Optimized lower leg injury probability curves from postmortem human subject tests under axial impacts

Area of Science:

• Biomechanics
• Injury Biomechanics
• Human Tolerance

Background:

• Understanding human lower leg tolerance to axial loading is crucial for automotive safety.
• Previous models lacked precise injury probability curves for lower leg fractures.

Purpose of the Study:

• To derive optimum injury probability curves for lower leg tolerance using parametric survival analysis.
• To establish reliable risk curves based on peak force and age for crashworthiness applications.

Main Methods:

• Reanalyzed lower leg postmortem human subject (PMHS) axial loading data.
• Employed parametric survival analysis (Weibull, log-normal, log-logistic distributions) with peak force as the explanatory variable and age as a covariate.
• Identified and excluded overly influential samples and assessed distribution quality at discrete probability levels.

Main Results:

• The Weibull distribution provided the best fit for injury probability curves.
• Excluding influential tests yielded the tightest confidence intervals.
• Provided specific peak force values (kN) for lower leg fracture risk at different ages (25, 45, 65 years) and risk levels (5%, 25%, 50%).

Conclusions:

• Developed validated axial loading-induced lower leg injury risk curves using survival analysis.
• These curves, accepted by international automotive communities, can improve future crashworthiness applications.
• The methodology accounts for censoring, influential samples, and quality assessment for robust injury prediction.