Head impact kinematics and injury risks during E-scooter collisions against a curb

E-scooters as a mode of transportation is rapidly growing in popularity. This study evaluates head impact conditions and injury risk associated with E-scooter crashes. A multibody model of E-scooter falls induced by wheel-curb collision was built and compared with an experimental E-scooter crash test. A total of 162 crash scenarios were simulated to assess the effect of fall conditions (E-scooter initial speed and inclination, obstacle orientation, and user size) on the head impact kinematics. The forehead hit the ground first in 44% of simulations. The average tangential and normal impact speeds were 3.5 m/s and 4.8 m/s respectively. Nearly 100% of simulations identified a risk of concussion (linear acceleration peak >82 g and rotational acceleration peak >6383 rad/s2) and 90% of simulations suggested a risk of severe head injuries (HIC>700). This work provides preliminary data useful for the assessment and design of protective gears.

Reconstruction of a real-world car-to-pedestrian collision using geomatics techniques and numerical simulations

The aim of this study is to provide an improved method for traffic accident reconstruction based on geomatics techniques and numerical simulations. A combination of various techniques was used. First, an unmanned aerial vehicle (UAV), laser scanner and structured-light scanner were used to obtain information on the accident scene, vehicle and victim. The collected traces provided detailed initial impact conditions for subsequent numerical simulations. Then, multi-body system (MBS) simulations were conducted to reconstruct the kinematics of the car-to-pedestrian collision. Finally, a finite element (FE) simulation using the THUMS model was performed to predict injuries. A real-life vehicle-pedestrian collision was used to verify the feasibility and effectiveness of this method. The reconstruction result revealed that the kinematic and injury predictions of the numerical simulations effectively conformed to the surveillance video and investigation of the actual accident. UAV photogrammetry was demonstrated to be more efficient in accident data collection than hand sketch and measurement, and 3D laser scanning enabled an easier and more accurate modeling process of vehicle. The present study shows the feasibility of this method for use in traffic accident reconstruction.

Characteristic analysis and reconstruction method of falls from windows

Fall accidents from windows usually start indoors, so it is impossible to secure witnesses or video clips of the initial fall situation, and there are difficulties such as a complicated fall process, but related studies are rare. In this study, the types of window fall accidents are classified, and behavior characteristics are analyzed through multibody simulation analysis based on the initial fall postures and speed values from the experimental results. And, in case studies of carefully selected real events, a method for specifying the type of window fall accident is presented along with the reconstruction simulation results using a fuzzy tool for type estimation. It is believed that the results of the characteristic analysis for each type of window fall accidents and the reconstruction method proposed in this paper will be useful for related forensic investigations.