Movement Disorder Assessment and Attenuation Techniques for Removal of Tremor

  • Wesley Teskey
  • Mohamed Elhabiby
  • Naser El-Sheimy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)


A weighted-frequency Fourier linear combiner (WFLC) filter is used for removal of tremor motion for data captured from movement disorders subjects with essential tremor (ET) and Parkinson’s disease (PD). This technique is applied here in six degrees-of-freedom and data are filtered so that a comparison can be made before and after filtering to assess the extent to which the WFLC filter removed tremor. A wavelet spectral analysis is employed to determine the effectiveness of the filter in removing tremor in the 3-12 Hz band of interest. A Kalman filter is employed to improve data processing so that six degree-of-freedom tremor motion can be accurately rendered for subsequent filtering; such accurate rendering is needed so that full tremor motion can be adequately described. A Fourier coherence based technique is utilized so that relationships for interrelated tremors for the different six degrees-of-freedom can be identified. Much of the analysis shown is novel.


Weighted-frequency fourier linear combiner (WFLC) Wavelets Accelerometers Gyroscopes Essential tremor Parkinson’s disease Kalman filter 


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  1. 1.
    Rocon, E., Belda-Louis, J.M., Sanchez-Lacuesta, J.J., Pons, J.L.: Pathological Tremor Management: Monitoring, Compensatory Technology and Evaluation. Technology and Disability 16, 3–18 (2004)Google Scholar
  2. 2.
    Rocon de Lima, E., Andrade, A.O., Pons, J.L., Kyberd, P., Nasuto, S.J.: Empirical Mode Decomposition: a Novel Technique for the Study of Tremor Time Series. Med. Bio. Eng. Comput. 44, 569–582 (2006)CrossRefGoogle Scholar
  3. 3.
    Gallego, J.A., Rocon, E., Roa, J.O., Moreno, J.C., Koutsou, A.D., Pons, J.L.: On the use of inertial measurement units for real-time quantification of pathological tremor amplitude and frequency. In: Proceedings of the Eurosensors XXIII Conference. Procedia Chemistry, vol. 1, pp. 1219–1222 (2009)Google Scholar
  4. 4.
    Louis, E.D.: Essential tremor. Lancet Neurol. 4, 100–110 (2005)CrossRefGoogle Scholar
  5. 5.
    Mansur, P.H.G., Cury, L.K.P., Andrade, A.O., Pereira, A.A., Miotto, G.A.A., Soares, A.B., Naves, E.L.M.: A Review on Techniques for Tremor Recording and Quantification. Critical Reviews in Biomedical Engineering 35(5), 343–362 (2007)CrossRefGoogle Scholar
  6. 6.
    Elble, R.J., Koller, W.C.: Tremor. The John Hopkins University Press, Baltimore (1990)Google Scholar
  7. 7.
    Sabatini, A.M.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Transactions on Biomedical Engineering 53(7), 1346–1356 (2006)CrossRefGoogle Scholar
  8. 8.
    El-Sheimy, N., Hou, H., Niu, X.: Analysis and modeling of inertial sensors using Allan variance. IEEE Transactions on Instrumentation and Measurement 57(1), 140–149 (2008)CrossRefGoogle Scholar
  9. 9.
    Shin, E.: Estimation Techniques for Low Cost Inertial Navigation. Ph.D. Department of Geomatics Engineering, University of Calgary (2005)Google Scholar
  10. 10.
    Stockwell, W.: Angle Random Walk. Crossbow Technologies Inc., pp. 1–4 (2010), (accessed July 13, 2010)
  11. 11.
    Chui, C.K., Chen, G.: Kalman Filtering With Real Time Applications, 2nd edn. Springer, Heidelberg (1991)zbMATHGoogle Scholar
  12. 12.
    Grewal, M.S.: Kalmen Filtering: Theory and Practice. Prentice-Hall (1993)Google Scholar
  13. 13.
    Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering, 2nd edn. John Wiley and Sons Inc. (1992)Google Scholar
  14. 14.
    Altmann, S.L.: Rotations, Quaternions and Double Group. Dover Publications (1986)Google Scholar
  15. 15.
    Kuipers, B.J.: Quaternions and Rotation Sequences: A Primer With Applications to Orbits, Aerospace and Virtual Reality. Princeton University Press (1999)Google Scholar
  16. 16.
    Brookner, E.: Tracking and Kalman filtering made easy. John Wiley & Sons, Ltd. (1998)Google Scholar
  17. 17.
    Riviere, C.N., Reich, S.G., Thakor, N.V.: Adaptive Fourier Modeling for Quantification of Tremor. Journal of Neuroscience Methods 74, 77–87 (1997)CrossRefGoogle Scholar
  18. 18.
    Riviere, C.N., Rader, R.S., Thakor, N.V.: Adaptive Canceling of Physiological Tremor for Improved Precision in Microsurgery. IEEE Transactions on Biomedical Engineering 45(7), 839–846 (1998)CrossRefGoogle Scholar
  19. 19.
    Goswami, J.C., Chan, A.K.: Fundamentals of Wavelets: Theory, Algorithms and Applications. Wiley Series in Microwave and Optical Engineering. Wiley-Interscience (1999)Google Scholar
  20. 20.
    Matlab (computational environment) Help Documentation, scal2frq function (2008), (accessed July 13, 2010)
  21. 21.
    LIS3L06AL MEMS Inertial Sensor Data Sheet, ST Microelectronics, pp. 1–17 (2006), (accessed July 13, 2010)
  22. 22.
    XV-8100CB ultra miniature size gyro sensor data sheet. Epson Toyocom, pp. 1–2 (2010), (accessed July 13, 2010)
  23. 23.
    Halliday, D.M., Rosenberg, J.R., Amjad, A.M., et al.: A framework for the analysis of mixed time series/point process data – theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Prog. Biophys. Molec. Biol. 64(2), 237–278 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wesley Teskey
    • 1
  • Mohamed Elhabiby
    • 1
  • Naser El-Sheimy
    • 1
  1. 1.Department of Geomatics EngineeringUniversity of CalgaryCalgaryCanada

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