Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models

The objectives of this study were to develop a method for modeling obese pedestrians and to investigate effects of obesity on pedestrian impact responses and injury outcomes.

The GHBMC (Global Human Body Model Consortium) 50th percentile male pedestrian model was morphed into geometries with 4 body mass index (BMI) levels (25/30/35/40 kg/m²) predicted by statistical body shape models. Each of the 4 morphed models was further morphed from a standing posture into 2 other gaits (toe-off and mid-swing), which resulted in a total of 12 (4 BMIs × 3 postures) models. Each model was used to simulate vehicle-to-pedestrian impact under 2 impact velocities. Pedestrian kinematics and injury measures were analyzed focusing on lower extremities. Statistical analyses were performed to examine significance of obesity on concerned injury measures.

Peak values of the bending moment at tibia, force at medial collateral ligament (MCL), bending angle at knee joint, and contact force between vehicle and pedestrian increased significantly (P < .05) with increased BMI. By analyzing kinematics of the lower extremity, the overall vehicle-to-pedestrian impact was divided into 2 phases: “initial contact” and “tibia rebound.” For obese pedestrians, the added mass caused a higher tibia bending moment in the initial contact phase, and the greater moment of inertia led to greater bending angle and MCL force in the tibia rebound phase. Statistical analyses also revealed that pre-impact posture and impact velocity had significant effects on all injury measures.

Obesity could significantly increase the risk of pedestrian lower extremity injuries due to the inertial effect from the added mass. Pre-impact posture and impact velocity also significantly affect pedestrian injury measures. Future vehicle designs for pedestrian protection should consider populations with obesity. This study demonstrated the feasibility of using parametric human modeling to account for population diversity in injury prediction.