Effect of body size and enhanced helmet systems on risk for motorsport drivers. (19th October 2021)

• Record Type: Journal Article
• Authors: Decker, William B.; Jones, Derek A.; Devane, Karan; Hsu, Fang-Chi; Davis, Matthew L.; Patalak, John P.; Gayzik, F. Scott
• Is Part Of: Traffic injury prevention. Volume 22:Supplement 1(2021)
• Page Start: S49
• Page End: S55
• Publication Date: 2021-10-19
• Subjects: Motorsport injury risk -- enhanced helmet systems -- computational human body modeling -- GHBMC -- body size -- simplified occupant model; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2021.1977802
• Languages: English
• ISSNs: 1538-9588

Abstract

Abstract: Objective: Computational modeling has been shown to be a useful tool for simulating representative motorsport impacts and analyzing data for relative injury risk assessment. Previous studies have used computational modeling to analyze the probability of injury in specific regions of a 50th percentile male driver. However, NASCAR drivers can represent a large range in terms of size and female drivers are becoming increasingly more common in the sport. Additionally, motorsport helmets can be outfitted with external attachments, or enhanced helmet systems (EHS), whose effect is unknown relative to head and neck kinematics. The current study expands on this previous work by incorporating the F05-OS and M95-OS into the motorsport environment in order to determine correlations between metrics and factors such as PDOF, resultant ΔV occupant size, and EHS. Methods: A multi-step computational process was used to integrate the Global Human Body Models Consortium family of simplified occupant models into a motorsport environment. This family included the 5th percentile female (F05-OS), 50th percentile male (M50-OS), and 95th percentile male (M95-OS), which provide a representative range for the size and sex of drivers seen in NASCAR's racing series'. A series of 45 representative impacts, developed from real-world crash data, and set of observed on-track severe impacts were conducted with these models. These impacts were run in triplicate for three helmet configurations: bare helmet, helmet with visor, helmet with visor and camera. This resulted in 450 total simulations. A paired t -test was initially performed as an exploratory analysis to study the effect of helmet configuration on 10 head and neck injury metrics. A mixed-effects model with unstructured covariance matrix was then utilized to correlate the effect between five independent variables (resultant ΔV, body size, helmet configuration, impact quadrant, and steering wheel position) and a selection of 25 metrics. All simulations were conducted in LS-Dyna R. 9.1. Results: Risk estimates from the M50-OS with bare helmet were used as reference values to determine the effect of body size and helmet configuration. The paired t -test found significance for helmet configuration in select head-neck metrics, but the relative increase in these metrics was low and not likely to increase injury risk. The mixed-effects model analyzed statistical relationships across multiple types of variables. Within the mixed-effects model, no significance was found between helmet configuration and metrics. The greatest effect was found from resultant ΔV, body size, and impact quadrant. Conclusions: Overall, smaller drivers showed statistically significant reductions in injury metrics, while larger drivers showed statistically significant increases. Lateral impacts showed the greatest effect on neck metrics and, on average, showed decreases for head metrics related to linear acceleration and increases for head metrics related to angular velocity. HBM parametric studies such as this may provide an avenue to assist injury detection for motorsport incidents, improve triage effectiveness, and assist in the development of safety standards.