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Updated: Jun 26, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
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Published on: May 25, 2019

Online pathological tremor characterization using extended Kalman filtering.

Antônio P L Bó1, Philippe Poignet, Ferdinan Widjaja

  • 1LIRMM UMR 5506 CNRS UM2, Montpellier, France. bo@lirmm.fr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
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This study presents algorithms for real-time pathological tremor analysis using acceleration data. Extended Kalman Filters efficiently estimate Auto-Regressive and harmonic models for improved tremor characterization.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Instrumentation

Background:

  • Pathological tremors significantly impact patient quality of life.
  • Accurate and real-time characterization of tremors is crucial for diagnosis and treatment.
  • Existing methods may lack efficiency or require costly equipment.

Purpose of the Study:

  • To develop and compare algorithms for online pathological tremor characterization.
  • To evaluate the performance of Auto-Regressive (AR) and harmonic models for tremor analysis.
  • To assess the efficacy of Extended Kalman Filters (EKFs) in recursive model estimation.

Main Methods:

  • Utilized two parametric models: Auto-Regressive (AR) and harmonic models.
  • Employed Extended Kalman Filters (EKFs) for recursive estimation of model parameters.

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  • Collected experimental data using low-cost sensors for tremor acceleration measurement.

    • Main Results:

    • Demonstrated the feasibility of online tremor characterization using AR and harmonic models.
    • Compared the performance of the two models in terms of spectrogram estimation.
    • Evaluated the prediction accuracy of the estimated models.

    Conclusions:

    • The proposed algorithms provide an efficient approach for real-time pathological tremor analysis.
    • EKF-based estimation enables effective characterization of tremors from acceleration data.
    • Low-cost sensors combined with advanced algorithms offer a promising solution for tremor assessment.