*2.4. Statistical Analysis*

The outcome set included both the regularity index RI and the period index PI for all subjects, all tasks, and all sensor locations. As data were non-normally distributed, we used non-parametric tests; the Friedman test was used for non-parametric analysis of variance and the Wilcoxon test was used for post hoc and planned comparisons. The significance values (significance set to *p* ≤ 0.05) of all the tests inside the same analysis were corrected according to Holm–Bonferroni method. The experiment was designed to study the effect of three factors: a sensor factor, a speed factor, and a strategy (walking vs. running) factor. No analysis of interaction between factors was planned for two reasons: the sample data size was relatively small to allow efficaciously looking for interactions, and our interests were focused only on the main effects of the three factors.

As to the period index PI, a one-way non-parametric analysis of variance was performed to assess the across-sensor method robustness.

As to the regularity index RI, the experimental design allowed for assessing several factors. The analysis for the sensor factor was performed using one-way non-parametric analysis of variance. The speed factor was analyzed using the following planned comparison tests: W1 vs. W2, W2 vs. W3, and R3 vs. R4. Other comparisons were considered not relevant (W1 vs. W3) or inadequate (for example, W1 vs. R4) to explore the speed factor. The strategy factor (locomotion by walking vs. locomotion by running) was analyzed using a planned comparison of W3 vs. R3. Other comparisons (e.g., W2 vs. R4) report the effect of the strategy factor mixed with the effect of other factors (speed) and, therefore, were not included in the planned analysis.

Finally, in order to compare outcomes from the present module-based method with outcomes from a single-component approach in the regularity assessment, the algorithm was applied also to the single components of measured accelerations. Starting from RI\_X, RI\_Y, and RI\_Z (regularity indexes of X, Y, and Z components, respectively), the minimum (worst index value) and the maximum (best index value) of the three single-component regularity indexes were identified for each subject/task/sensor, and multiple comparisons were performed between the module-based regularity index and the best and worst single-component ones.
