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Human postural equilibrium is the result of complex control processes. Nevertheless these processes are taken for granted in our daily life, disturbance or degeneration of a single system involved in these processes leads to a variety of diseases, which pile up with age. Therefore, investigation of postural signals is the aim of many clinical and biophysical studies, in order to recognize diseases early and to improve the precision of diagnostics. In order to analyze posturographic signals we conducted a pilot study to measure body sway of nine healthy subjects during four trials with different acoustic and visual impairments, in order to detect their influence on stance. Ten time domain and five spectral domain feature extraction methods were applied on segmented raw data and classified by five different classification methods. The test errors were empirically minimized first by estimating best parameters for each feature extraction method, yielding to an optimal combination of feature extraction and classification methods. It turned out, that Burg autoregressive method of power spectral density estimation and Optimized Learning Vector Quantization was the best method combination. The classification task “no impairment” versus “visual impairment”, i.e. “eyes open” versus “eyes closed”, showed best discriminative performance indicated by mean test errors of 2.2%. The pilot study pointed out, that the established biosignal analysis system gained a high sensitivity on small postural influences.

Comparison of Time and Spectral Domain Features on Postural Signals Utilizing Neural Networks