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Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ( strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents (DFA) and (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning and . Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true . The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.

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This study investigates the choice of posturographic parameter sets with respect to the influence of different sampling durations (30 s, 60 s, 300 s). Center of pressure (COP) data are derived from 16 healthy subjects standing quietly on a force plate. They were advised to focus on the postural control process (i.e. internal focus of attention). 33 common linear and 10 nonlinear parameters are calculated and grouped into five classes. Component structure in each group is obtained via exploratory factor analysis. We demonstrate that COP evaluation-irrespective of sampling duration-necessitates a set of diverse parameters to explain more variance of the data. Further more, parameter sets are uniformly invariant towards sampling durations and display a consistent factor loading pattern. These findings pose a structure for COP parametrization. Hence, specific recommendations are preserved in order to avoid redundancy or misleading basis for inter-study comparisons. The choice of 11 parameters from the groups is recommended as a framework for future research in posturography.

Public transportation by bus is an essential part of mobility. Braking and starting, e.g., approaching a bus stop, are documented as the main reason for non-collision incidents. These situations are evoked by the acceleration forces leading to perturbations of the passenger's base of support. In laboratory studies perturbations are applied to getting insight into the postural control system and neuromuscular responses. However, bus perturbations diverge from laboratory ones with respect to duration, maximum and shape, and it was shown recently that these characteristics influence the postural response. Thus, results from posturographic studies cannot be generalised and transferred to bus perturbations. In this study, acceleration (ACC) and deceleration (DEC) signals of real traffic situations were examined. A mathematical approach is proposed in order to identify characteristics of these signals and to quantify their similarity and complexity. Typical characteristics (duration, maximum, and shape) of real-world driving manoeuvres concerning start and stop situations could be identified. A mean duration of 13.6 s for ACC and 9.8 s for DEC signals was found which is clearly longer than laboratory perturbations. ACC and DEC signals are more complex than the used signals for platform displacements in the laboratory. The proposed method enables the reconstruction of bus ACC and DEC signals. The data can be used as input for studies on postural control with high ecological validity.

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There exists a great variety of posturographic parameters which complicates the evaluation of center of pressure (COP) data. Hence, recommendations were given to use a set of complementary parameters to explain most of the variance. However, it is unknown whether a dual task paradigm leads to different parametrization sets. On account of this problem an exploratory factor analysis approach was conducted in a dual task experiment. 16 healthy subjects stood on a force plate performing a posture-cognition dual task (DT, focus of attention on a secondary task) with respect to different sampling durations. The subjects were not aware of being measured in contrast to a baseline task condition (BT, internal focus of attention) in the previously published part I. In compareson to BT a different factor loading pattern appears. In addition, factor loadings are strongly affected by different sampling durations. DT reveals a change of factor loading structure with longer sampling durations compared to BT. Specific recommendations concerning a framework of posturographic parametrization are given.