## 4 Discussion
The most important predictor (peak-to-RMS ratio of roll acceleration) is computed with the differential of a collected measure (Gyro-X), so the values may be sensitive to noise and the filter methods applied to reduce it. Additionally, the high accuracy achieved by the proposed models applies to braking events in specific conditions on a straight line and velocities of around 50 km/h using a 300 cc scooter. To reduce overfitting, we adopted the following measures: we developed three different models with low complexity (logistic regression models); we reduced the number of predictors to a maximum of three; we selected our data in order to consider _loss of control_ cases with riders of different skills and finally, when required, using regularization methods to make decision boundaries smoother to reduce coefficient variability. Despite this, the 100% accuracy obtained with Model-3 is expected to decrease for a broad sample of cases at the limit of loss of control (close to the decision boundaries of the model). Further research is warranted to confirm the accuracy of the proposed models, using these models on loss of control scenarios due to braking events that are different from those analyzed in this experiment such as naturalistic near-crash events or the more common straight line hard braking without an opponent vehicle. It would also be important to assess the effectiveness of the proposed approach for braking maneuvers under a broader set of conditions. The study presented provides valuable information on the stability capabilities of the riders under a braking maneuver close to emergency scenarios and help to understand the process and the motion conditions that lead rider to start losing control. The loss of control predictive models developed may provide support to: (a) active safety systems developments; and (b) training actions aiming to improve rider's skills with objective indicators of the controllability of the braking maneuver. The new development of active safety technologies are rider-in-the-loop-systems whose efficiency depends on understanding the response of the riders. The predictive models and kinematic thresholds identified in this work can be used in cases of emergency braking to improve the triggering algorithms used by active stability control systems (e.g., cable steering systems [23] or gyro-stabilization systems [24]). Furthermore, the models can also be applied for acceptance assessment of new advanced riding assistance systems such as motorcycle autonomous emergency braking system [25] by classifying the riding controllability/instability during the activation of these systems and providing information about the riders' performance capabilities. Regarding the actions to improve the riders' braking skills, our predictive models may be a helpful tool for rider instructors to understand and assess the trainee performance limits using indicators. This may support training programs for assigning the appropriate level of demand for improvement without exceeding the safety limits. The findings of this study contribute to a better understanding of the riding responses for the development of future countermeasures to reduce motorcycle crashes. \"conceptualization, P.H.-L., G.S., N.B. and M.P. ; methodology, P.H.-L., G.S., N.B. and M.P. ; software, P.H.-L.; validation, P.H.-L., G.S., N.B. and M.P. ; formal analysis, P.H.-L.; investigation, P.H.-L.; resources, M.P., P.H.-L. and G.S. ; data curation, P.H.-L.; writing--original draft preparation, P.H.-L.; writing--review and editing, P.H.-L.; G.S. and N.B. ; visualization, P.H.-L.; supervision, N.B. and G.S. ; project administration, M.P., G.S. and P.H.-L.; funding acquisition, M.P. and G.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the 7 th Framework Program of the European Commission within the Marie Curie Actions (MOTOrcycle Rider Integrated SafeTy, grant number 608092).