A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications
Figure 15
Wheelchair acceleration signal recorded with three different devices at 100 Hz against time (a); the two arrows refer to the time data with the lowest and highest values used for determining the amplitude spectrum of Figure 14; (b): unoptimised fractal dimension of the 100 Hz signal , running average with a window width of 251 data points over 2.5 s); (c): optimised fractal dimension of the 100 Hz signal; (d): fractal dimension of the original 100 Hz signal at ; (e): optimised fractal dimension of the signal reduced to 50 Hz (running average with a window width of 125 data points over 2.5 s) calculated for different amplitude multipliers (cf. Figure 14 and Table 1).
