Mouthguard Sensor Research Articles

Energy harvesting from biting force with thin sheet harvester based on electret and dielectric elastomer

Mouthguard-type sensors have been developed to monitor oral health. These sensors are expected to be used in preventive medicine; however, long-term monitoring has not been realized owing to problems with their power supply. In this study, a thin-sheet energy harvester was developed to generate a biting force. The proposed harvester consists of an electret and a dielectric elastomer, which are embedded inside a double-layered mouthguard and integrated with a mouthguard-type sensor. By utilizing a large deformation of the dielectric elastomer owing to the biting force, the proposed harvester can generate high output power even from low-frequency movements such as the biting force. The design was optimized based on the analysis of equivalent circuit of the proposed harvester. The fabricated mouthguard harvester produced 2.5 μW under the load equivalent to the biting force, which was 560 times higher than that by piezoelectric elements under the same condition. Further experiments on power charging and feeding demonstrated that the fabricated mouthguard harvester generates sufficient power to transmit the data measured by the mouthguard sensor every 5 min. These results demonstrate that the proposed principle is effective as a power-generation method for low-frequency movements in a narrow space.

Two‐Minute Drill

Study examines correlation between artificial turf and injury NFL's mouthguard sensors gather head‐impact data SJSU, student‐athletes agree to $3.3M settlement

Head Impact Sensor Triggering Bias Introduced by Linear Acceleration Thresholding.

Contact sports players frequently sustain head impacts, most of which are mild impacts exhibiting 10-30g peak head center-of-gravity (CG) linear acceleration. Wearable head impact sensors are commonly used to measure exposure and typically detect impacts using a linear acceleration threshold. However, linear acceleration across the head can substantially vary during 6-degree-of-freedom motion, leading to triggering biases that depend on sensor location and impact condition. We conducted an analytical investigation with impact characteristics extracted from on-field American football and soccer data. We assumed typical mouthguard sensor locations and evaluated whether simulated multi-directional impacts would trigger recording based on per-axis or resultant acceleration thresholding. Across 1387 impact directions, a 10g peak CG linear acceleration impact would trigger at only 24.7% and 31.8% of directions based on a 10g per-axis and resultant acceleration threshold, respectively. Anterior impact locations had lower trigger rates and even a 30g impact would not trigger recording in some directions. Such triggering biases also varied by sensor location and linear-rotational head kinematics coupling. Our results show that linear acceleration-based impact triggering could lead to considerable bias in head impact exposure measurements. We propose a set of recommendations to consider for sensor manufacturers and researchers to mitigate this potential exposure measurement bias.

An in-silico study of the effect of non-linear skin dynamics on skin-mounted accelerometer inference of skull motion

Accurate and precise analysis of head impact telemetry data is important for development of biomechanical models and methodologies to decrease the risk of traumatic brain injury. Systematic review suggests that much existing data lacks verification. Soft tissue artefact is a common problem that is not frequently addressed. This paper outlines a method of modelling the coupled, non-linear, skull-skin-sensor system. The model is based on a second order underdamped spring mass damper system that incorporates non-linear values to account for the complex dynamic nature of skin. MATLAB was used to simulate the estimated movement of a sensor mounted to the skin relative to measurements collected via a mouthguard sensor. The non-linear elastic and damping models were developed from descriptions in literature. The model assumed a sensor of 8 g, mounted behind the ear. Results were compared to a typical linear system. In small impacts, the linear and non-linear models provided similar accelerations to the skull. However, in large impacts, the acceleration of the sensor was estimated to be 158% greater than the skull acceleration when modelled non-linearly, while a linear model showed only a 0.7% increase. This implies that for small impacts, the nonlinearity of skin-skull dynamics is not an important characteristic for modelling. However, in large impacts, the non-linearity of the skin-skull dynamic can lead to drastic over-estimates of skull acceleration when using skin mounted accelerometers. In BriefThe system of an accelerometric sensor mounted to the skin is often used to assess the accelerations experienced by athletes during impacts. By developing a non-linear model of the skull-skin-sensor model, the importance of accounting for the non-linear nature of skin while taking telemetry measurements of head acceleration during impact is demonstrated.

