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Step Length Estimation and Activity Detection in a PDR System Based on a Fuzzy Model with Inertial Sensors

  • Mariana Natalia Ibarra-Bonilla
  • Ponciano Jorge Escamilla-Ambrosio
  • Juan Manuel Ramirez-Cortes
  • Jose Rangel-Magdaleno
  • Pilar Gomez-Gil
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

This chapter presents an approach on pedestrian dead reckoning (PDR) which incorporates activity classification over a fuzzy inference system (FIS) for step length estimation. In the proposed algorithm, the pedestrian is equipped with an inertial measurement unit attached to the waist, which provides three-axis accelerometer and gyroscope signals. The main goal is to integrate the activity classification and step-length estimation algorithms into a PDR system. In order to improve the step-length estimation, several types of activities are classified using a multi-layer perceptron (MLP) neural network with feature extraction based on statistical parameters from wavelet decomposition. This work focuses on classifying activities that a pedestrian performs routinely in his daily life, such as walking, walking fast, jogging and running. The step-length is dynamically estimated using a multiple-input–single-output (MISO) fuzzy inference system. Results provide an average classification rate of 87.49 % with an accuracy on step-length estimation about 92.57 % in average.

Notes

Acknowledgments

The first author acknowledges the financial support from the Mexican National Council for Science and Technology (CONACYT), scholarship No. 237756.

References

  1. 1.
    Gartner, G., Ortag, F.: Advances in Location-based Services, Lecture Notes in Geoinformation and Cartography. Springer-Verlag, Berlin (2012)Google Scholar
  2. 2.
    Sun, Z., Mao, X., Tian, W., Zhang, X.: Activity classification and dead reckoning for pedestrian navigation with wearable sensors. Measur. Sci. Technol. 20(1), 1–10 (2009)Google Scholar
  3. 3.
    Altun, K., Barshan, B.: Pedestrian dead reckoning employing simultaneous activity recognition cues. Measur. Sci. Technol. 23(2), 1–20 (2012)Google Scholar
  4. 4.
    Bancroft, J.B., Garrett, D., Lachapelle, G.: Activity and environment classification using foot mounted navigation sensors. In: Proceedings of International Conference on Indoor Positioning and Indoor Navigation, pp. 13–15. Sydney, NSW (2012)Google Scholar
  5. 5.
    Chen, X., Hu, S., Shao, Z., Tan, J.: Pedestrian positioning with physical activity classification for indoors. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1311–1316. Shanghai, China May, 2011Google Scholar
  6. 6.
    Panahandeh, G., Mohammadiha, N., Leijon, A., Handel, P.: Continuous hidden markov model for pedestrian activity classification and gait analysis. IEEE Trans. Instrum. Meas. 62(5), 1073–1083 (2013)CrossRefGoogle Scholar
  7. 7.
    Mathie, M.J., Celler, B.G., Lovell, N.H., Coster, A.C.F.: Classification of basic daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 42(5), 679–687 (2004)CrossRefGoogle Scholar
  8. 8.
    Yang, C.C., Hsu, Y.L.: A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8), 7772–7788 (2010)CrossRefGoogle Scholar
  9. 9.
    Mannini, A., Sabatini, A.M.: Accelerometry-based classification of human activities using markov modeling. Comput. Intell. Neurosci. 2011, 2–9 (2011)CrossRefGoogle Scholar
  10. 10.
    Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D.: A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 56(3), 871–879 (2009)CrossRefGoogle Scholar
  11. 11.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: Proceedings of 17th Conference on Innovative Applications of Artificial Intelligence, vol. 5, pp. 1541–1546. Pittsburgh, Pennsylvania (2005)Google Scholar
  12. 12.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newslett. 12(2), 74–82 (2011)CrossRefGoogle Scholar
  13. 13.
    Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10(1), 156–167 (2006)CrossRefGoogle Scholar
  14. 14.
    Ning, W., Ambikairajah, E., Lovell, N.H., Celler, B.G.: Accelerometry based classification of walking patterns using time-frequency analysis. In: Proceedings of 29th IEEE Annual International Conference Engineering in Medicine and Biology Society, pp. 4899–4902. Lyon, France, 22–26 Aug 2007Google Scholar
  15. 15.
    Yunqian, M.A., Hesch, J.A.: Gait classification using wavelet descriptors in pedestrian navigation. In: Proceedings of 24th Institute of Navigation GNSS Conference, pp. 1328–1337. Portland, Oregon (2011)Google Scholar
  16. 16.
    West, B.J., Scafetta, N.: Nonlinear dynamical model of human gait. Phys. Rev. E. 67(5), 1–10 (2003)Google Scholar
  17. 17.
  18. 18.
    Priestley, M.B.: Wavelets and time-dependent spectral analysis. J. Time Ser. Anal. 17(1), 85–103 (2008)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Pinsky, M.A.: Introduction to Fourier Analysis and Wavelets. Graduate Studies in Mathematics, (102), American Mathematical Society (2009)Google Scholar
  20. 20.
    Demuth, H., Beale, M.: Neural Network Toolbox for Use with MATLAB, pp. 5-28–5-30. The Mathworks Inc, Natick (2001)Google Scholar
  21. 21.
    Kim, Y.K., Park, J.H., Kim, H.W., Hwang, S.Y., Lee, J.M.: Step estimation in accordance with wear position using the 3-axis accelerometer. In: Proceedings of 3rd SPENALO International Symposium. Bexco, Busan, Korea, Sep 2011Google Scholar
  22. 22.
    Nam, Y.: Map-based indoor people localization using an inertial measurement unit. J. Inf. Sci. Eng. 27(4), 1233–1248 (2011)MathSciNetGoogle Scholar
  23. 23.
    Ibarra-Bonilla, M.N., Escamilla-Ambrosio, P.J., Ramírez-Cortes, J.M.: Pedestrian dead reckoning towards indoor location based applications. In: Proceedings of 8th International Conference on Electrical Engineering Computing Science and Automatic Control, Yucatán, México, Oct 2011Google Scholar
  24. 24.
    Park, S.K., Suh, Y.S.: A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems. Sensors 10(10), 9163–9178 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mariana Natalia Ibarra-Bonilla
    • 1
  • Ponciano Jorge Escamilla-Ambrosio
    • 2
  • Juan Manuel Ramirez-Cortes
    • 1
  • Jose Rangel-Magdaleno
    • 1
  • Pilar Gomez-Gil
    • 3
  1. 1.Department of ElectronicsInstituto Nacional de Astrofísica, Óptica y ElectrónicaTonantzintlaMéxico
  2. 2.Centro de Investigacion en ComputacionInstituto Politécnico NacionalMexico CityMexico
  3. 3.Computer Science DepartmentInstituto Nacional de Astrofísica, Óptica y ElectrónicaTonantzintlaMéxico

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