Structural and functional brain changes have been observed with the accumulation of head impacts that do not cause typical concussion symptoms nor result in a diagnosed concussion, also known as subconcussive impacts (Mainwaring et al., 2018, Tarnutzer et al., 2017). The number, timing, and magnitude of repetitive subconcussive impacts have also been reported to influence the risk of sustaining a concussion (Elliott et al., 2015, Stemper et al., 2019). To understand the short- and long-term risk of brain trauma, we must first quantify the exposure to repetitive head acceleration events (HAE), including both direct head contact as well as indirect head loading due to body contact. Human sport participation provides a unique opportunity to collect HAE data, and wearable inertial measurement unit (IMU) sensors have been deployed in athletes to measure head linear and angular kinematics.

Since the reliability of injury risk investigation depends on sensor accuracy, it is imperative to quantify sensor errors (Elliott et al., 2015). Past studies have identified skull coupling as a key factor in sensor kinematics accuracy (Gabler et al., 2022, Wu et al., 2016), where poorly coupled sensors may exhibit substantial noise in estimating skull kinematics during impact (Siegmund et al., 2014, Wu et al., 2016). Mouthguard-based sensors, or instrumented mouthguard (iMG) sensors, can be worn by athletes during play and directly couple to the upper dentition (Wu et al., 2016). In laboratory validation with a clenched jaw, iMG measurements were highly correlated with reference sensors in peak linear acceleration (r 2 = 0.96), peak angular acceleration (r 2 = 0.89), and peak angular velocity (r 2 = 0.98) (Camarillo et al., 2013). Additional validation of various mouthguard sensors has demonstrated comparable performance when the sensor is worn on a dummy head dentition under ideal laboratory conditions (Kieffer et al., 2020, Liu et al., 2020, Siegmund et al., 2014, Jones et al., 2022). However, it is unclear if on-field mouthguard skull coupling is sufficient to provide similar kinematics accuracy.

Proximity sensors within iMGs can measure mouthguard coupling to teeth and have been validated in lab settings (Wu et al., 2014). These sensors emit infrared light and detect the amount of light reflected off the teeth. As such, sensor readings exhibit two distinct distributions when the mouthguard is coupled and decoupled (Wu et al., 2014). Differentiating these distributions through a thresholding method, proximity sensors have been shown to reliably filter and reject events when the mouthguard is decoupled from teeth in laboratory settings (Wu et al., 2014).

While in-lab iMG error has been well-documented, on-field kinematics accuracy has not been validated. On-field kinematics noise has been speculated to be associated with poor mouthguard coupling (Kieffer et al., 2020), yet difficult to quantitatively assess. It is unclear if laboratory-evaluated sensor accuracy will be consistent with field performance, especially considering many practical factors that may lead to sensor coupling error (e.g., loosely fitting mouthguards). In the current paper, we aim to characterize on-field iMG coupling through the validated proximity sensing method and investigate coupling effects on kinematics signal characteristics. We will investigate this in women’s rugby and men’s hockey to understand potential sensor coupling differences across athlete populations. We hypothesize that HAE recordings coming from poorly coupled iMGs will have different signal characteristics than HAE recordings coming from well-coupled iMGs. We should note that while prior literature has commonly referred to the kinematics recordings from IMU sensors as impacts, we broadly define these as acceleration events (AEs) to include any event that may trigger recording on the IMU sensor. These AEs undergo a video verification process to identify true/false positive HAEs. Our study is a first step towards evaluating potential on-field sensor kinematics error and may lead to further data cleaning techniques to ensure the accuracy of HAE biomechanics for injury risk investigations.