, G.W.: On the early history of the singular value decomposition. SIAM Rev. 35(4), 551–566 (1993)
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© 2011 Springer-Verlag Berlin Heidelberg
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Maruno, Y., Cho, K., Okamoto, Y., Setoguchi, H., Ikeda, K. (2011). An Online Human Activity Recognizer for Mobile Phones with Accelerometer. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_42
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DOI: https://doi.org/10.1007/978-3-642-24958-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
Online ISBN: 978-3-642-24958-7
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