## 4 Discussion  
This study aimed to provide a method to identify loss of control events in braking maneuvers on a straight line before they become irreversibly unstable to support motorcycle crash reduction through active systems developments and training initiatives. Using experimental data, we analyzed hard braking maneuvers in an emergency scenario to identify loss of control conditions during the braking performance with riders from low to high braking skill levels. The results provide a measure of the motorcycle dynamics variability in emergency braking maneuvers associated with riders of different skill levels. Three predictive models with 100%, 99.8% and 98.8% of area under the ROC curve (AUC) were developed using the kinematics of the vehicle as an input. The effectiveness of the supervised models for real-time applications, adapted for times series of data as input, was satisfactory in all two of the tested models. The study also revealed that the thresholds based on typical parameters characterizing the kinematics of the vehicle (maximum or RMS) to predict loss of control are dependent of the  
Figure 11: (**a**) full control braking trial with 0% of LC probability for Model-1 and Model-3; (**b**) loss of control detected only by Model-3 (LC probability for Model-1 = 0%); (**c**) loss of control detected earlier by Model-3; (**d**) loss of control detected earlier by Model-1. rider skill levels. Expert riders may maintain stability (i.e., handle the controllability) under dynamic conditions that would normally bring less skilled riders to _loss of control_ or falling events. The results of our study are comparable with the work in Reference [6] and Reference [13], which defined threshold for the lock front-wheel duration and the maximum of the gyro norm (Gyro Norm Max) respectively as predictors of falling events. [PERSON] et al. [6] determined that after 0.4 seconds of front wheel locked, the instability conditions lead to a fall. Our tests were performed with standard brakes (no ABS), and in many trials during the hard braking, the tires started to lock. We found that for less skilled riders, front wheel locking longer than 0.1s is linked to some kind of loss of control. Yet the two expert skilled riders in three trials were able to keep stability with front wheel lock events lasting more than 0.4s. [PERSON] et al. [13] in their test performing falls with a stunt defined a threshold of Gyro Norm Max of 2 rad/s to identify falling events. Our study, focused on _loss of control_ events not necessarily leading to falls, set the threshold for the best AUC performance to a lower level (0.77 rad/s). However, we also found that in one case, expert riders avoided the falling (not the loss of control) with an absolute gyro rate slightly higher than those 2 rad/s. Our results, presenting Gyro-Z (yaw rate) as one of the predictors of loss of control, are consistent with [10], which found correlation between low-side fall and high yaw rate. However, our study revealed that the peak-to-RMS ratio of roll acceleration is the most robust parameter for identifying loss of control for braking maneuvers performed by riders of every skill levels. Results from previous studies aiming to detect falling events after loss detection were focused on passive safety applications. The present study, which proposed predictive models to detect _loss of control_ events before they irreversibly lead to a fall, has applications for active safety technologies. Additionally, our findings indicate that previous falling detection algorithms (typically linked to activation of passive safety systems, e.g., airbags) may produce false positive cases for highly skilled riders who can handle the vehicle in extreme conditions. Some limitations have to be considered. Predictive models defined based on a small sample of _loss of control_ cases have the risk of overfitting.