Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices

Detecting and Salvaging Head Impacts with Decoupling Artifacts from Instrumented Mouthguards

In response to growing evidence that repetitive head impact exposure and concussions can lead to long-term health consequences, many research studies are attempting to quantify the frequency and severity of head impacts incurred in various sports and occupations. The most popular apparatus for doing so is the instrumented mouthguard (iMG). While these devices hold greater promise of head kinematic accuracy than their helmet-mounted predecessors, data artifacts related to iMG decoupling still plague results. We recreated iMG decoupling artifacts in a laboratory test series using an iMG fit to a dentition mounted in a NOCSAE headform. With these data, we identified time, frequency, and time-frequency features of decoupled head impacts that we used in a machine learning classification algorithm to predict decoupling in six-degree-of-freedom iMG signals. We compared our machine learning algorithm predictions on the laboratory series and 80 video-verified field head acceleration events to several other proprietary and published methods for predicting iMG decoupling. We also present a salvaging method to remove decoupling artifacts from signals and reduce peak resultant error when decoupling is detected. Future researchers should expand these methods using on-field data to further refine and enable prediction of iMG decoupling during live volunteer use. Combining the presented machine learning model and salvaging technique with other published methods, such as infrared proximity sensing, advanced triggering thresholds, and video review, may enable researchers to identify and salvage data with decoupling artifacts that previously would have had to be discarded.

Methods for Capturing and Quantifying Contact Events in Collision Sports

Technological advancements have led to widespread use of wearable devices that capture external performance metrics in team sports. Tracking systems including global positioning system (GPS) technology with inbuilt microelectromechanical systems (MEMS), instrumented mouthguards (iMGs), and video analysis provide valuable insights into the contact demands of collision sports. In collision sports, successfully "winning the contact" is positively associated with better individual and team performance, but it also comes with a high risk of injury, posing a concern for player welfare. Understanding the frequency and intensity of these contact events is important in order for coaches and practitioners to adequately prepare players for competition and can simultaneously reduce the burden on athletes. Different methods have been developed for detecting contact events, although limitations of the current methods include validity and reliability issues, varying thresholds, algorithm inconsistencies, and a lack of code- and sex-specific algorithms. In this review, we evaluate common methods for capturing contact events in team collision sports and detail a new method for assessing contact intensity through notational analysis, offering a potential alternative for capturing contact events that are currently challenging to detect through microtechnology alone.

Distribution of position-specific head impact severities among professional and Division I collegiate American football athletes during games

Objective

To compare the severity of head impacts between professional and Division I (D-I) collegiate football games for the purpose of improving protective equipment.

Methods

A total of 243 football players from the National Football League (NFL) and from D-I of the National Collegiate Athletic Association (NCAA) were equipped with instrumented mouthpieces capable of measuring six degrees-of-freedom head kinematics. Head impacts were processed using a custom algorithm and combined with game period descriptors to produce a curated dataset for analysis. Head impact severity distributions for several kinematic-based metrics were compared within position groupings between leagues.

Results

A total of 11 038 head impacts greater than 10 g from 1208 player-games were collected during 286 player-seasons (2019-2022). No significant differences were found between leagues in the distributions of kinematic-based metrics for all investigated position groupings (p≥0.320). The median and IQRs for peak linear acceleration for NFL and NCAA were 17.2 (9.3) g and 17.0 (8.6) g for linemen, 20.7 (13.8) g and 20.0 (13.5) g for hybrid and 21.0 (17.0) g and 20.8 (15.5) g for speed position groupings, respectively.

Conclusion

The absence of statistically significant differences in the distributions of head impact severity between professional and D-I collegiate football players indicates that these data can be combined for the purpose of understanding the range of loading conditions for which new protective equipment, such as position-specific helmets, should be designed. This observation underscores the potential for knowledge transfer regarding biomechanical factors affecting head loading across professional and D-I college football, highlighting crucial implications for innovation in protective equipment.

