**6. Conclusions**

The present study proposes a novel methodology for classifying stance vs. swing and predicting gait-event timing, based on neural-network classification of signals acquired by a single knee electrogoniometer during walking. The clinically useful contribution of the study consists in assessing gait events from only sagittal knee-angle signals, avoiding the installation of additional sensors on the human body and promoting the reduction of the sensor-system complexity. Additional goal is to evaluate if the introduction of knee-angle data from the electrogoniometer could improve the classification performance of state-of-the-art sEMG-based methods, in order to provide a sensor-fusion approach useful to face more complex task or to pursue higher classification/prediction performances. The comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of classification performances. However, the choice of the suitable approach should not only be driven by network performance but also (mainly) by patient comfort and clinical needs.

**Author Contributions:** Conceptualization, F.D.N., C.M., S.F., and A.C.; Methodology, F.D.N. and C.M.; Software, C.M.; Investigation, C.M. and F.D.N.; Validation, C.M.; Resources, S.F.; Data curation, F.D.N. and C.M.; Writing—original draft preparation, F.D.N.; Writing—review and editing, A.C. and S.F.; Visualization, F.D.N. and C.M.; Supervision, A.C. and S.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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