Identifying Risk Factors For Head Impact Exposure In High School Football Using A Validated Instrumented Mouthguard

Head impact sensor technology is evolving, and recent advances include the design and validation of instrumented mouthguards that accurately measure head impact kinematics. The use of such devices, in conjunction with video-based impact detection algorithms, can be useful in elucidating the true nature of head impact exposure in contact sports such as American football. PURPOSE: To characterize patterns of head impact exposure in high school football using a validated instrumented mouthguard sensor. METHODS: High school football athletes (n = 116) wore Stanford instrumented mouthguards (the “MiG2.0”) during practices and games in their 2018 and 2019 Fall seasons. A validated impact detection algorithm made specifically for the MiG2.0 in American football was used to filter out false-positive accelerative events recorded by the MiG2.0 devices. Peak linear acceleration (PLA) and peak rotational acceleration (PRA) of verified head impacts were saved for analyses. Helmet model, level of competition (JV or varsity), body mass index (BMI), age, and prior concussion history were also recorded at the beginning of each season for each athlete. RESULTS: 66969 events were recorded by the MiG2.0 sensors across 888 athlete exposures. The impact detection algorithm determined that 713 events were true head impacts. Athletes wearing Riddell Speedflex helmets sustained head impacts of lower PRA than those wearing VICIS Zero1 helmets (p < 0.01). Athletes with a previous concussion history sustained head impacts of lower PLA than those without (p < 0.04). No statistically significant differences were found between peak kinematics of head impacts between athletes of varying age, BMI, or level of competition. Higher number of previous concussions, varsity competition, and higher BMI were associated with more frequent head impacts (p < 0.036). CONCLUSION: The MiG2.0 instrumented mouthguard sensor, when used with a validated impact detection algorithm, is an effective tool for characterizing head impact exposure in high school football. In our cohort, concussion history and helmet model affected the severity of head impacts sustained by athletes, and the in vivo performance of helmets may differ from previous reports of laboratory helmet testing. Athletes with more football experience may sustain head impacts more frequently.

Differences in Head Impact Exposures Between Youth Tackle and Flag Football Games and Practices: Potential Implications for Prevention Strategies

Background: Interventions designed to reduce the risk for head impacts and concussion in youth football have increased over the past decade; however, understanding of the role of regular game play on head impact exposure among youth tackle and flag football athletes is currently limited. Purpose: To explore head impact exposure among youth tackle and flag football athletes (age range, 6-14 years) during both practices and games. Study Design: Cohort study; Level of evidence, 2. Methods: Using the Vector MouthGuard sensor, the authors collected head impact data from 524 tackle and flag youth football athletes over the course of a football season. Quantities of interest were estimated from regression models using Bayesian methods. Results: For impacts ≥10g, a tackle football athlete had an estimated 17.55 (95% CI, 10.78-28.96) times more head impacts per practice compared with a flag football athlete (6.85 [95% CI, 6.05-7.76] and 0.39 [95% CI, 0.24-0.62] head impacts, respectively). Additionally, a tackle football athlete had an estimated 19.48 (95% CI, 12.74-29.98) times more head impacts per game compared with a flag football athlete (13.59 [95% CI, 11.97-15.41] and 0.70 [95% CI, 0.46-1.05] head impacts, respectively). Among tackle football athletes, the estimated average impact rate was 6.51 (95% CI, 5.75-7.37) head impacts during a practice and 12.97 (95% CI, 11.36-14.73) impacts during a game, resulting in 2.00 (95% CI, 1.74-2.29) times more ≥10g head impacts in games versus practices. Tackle football athletes had 2.06 (95% CI, 1.80-2.34) times more high-magnitude head impacts (≥40g) during a game than during a practice. On average, flag football athletes experienced an estimated 0.37 (95% CI, 0.20-0.60) head impacts during a practice and 0.77 (95% CI, 0.53-1.06) impacts during a game, resulting in 2.06 (95% CI, 1.29-3.58) times more ≥10g head impacts in games versus practices. Because of model instability caused by a large number of zero impacts for flag football athletes, a comparison of high-magnitude head impacts is not reported for practices or games. Conclusion: This study provides a characterization of the head impact exposure of practices and games among a large population of youth tackle and flag football athletes aged 6 to 14 years. These findings suggest that a greater focus on game-based interventions, such as fair play interventions and strict officiating, may be beneficial to reduce head impact exposures for youth football athletes.

A new open-access platform for measuring and sharing mTBI data

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.

Head Impact Exposures Among Youth Tackle and Flag American Football Athletes.

