The time evolution and complex interactions of many nonlinear systems, such as in the
human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/f. The scaling exponent can be interpreted as the degree of fractal characteristic and thus as a "biomarker" of relative health and decline. This thesis presents a thorough numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index. In consideration of the constraints in applications, the significant drawbacks of proposed time domain methods are noted, and it is concluded that time-scale domain wavelet methods can provide a reasonably consistent and accurate biomarker technique for these fractal time series