Accuracy of Instrumented Mouthguards During Direct Jaw Impacts Seen in Boxing

PURPOSE

Measuring head kinematics data is important to understand and develop methods and standards to mitigate head injuries in contact sports. Instrumented mouthguards (iMGs) have been developed to address coupling issues with previous sensors. Although validated with anthropomorphic test devices (ATDs), there is limited post-mortem human subjects (PMHS) data which provides more accurate soft tissue responses. This study evaluated two iMGs (Prevent Biometrics (PRE) and Diversified Technical Systems (DTS) in response to direct jaw impacts.

METHODS

Three unembalmed male cadaver heads were properly fitted with two different boil-and-bite iMGs and impacted with hook (4 m/s) and uppercut (3 m/s) punches. A reference sensor (REF) was rigidly attached to the base of the skull, impact kinematics were transformed to the head center of gravity and linear and angular kinematic data were compared to the iMGs including Peak Linear Acceleration, Peak Angular Acceleration, Peak Angular Velocity, Head Injury Criterion (HIC), HIC duration, and Brain Injury Criterion.

RESULTS

Compared to the REF sensor, the PRE iMG underpredicted most of the kinematic data with slopes of the validation regression line between 0.72 and 1.04 and the DTS overpredicted all the kinematic data with slopes of the regression line between 1.4 and 8.7.

CONCLUSION

While the PRE iMG was closer to the REF sensor compared to the DTS iMG, the results did not support the previous findings reported with use of ATDs. Hence, our study highlights the benefits of using PMHS for validating the accuracy of iMGs since they closely mimic the human body compared to any ATD's mandible.

On-field Head Acceleration Exposure Measurements Using Instrumented Mouthguards: Multi-stage Screening to Optimize Data Quality

Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men's ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9 g; median peak angular acceleration from 3922 to 942 rad/s2) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.

Biofidelity and Limitations of Instrumented Mouthguard Systems for Assessment of Rigid Body Head Kinematics

Instrumented mouthguard systems (iMGs) are commonly used to study rigid body head kinematics across a variety of athletic environments. Previous work has found good fidelity for iMGs rigidly fixed to anthropomorphic test device (ATD) headforms when compared to reference systems, but few validation studies have focused on iMG performance in human cadaver heads. Here, we examine the performance of two boil-and-bite style iMGs in helmeted cadaver heads. Three unembalmed human cadaver heads were fitted with two instrumented boil-and-bite mouthguards [Prevent Biometrics and Diversified Technical Systems (DTS)] per manufacturer instructions. Reference sensors were rigidly fixed to each specimen. Specimens were fitted with a Riddell SpeedFlex American football helmet and impacted with a rigid impactor at three velocities and locations. All impact kinematics were compared at the head center of gravity. The Prevent iMG performed comparably to the reference system up to ~ 60 g in linear acceleration, but overall had poor correlation (CCC = 0.39). Prevent iMG angular velocity and BrIC generally well correlated with the reference, while underestimating HIC and overestimating HIC duration. The DTS iMG consistently overestimated the reference across all measures, with linear acceleration error ranging from 10 to 66%, and angular acceleration errors greater than 300%. Neither iMG demonstrated consistent agreement with the reference system. While iMG validation efforts have utilized ATD testing, this study highlights the need for cadaver testing and validation of devices intended for use in-vivo, particularly when considering realistic (non-idealized) sensor-skull coupling, when accounting for interactions with the mandible and when subject-specific anatomy may affect device performance.

On-Field Evaluation of Mouthpiece-and-Helmet-Mounted Sensor Data from Head Kinematics in Football

PURPOSE

Wearable sensors are used to measure head impact exposure in sports. The Head Impact Telemetry (HIT) System is a helmet-mounted system that has been commonly utilized to measure head impacts in American football. Advancements in sensor technology have fueled the development of alternative sensor methods such as instrumented mouthguards. The objective of this study was to compare peak magnitude measured from high school football athletes dually instrumented with the HIT System and a mouthpiece-based sensor system.

METHODS

Data was collected at all contact practices and competitions over a single season of spring football. Recorded events were observed and identified on video and paired using event timestamps. Paired events were further stratified by removing mouthpiece events with peak resultant linear acceleration below 10 g and events with contact to the facemask or body of athletes.

