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Effects of weight load on physiological tremor: the AR representation
1Department of Communications and Systems, University of Electro-Communications, Tokyo, Japan.
Applied Human Science : Journal of Physiological Anthropology
|January 1, 1995
Summary
Autoregressive (AR) modeling effectively processes human finger tremor signals. Increased weight loads enhance tremor amplitude but decrease AR model parameters, offering insights into physiological tremor dynamics.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Human Physiology
Background:
- Physiological tremor is a common human motor function.
- Understanding tremor dynamics is crucial for diagnosing neurological disorders.
- Autoregressive (AR) modeling offers a potential method for analyzing tremor signals.
Purpose of the Study:
- To investigate the efficacy of the autoregressive (AR) method for processing human finger tremor signals.
- To determine the optimal order of the AR model for finger tremor analysis.
- To examine the effects of varying weight loads on finger tremor characteristics using AR parameters.
Main Methods:
- Applied Akaike's criterion to determine the optimal AR model order (15th order).
- Utilized Burg's algorithm to estimate AR spectrum and parameters from finger tremor recordings.
- Compared AR method results with Fast Fourier Transform (FFT) and autocorrelation function analyses.
Main Results:
- The amplitude of the AR spectrum significantly increased with added weight loads.
- The first prediction coefficient (a1) and first reflection coefficient (rho 1) decreased as weight loads increased.
- AR parameters (a1, rho 1) showed a relationship with the system's resonant modes.
Conclusions:
- The autoregressive (AR) method is a suitable technique for analyzing physiological finger tremor.
- Weight loading alters finger tremor dynamics, reflected in changes in AR spectrum amplitude and parameters.
- AR parameters provide a physically interpretable measure of tremor system characteristics.