Technical Union for the Automobile Motorcycle and Cycle Industries

Powered two-wheeler (PTW) riders are most frequently injured seriously to the thorax. Some thorax protective devices have been developed in order to reduce thorax injuries. Nevertheless, actual tests and standard lack real-world accident representativeness. The aim of the study was to determine realistic trunk impact conditions for accidents likely to result in thorax injury. The study is divided into two phases: a literature review to classify accident scenarios based on rider kinematics, and numerical simulations to analyze the riders impact conditions. Three accident scenarios have been found through a literature review and their main configurations have been numerically investigated via multi-body modeling. The 912 performed simulations were used to explore body contacts with counterparts over time, relative velocities and relative body orientation at impact. The results have highlighted the necessity of a conservative activation time delay for triggering protective devices. They have also led to propose three realistic impact conditions that can be used in future test protocols.

Abstract Outlier detection is crucial in various sectors such as finance, insurance, medicine, and IT security. Its application helps to identify suspicious behaviors and enhance the robustness of statistical models. However, a common challenge arises when dealing with data that includes both numerical and categorical attributes, as most traditional outlier detection methods are applicable only to numerical data. To overcome this limitation, this study proposes to apply the Factor Analysis on Mixed Data (FAMD) method to transform both types of attributes into numerical Principal Components (PCs). Traditional outlier detection methods are then applied to these PCs. The proposed method is evaluated on classic datasets from the supervised classification literature and two simulated data contaminated by different types of outliers : (a) global outliers which significantly deviate from most data points, (b) local outliers which are not necessarily extreme values but are considered abnormal within their specific context or neighborhood, (c) rare outliers which have unexpected categories compared to the typical data distribution, and (d) mixed outliers which can be both global and rare, or local and rare.