RESULTS

A total of 133 paired events were analyzed in the results. There was a median difference (mouthpiece subtracted from HIT System) in peak resultant linear and rotational acceleration for concurrently measured events of 7.3 g and 189 rad/s2. Greater magnitude events resulted in larger kinematic differences between sensors and a Bland Altman analysis found a mean bias of 8.8 g and 104 rad/s2, respectively.

CONCLUSION

If the mouthpiece-based sensor is considered close to truth, the results of this study are consistent with previous HIT System validation studies indicating low error on average but high scatter across individual events. Future researchers should be mindful of sensor limitations when comparing results collected using varying sensor technologies.

Effect of Impact Kinematic Filters on Brain Strain Responses in Contact Sports

OBJECTIVE

Impact kinematics are widely employed to investigate mechanisms of traumatic brain injury (TBI). However, they are susceptible to noise and artefacts; thus, require data filtering. Few studies have focused on how data filtering affects brain strain most relevant to TBI. Here, we report that impact-induced brain strains are much less sensitive to data filtering than kinematics based on three filtering methods: CFC180, lowpass 200 Hz, and a new method called Head Exposure to Acceleration Database in Sport (HEADSport).

METHODS

Using mouthguard-measured head impacts in elite rugby (N = 5694), average Euclidean distances between the three filtered angular velocity profiles and their unfiltered counterparts are used to identify three groups of impacts with large variations: 90-95th, 95-99th, and >99th percentile. From each group, 20 impacts are randomly selected for simulation using the anisotropic Worcester Head Injury Model (WHIM) V1.0.

RESULTS AND CONCLUSION

HEADSport and CFC180 are the most and least effective, respectively, in suppressing "unphysical artefacts" shown as sharp spikes with a rather short impulse duration (e.g., <3 ms) in angular velocity. However, maximum principal strain (MPS), especially that in the corpus callosum, is much less sensitive to data filtering compared to kinematic peaks (e.g., reduction of 3% vs. 47% and 90% for peak angular velocity and acceleration with HEADSport for impacts of >99th percentile).

SIGNIFICANCE

These findings confirm that the brain acts as a low-pass filter, itself, to suppress high frequency noise in impact kinematics. Therefore, brain strain can serve as a common metric for TBI biomechanical studies to maximize relevance to the injury, as it is not sensitive to kinematic filters.

When to Pull the Trigger: Conceptual Considerations for Approximating Head Acceleration Events Using Instrumented Mouthguards

Head acceleration events (HAEs) are acceleration responses of the head following external short-duration collisions. The potential risk of brain injury from a single high-magnitude HAE or repeated occurrences makes them a significant concern in sport. Instrumented mouthguards (iMGs) can approximate HAEs. The distinction between sensor acceleration events, the iMG datum for approximating HAEs and HAEs themselves, which have been defined as the in vivo event, is made to highlight limitations of approximating HAEs using iMGs. This article explores the technical limitations of iMGs that constrain the approximation of HAEs and discusses important conceptual considerations for stakeholders interpreting iMG data. The approximation of HAEs by sensor acceleration events is constrained by false positives and false negatives. False positives occur when a sensor acceleration event is recorded despite no (in vivo) HAE occurring, while false negatives occur when a sensor acceleration event is not recorded after an (in vivo) HAE has occurred. Various mechanisms contribute to false positives and false negatives. Video verification and post-processing algorithms offer effective means for eradicating most false positives, but mitigation for false negatives is less comprehensive. Consequently, current iMG research is likely to underestimate HAE exposures, especially at lower magnitudes. Future research should aim to mitigate false negatives, while current iMG datasets should be interpreted with consideration for false negatives when inferring athlete HAE exposure.

Head Exposure to Acceleration Database in Sport (HEADSport): a kinematic signal processing method to enable instrumented mouthguard (iMG) field-based inter-study comparisons

Objective

Instrumented mouthguard (iMG) systems use different signal processing approaches limiting field-based inter-study comparisons, especially when artefacts are present in the signal. The objective of this study was to assess the frequency content and characteristics of head kinematic signals from head impact reconstruction laboratory and field-based environments to develop an artefact attenuation filtering method (HEADSport filter method).

