(C-95) Characterizing Head Kinematics in Rear Impacts in Stock Car Racing

Head injuries are not uncommon in motorsports due to high speeds and risk of collisions. In recent years, numerous steps have been taken to reduce the risk of head injury. One of these steps includes the head and neck support (HANS) device, which was mandated by the National Association of Stock Car Auto Racing (NASCAR) for all drivers in 2001 [1]. Prior to the HANS device, a total of 204 drivers died at motorsports events between 1990 and 2002 [2]. The purpose of the HANS device is to limit neck loads and head kinematics through tethers attached to the driver’s helmet [3, 4]. But, this device is not designed to reduce the impact magnitude of rear crashes [5]. During rear crashes, the helmet interacts with the head surround, which is designed to mitigate crash forces and control head motion but must be designed to perform well both in low speed and high-speed crashes. A study conducted by Somers et al. selected 274 impacts from the 2002-2008 NASCAR race seasons and reported that head injury occurred in 27 of these impacts, all of which were mild concussions [6]. Although the probability of severe head injuries has significantly decreased in recent years, there is still a gap in the knowledge surrounding concussions and head impacts in motorsports. The purpose of this study was to characterize head kinematics of drivers involved in rear impacts during stock car racing as well as to estimate the compression characteristics of the head surround during crashes.

NASCAR Cup Series drivers were recruited to participate in this study. Drivers were instrumented with custom-fit mouthpiece sensors containing a tri-axial accelerometer and angular rate sensor, both of which recorded at 1600 Hz with a 4g threshold. Rear impacts were characterized using peak linear acceleration in the x (anterior-posterior) direction and angular velocity about the y (medial-lateral) axis. Impacts collected by the sensor were verified using in-car and broadcast video. The identified rear impacts included car-car and car-wall crashes, oblique-rear crashes, and rear impacts experienced at restarts. Along with the mouthpiece, each chassis was also equipped with an incident data recorder (IDR) system that recorded linear acceleration at 2000 Hz.

      To segment impacts and estimate head motion relative to head surround motion, the mouthpiece data was processed according to the process outlined below. First, the point of maximum contact between the head and head surround was defined as the point of maximum linear acceleration in the x direction. Minimum values to the right and left were then located to define the initial contact of the head with the surround and the instant the head loses contact with the surround. The resulting displacement curves were then fit with a fourth order polynomial, differentiated, and subtracted from the velocity data. These steps essentially estimated the motion of the head relative to the head surround / chassis. Using this final curve, incoming/exiting head velocities were estimated as well as compression of the head surround.

      22 NASCAR drivers were scanned for mouthpieces. There were 6 instrumented drivers in 2022 and 21 in 2023. A preliminary identification of rear impacts based on the governing acceleration component collected with the mouthpieces during races throughout 2022 and 2023 identified 11 rear impacts. These 11 impacts were recorded and analyzed from eight drivers across ten different races. The mean (range) of peak linear acceleration (PLA), peak angular velocity (PAV), and peak angular acceleration (PAA) recorded by the mouthpieces can be seen in Table 1. Other metrics, such as incoming/exiting velocities as well as foam compressions estimated using the polynomial fit data can be seen in Table 2. The compression values calculated do not represent exclusively head surround foam compression - these values also include compression of the helmet foam liner, skin, hair, etc. Future studies may estimate relative contribution of each of these to overall compression.

Of the 11 rear impacts recorded with the mouthpiece, we were able to obtain chassis data from seven. Further, an additional rear impact was recorded by the IDR in the chassis data that was not registered by the mouthpiece. This resulted in eight rear impacts with chassis data, 87.5% of which also had mouthpiece data. The mean (range) of chassis acceleration in the x direction was 7.85 (2.81, 11.69) g (Table 1).

            The aim of this study was to characterize head kinematics of drivers involved in rear impacts during stock car races. This study provides insight in rear impacts by reporting head kinematics and estimating head surround compression. The impact magnitudes described in these results occur due to interaction of the helmet with the head surround. Future work will explore new ways to process the mouthpiece data to account for chassis / head surround motion as well as conduct finite element analysis of the head response to rear impacts using the chassis acceleration data in an LS-Dyna model.

This project was supported in part by the NSF REU Site (Award #1950281) in the Department of Biomedical Engineering at Wake Forest University School of Medicine as well as the National Association of Stock Car Auto Racing (NASCAR).