Inertial Sensors and Their Applications

  • Jussi Collin
  • Pavel Davidson
  • Martti Kirkko-Jaakkola
  • Helena Leppäkoski


Due to the universal presence of motion, vibration, and shock, inertial motion sensors can be applied in various contexts. Development of the microelectromechanical (MEMS) technology opens up many new consumer and automotive applications for accelerometers and gyroscopes. The large variety of application creates different requirements to inertial sensors in terms of accuracy, size, power consumption and cost. It makes it difficult to choose sensors that are suited best for the particular application. Signal processing methods depend on the application and should reflect on the physical principles behind this application. This chapter describes the principles of operation of accelerometers and gyroscopes, different applications involving the inertial sensors. It also gives examples of signal processing algorithms for pedestrian navigation and motion classification.


Inertial Navigation System Inertial Sensor Proof Mass Sensor Unit Allan Variance 
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.


  1. 1.
    Allan, D.W.: Statistics of atomic frequency standards. Proc. IEEE 54(2), 221–230 (1966)CrossRefGoogle Scholar
  2. 2.
    Altun, K., Barshan, B., Tunçel, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recogn. 43, 3605–3620 (2010)MATHCrossRefGoogle Scholar
  3. 3.
    Armenise, M.N., Ciminelli, C., Dell’Olio, F., Passaro, V.: Advances in Gyroscope Technologies. Springer Verlag (2010)Google Scholar
  4. 4.
    Broffit, J.D.: Nonparametric classification. In: P.R. Krishnaiah, L.N. Kanal (eds.) Handbook of Statistics 2. North-Holland (1990)Google Scholar
  5. 5.
    Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering, 3rd edn. John Wiley & Sons (1997)Google Scholar
  6. 6.
    Collin, J.: Investigations of self-contained sensors for personal navigation. Dr. Tech. thesis, Tampere University of Technology (2006). URL
  7. 7.
    Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Computer Graphics and Applications 25(6), 38–46 (2005)CrossRefGoogle Scholar
  8. 8.
    Gianchandani, Y.B., Tabata, O., Zappe, H.P.: Comprehensive microsystems. Elsevier (2008)Google Scholar
  9. 9.
    IEEE Std 528-2001: IEEE standard for inertial sensor terminology. standard, The Institute of Electrical and Electronics Engineers, Inc., New York, NY, U.S.A. (2001)Google Scholar
  10. 10.
    IEEE Std 647-1995: IEEE standard specification format guide and test procedure for single-axis laser gyros. standard, The Institute of Electrical and Electronics Engineers, Inc., New York, NY, U.S.A. (1996)Google Scholar
  11. 11.
    Jahn, J., Batzer, U., Seitz, J., Patino-Studencka, L., Gutiérrez Boronat, J.: Comparison and evaluation of acceleration based step length estimators for handheld devices. In: Proc. Int. Conf. on Indoor Positioning and Indoor Navigation, pp. 1–6. Zurich, Switzerland (2010)Google Scholar
  12. 12.
    Kantola, J., Perttunen, M., Leppänen, T., Collin, J., Riekki, J.: Context awareness for GPS-enabled phones. In: Proc. ION ITM, pp. 117–124. San Diego, CA (2010)Google Scholar
  13. 13.
    Käppi, J., Syrjärinne, J., Saarinen, J.: MEMS-IMU based pedestrian navigator for handheld devices. In: Proc. ION GPS, pp. 1369–1373. Salt Lake City, UT (2001)Google Scholar
  14. 14.
    Keshner, M.S.: 1 ∕ f noise. Proc. IEEE 70(3), 212–218 (1982)CrossRefGoogle Scholar
  15. 15.
    Kirkko-Jaakkola, M., Collin, J., Takala, J.: Bias prediction for MEMS gyroscopes. IEEE Sensors J. (2012). DOI 10.1109/JSEN.2012.2185692Google Scholar
  16. 16.
    Könönen, V., Mäntyjärvi, J., Similä, H., Pärkkä, J., Ermes, M.: Automatic feature selection for context recognition in mobile devices. Pervasive Mob. Comput. 6, 181–197 (2010)CrossRefGoogle Scholar
  17. 17.
    Krobka, N.I.: Differential methods of identifying gyro noise structure. Gyroscopy and Navigation 2, 126–137 (2011)CrossRefGoogle Scholar
  18. 18.
    Ladetto, Q.: On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering. In: Proc. ION GPS, pp. 1735–1740. Salt Lake City, UT (2000)Google Scholar
  19. 19.
    Leland, R.P.: Mechanical-thermal noise in MEMS gyroscopes. IEEE Sensors J. 5(3), 493–500 (2005)CrossRefGoogle Scholar
  20. 20.
    Levi, R.W., Judd, T.: Dead reckoning navigational system using accelerometer to measure foot impacts. U.S. Patent 5,583,776 (1996)Google Scholar
  21. 21.
    Meriheinä, U.: Method and device for measuring the progress of a moving person. U.S. Patent 7,962,309 (2007)Google Scholar
  22. 22.
    Mezentsev, O., Collin, J., Lachapelle, G.: Pedestrian Dead Reckoning – A Solution to Navigation in GPS Signal Degraded Areas. Geomatica 59(2), 175–182 (2005)Google Scholar
  23. 23.
    Misra, P., Enge, P.: Global Positioning System: Signals, Measurements, and Performance, 2nd edn. Ganga–Jamuna Press (2006)Google Scholar
  24. 24.
    Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors. Tech. rep., Xsens Motion Technologies BV (2009)Google Scholar
  25. 25.
    Savage, P.G.: Laser gyros in strapdown inertial navigation systems. In: Proc. IEEE Position, Location, and Navigation Symp. San Diego, CA (1976)Google Scholar
  26. 26.
    Sierociuk, D., Tejado, I., Vinagre, B.M.: Improved fractional Kalman filter and its application to estimation over lossy networks. Signal Process. 91(3), 542–552 (2011)MATHCrossRefGoogle Scholar
  27. 27.
    Stirling, R., Collin, J., Fyfe, K., Lachapelle, G.: An innovative shoe-mounted pedestrian navigation system. In: Proc. ENC GNSS, pp. 110–115. Graz, Austria (2003)Google Scholar
  28. 28.
    Syrjärinne, J., Käppi, J.: Method and apparatus for lowering power use by a ranging receiver. U.S. Patent 7,409,188 (2008)Google Scholar
  29. 29.
    Titterton, D.H., Weston, J.L.: Strapdown Inertial Navigation Technology, 2nd edn. American Institute of Aeronautics and Astronautics, Reston, VA (2004)CrossRefGoogle Scholar
  30. 30.
    Voss, R.F.: 1 ∕ f (flicker) noise: A brief review. In: Proc. 33rd Ann. Symp. Frequency Control, pp. 40–46 (1979)Google Scholar
  31. 31.
    VTI Technologies Oy: SCC1300-D04 combined gyroscope and 3-axis accelerometer with digital SPI interfaces. rev. 1.0.3. Doc.Nr. 82 1131 00 A, Data sheet (2010)Google Scholar
  32. 32.
    Webb, A.: Statistical Pattern Recognition, 2nd edn. John Wiley & Sons, LTD (2002)MATHCrossRefGoogle Scholar
  33. 33.
    Xsens MVN – inertial motion capture. URL

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Jussi Collin
    • 1
  • Pavel Davidson
    • 1
  • Martti Kirkko-Jaakkola
    • 1
  • Helena Leppäkoski
    • 1
  1. 1.Department of Pervasive ComputingTampere University of TechnologyTampereFinland

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