Methods

Laboratory impacts (n=72) on a test-dummy headform ranging from 25 to 150 g were conducted and 126 rugby union players were equipped with iMGs for 209 player-matches. Power spectral density (PSD) characteristics of the laboratory impacts and on-field head acceleration events (HAEs) (n=5694) such as the 95th percentile cumulative sum PSD frequency were used to develop the HEADSport method. The HEADSport filter method was compared with two other common filtering approaches (Butterworth-200Hz and CFC180 filter) through signal-to-noise ratio (SNR) and mixed linear effects models for laboratory and on-field events, respectively.

Results

The HEADSport filter method produced marginally higher SNR than the Butterworth-200Hz and CFC180 filter and on-field peak linear acceleration (PLA) and peak angular acceleration (PAA) values within the magnitude range tested in the laboratory. Median PLA and PAA (and outlier values) were higher for the CFC180 filter than the Butterworth-200Hz and HEADSport filter method (p<0.01).

Conclusion

The HEADSport filter method could enable iMG field-based inter-study comparisons and is openly available at https://github.com/GTBiomech/HEADSport-Filter-Method.

On-field instrumented mouthguard coupling

Instrumented mouthguard (iMG) sensors have been developed to measure sports head acceleration events (HAE) in brain injury research. Laboratory validation studies show that effective coupling of iMGs with the human skull is crucial for accurate head kinematics measurements. However, iMG-skull coupling has not been investigated in on-field sports settings. The objective of this study was to assess on-field iMG coupling using infrared proximity sensing and to investigate coupling effects on kinematics signal characteristics. Forty-two university-level men's ice hockey (n = 21) and women's rugby (n = 21) athletes participated in the study, wearing iMGs during 6-7 month in-season periods. Proximity data classified video-verified HAE recordings into four main iMG coupling categories: coupled (on-teeth), decoupling (on-teeth to off-teeth), recoupling (off-teeth to on-teeth) and decoupled (off-teeth). Poorly-coupled HAEs showed significantly higher peak angular acceleration amplitudes and greater signal power in medium-high frequency bands compared with well-coupled HAEs, indicating potential iMG movements independent of the skull. Further, even video-verified true positives included poorly-coupled HAEs, and iMG coupling patterns varied between the men's hockey and women's rugby teams. Our findings show the potential of using proximity sensing in iMGs to identify poorly-coupled HAEs. Utilizing this data screening process in conjunction with video review may mitigate a key source of sensor noise and enhance the overall quality of on-field sports HAE datasets.

An Instrumented Mouthguard for Real-Time Measurement of Head Kinematics under a Large Range of Sport Specific Accelerations

BACKGROUND

Head impacts in sports can produce brain injuries. The accurate quantification of head kinematics through instrumented mouthguards (iMG) can help identify underlying brain motion during injurious impacts. The aim of the current study is to assess the validity of an iMG across a large range of linear and rotational accelerations to allow for on-field head impact monitoring.

METHODS

Drop tests of an instrumented helmeted anthropometric testing device (ATD) were performed across a range of impact magnitudes and locations, with iMG measures collected concurrently. ATD and iMG kinematics were also fed forward to high-fidelity brain models to predict maximal principal strain.

RESULTS

The impacts produced a wide range of head kinematics (16-171 g, 1330-10,164 rad/s2 and 11.3-41.5 rad/s) and durations (6-18 ms), representing impacts in rugby and boxing. Comparison of the peak values across ATD and iMG indicated high levels of agreement, with a total concordance correlation coefficient of 0.97 for peak impact kinematics and 0.97 for predicted brain strain. We also found good agreement between iMG and ATD measured time-series kinematic data, with the highest normalized root mean squared error for rotational velocity (5.47 ± 2.61%) and the lowest for rotational acceleration (1.24 ± 0.86%). Our results confirm that the iMG can reliably measure laboratory-based head kinematics under a large range of accelerations and is suitable for future on-field validity assessments.

Consensus Head Acceleration Measurement Practices (CHAMP): Study Design and Statistical Analysis

Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis.