**5. Conclusions**

The proposed method was proven to be feasible and reliable, to be able to quantify the regularity of movement of different anatomical parts, and to be able to track modulation of regularity determined by locomotor strategy and speed. The novel methodological approach of considering the acceleration module has the advantage over single-component methods [17,29] to be unaffected by sensor misalignment. The data from a group of healthy individuals may foster the collection of a larger dataset and provide reference in studies concerning pathological conditions.

Already planned future applications of the method include (a) a real-time application in providing biofeedback to the patient during periodic movements to help them in keeping a regular motor pattern (such as gait, but also upper limb movements), thus supporting motor learning rehabilitation protocols [46,47]; (b) studies on the modulation of regularity during prolonged performances, potentially induced by fatigue or by a voluntary change in motor strategy [48]; (c) studies on non

functional pseudo-periodic movements, such as tremor or dyskinesia, by means of regularity analysis of long-term actigraphic recordings [49].

**Author Contributions:** Conceptualization, M.R. and M.F.; methodology, M.R.; software, M.R. and G.M.S.; validation, M.R. and G.M.S.; formal analysis, M.R., G.M.S., and M.F.; investigation, G.M.S.; resources, G.M.S.; data curation, M.R. and G.M.S.; writing—original draft preparation, M.R.; writing—review and editing, M.R., G.M.S., and M.F.; visualization, M.R. and G.M.S.; supervision, M.F.; project administration, M.R. and M.F.; funding acquisition, M.R. and M.F.

**Funding:** This research was funded by the Italian Ministry of Health, Ricerca Corrente.

**Acknowledgments:** Alberto Marzegan provided his contribution in the acquisition of experimental data and in software development.

**Conflicts of Interest:** The authors declare no conflicts of interest.
