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The autoregressive time series modelling of stabilograms
Acta Biologica Et Medica Germanica
|January 1, 1978
Summary
Autoregressive modeling effectively analyzes body sway control using power spectral density of stabilograms. This method provides reliable spectral estimates for assessing postural stability in young men.
Area of Science:
- Biomechanics
- Systems Neuroscience
- Signal Processing
Background:
- Postural stability is crucial for daily activities.
- Vestibular function plays a key role in maintaining balance.
- Understanding body sway dynamics can reveal insights into postural control mechanisms.
Purpose of the Study:
- To apply parametric time series models to analyze stabilograms.
- To investigate the power spectral density of frontal and sagittal stabilograms.
- To characterize the human body's postural control system.
Main Methods:
- Subjects stood on a force platform under three conditions.
- Stabilograms were recorded for 2 minutes and processed using a TPAi computer.
- Digital filtering, correlation functions, and power spectral density were estimated.
- Linear autoregressive models (up to order 30) were fitted to the data.
Main Results:
- The goodness of fit for autoregressive models varied significantly across standing conditions, planes, and subjects.
- Frontal stabilograms required higher model orders but had lower residual variances than sagittal stabilograms.
- Autoregressive modeling proved suitable for spectral estimation.
Conclusions:
- Parametric time series modeling, specifically autoregressive modeling, is a robust method for analyzing stabilograms.
- This approach allows for reliable spectral estimation and characterization of the body sway control system.
- Findings highlight differences in control strategies between frontal and sagittal planes.