## 1 Introduction  
Braking is the most frequent evasive maneuver and one of the most complicated to perform by motorcycle riders because of the inherent instability and the complex driving dynamics of motorcycles. During emergency braking, while considering the variations occurring in load distribution between the two wheels [1] and the variations in the tire-road adherence conditions, riders require simultaneous optimal management of the front and rear brakes to achieve maximum deceleration. This complexity makes riders frequently lose control and fall when performing braking to avoid a collision [2]. In order to enhance the rider's safety maintaining the stability of the vehicle and to avoid loss of control events leading to fall during emergency braking, two different approaches can be pursued: designing active systems that support the braking maneuver under stable conditions, and improving the braking skills of the riders. In both cases, it is necessary to understand the process and the motion conditions that lead riders to start losing control. Previous research analyzed several factors leading to more efficient braking maneuvers, namely the optimum braking from the theoretical perspective with multi-body models and simulations [1; 3; 4], the braking patterns during different maneuvers [5] and the potential improvements in designing vehicle stability control systems [6; 7]. Concerning autonomous braking applications, the stability of riders of powered-two-wheelers (PTWs) under moderate automatic deceleration was assessed with motion captured and EMG data using a motorcycle mock-up mounted on a sled [8] and with qualitative observations in a field trial [9]. Few studies using experimental measures in the field have developed fall detection models. Such studies used the data from several race riders running in a race circuit [10; 11] or of a stuntman reproducing fall and near fall scenarios [12; 13]. The study focused on race competitions [10] defined a method based on the accelerations and angular rates of both the rider and the motorcycle to represent a falling 'risk index' (for which no equations were defined) during the execution of racing maneuvers. The work with a stuntman rider defined a fall detection algorithm based on a certain threshold [13] and multivariate cumulative sum control charts [12] for the values of the norm of the tri-axial gyroscopes and the norm of the tri-axial accelerometers. The few existing fall detection models have focused on loss control events with unavoidable falls to develop passive safety systems such as airbags. However, a momentary loss of control does not always lead to a fall event if the unstable conditions are not maintained or are corrected in time. Loss of control may be limited to the occurrence of instability and of unsafe behaviors and not necessarily to total loss of control [14]. Concerning braking, safe braking may be defined as a maneuver that allows the vehicle to stop in an upright position, with small oscillations, where the driver maintains control of the handlebars with smooth movements [15]. In the literature, we could find only one single example of a definition of algorithms which can be to detect critical braking events (defined as potential loss of control cases with locked wheel) [15]. The parameters of those algorithms were optimized by using virtual data reproducing emergency braking maneuvers and calibrated by six 'near missed' accident cases of naturalistic riding data. Given the importance of this topic and the ambition to increase riders' safety during hard braking, more research to understand the riders' response under loss of control conditions is necessary. To contribute to this, in this study we proposed the development of predictive models of the riders' responses in safety-critical situations, validated with consistent experimental data and including riders who cover all riding skill levels.