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Original articles  KINANTHROPOMETRY

The Journal of Sports Medicine and Physical Fitness 2005 June;45(2):229-37

language: English

Examination of quantitative and fractal analysis of sway characteristics of the center of foot pressure ovement during a static upright posture. Analysus based on alchool intake

Noda M. 1, Demura S. 2, Kitabayashi T. 3, Imaoka K. 4

1 Jin-ai University, Fukui, Japan
2 Department of Physical Education Kanazawa University, Ishikawa, Japan
3 Kanazawa University Graduate School of Natural Science and Technology, Ishikawa, Japan
4 Anima Corporation, Tokyo, Japan


Aim. This study aimed to examine the sway characteristics of center of foot pressure (CFP) movement during a static upright posture under the influence of alcohol by using quantitative and fractal analysis.
Methods. Eleven healthy young people participated. They drank in a range of 0.54-1.83 ml/kg of alcohol, standardized by body mass, within 10 min. Blood pressure, heart rate, 2 types of nervous function tests and the CFP movement were measured before and after the alcohol intake. Thirty-six parameters of the CFP movement were used for quantitative analysis. Diffusion coefficient, scaling exponent and critical point coordinates were used in fractal analysis.
Results. Quantitative analysis confirmed that the CFP parameters such as distance, velocity and area were significantly changed, although the parameters evaluating cyclical characteristics and center average were not. Fractal analysis confirmed that critical point coordinates existed at time-lag=1.0 before and after the alcohol intake, and the short-term region (ST) and long-term region (LT) could be separated. A diffusion coefficient in the ST was larger than that in the LT, and the scaling exponent was over 0.5 in the ST and under 0.5 in the LT.
Conclusion. Influences of alcohol intake were found in amount of body-sway, but not in the displacement or periodicity of the CFP parameters. Fractal analysis confirmed that CFP movement was dependent on time-series, and its characteristics changed before and after the time-series critical point and was unsteady.

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