Background:Promoted as a safer alternative to tackle football, there has been an increase in flag football participation in recent years. However, examinations of head impact exposure in flag football as compared with tackle football are currently limited.Hypothesis:Tackle football athletes will have a greater number and magnitude of head impacts compared with flag football athletes.Study Design:Cohort study.Level of Evidence:Level 4.Methods:Using mouthguard sensors, this observational, prospective cohort study captured data on the number and magnitude of head impacts among 524 male tackle and flag football athletes (6-14 years old) over the course of a single football season. Estimates of interest based on regression models used Bayesian methods to estimate differences between tackle and flag athletes.Results:There were 186,239 head impacts recorded during the study. Tackle football athletes sustained 14.67 (95% CI 9.75-21.95) times more head impacts during an athletic exposure (game or practice) compared with flag football athletes. Magnitude of impact for the 50th and 95th percentile was 18.15g (17.95-18.34) and 52.55g (51.06-54.09) for a tackle football athlete and 16.84g (15.57-18.21) and 33.51g (28.23-39.08) for a flag football athlete, respectively. A tackle football athlete sustained 23.00 (13.59-39.55) times more high-magnitude impacts (≥40g) per athletic exposure compared with a flag football athlete.Conclusion:This study demonstrates that youth athletes who play tackle football are more likely to experience a greater number of head impacts and are at a markedly increased risk for high-magnitude impacts compared with flag football athletes.Clinical Relevance:These results suggest that flag football has fewer head impact exposures, which potentially minimizes concussion risk, making it a safer alternative for 6- to 14-year-old youth football athletes.

Development of a Wearable Mouth Guard Device for Monitoring Teeth Clenching during Exercise

Teeth clenching during exercise is important for sports performance and health. Recently, several mouth guard (MG)-type wearable devices for exercise were studied because they do not disrupt the exercise. In this study, we developed a wearable MG device with force sensors on both sides of the maxillary first molars to monitor teeth clenching. The force sensor output increased linearly up to 70 N. In four simple occlusion tests, the trends exhibited by the outputs of the MG sensor were consistent with those of an electromyogram (EMG), and the MG device featured sufficient temporal resolution to measure the timing of teeth clenching. When the jaw moved, the MG sensor outputs depended on the sensor position. The MG sensor output from the teeth-grinding test agreed with the video-motion analysis results. It was comparatively difficult to use the EMG because it contained a significant noise level. Finally, the usefulness of the MG sensor was confirmed through an exercise tolerance test. This study indicated that the developed wearable MG device is useful for monitoring clenching timing and duration, and the degree of clenching during exercise, which can contribute to explaining the relationship between teeth clenching and sports performance.

A Qualitative Study of Youth Football Coaches' Perception of Concussion Safety in American Youth Football and Their Experiences With Implementing Tackling Interventions.

Due in part to concern about the potential long-term effects of concussion and repetitive head injuries in football, some programs have implemented tackling interventions. This paper explores youth football coaches' perception of football safety and their experiences implementing these interventions aimed at athlete safety. Using a qualitative approach, coaches were interviewed by means of a semi-structured protocol that covered: (a) demographics; (b) background and experiences; (c) personal relevance risks, safety, and benefits of youth football; (d) experiences with tackling technique; (e) experiences with mouth guard sensors; and (f) opinions on disseminating information on football safety. Most coaches felt that learning tackling at a young age helped prepare them for their playing later in life and believed that youth should begin playing tackle football at a young age. Coaches were mixed regarding their concerns about the risk for concussion and subconcussive head impacts. Still, most were receptive to changes in rules and policies aimed at making football safer. Findings from this study demonstrate that youth football coaches are important stakeholders to consider when implementing changes to youth football. Understanding coach perceptions and experiences may inform future efforts aimed to educate coaches on rules and policies to make the game safer for youth athletes.

On-Field Performance of an Instrumented Mouthguard for Detecting Head Impacts in American Football

Wearable sensors that accurately record head impacts experienced by athletes during play can enable a wide range of potential applications including equipment improvements, player education, and rule changes. One challenge for wearable systems is their ability to discriminate head impacts from recorded spurious signals. This study describes the development and evaluation of a head impact detection system consisting of a mouthguard sensor and machine learning model for distinguishing head impacts from spurious events in football games. Twenty-one collegiate football athletes participating in 11 games during the 2018 and 2019 seasons wore a custom-fit mouthguard instrumented with linear and angular accelerometers to collect kinematic data. Video was reviewed to classify sensor events, collected from instrumented players that sustained head impacts, as head impacts or spurious events. Data from 2018 games were used to train the ML model to classify head impacts using kinematic data features (127 head impacts; 305 non-head impacts). Performance of the mouthguard sensor and ML model were evaluated using an independent test dataset of 3 games from 2019 (58 head impacts; 74 non-head impacts). Based on the test dataset results, the mouthguard sensor alone detected 81.6% of video-confirmed head impacts while the ML classifier provided 98.3% precision and 100% recall, resulting in an overall head impact detection system that achieved 98.3% precision and 81.6% recall.

Development of an Electrostatic Oral Cavity Generator Driven by Occlusal Force.

Mouthguard type sensors have been developed to monitor the healthcare-related information, which is expected to be used for pre-illness and preventive medicine. However, a method for supplying the power to these sensors is still an issue to be solved. In this paper, we propose an electrostatic oral cavity generator driven by occlusal force to supply the power for mouthguard type sensors. The proposed generator is a sheet form and consists of the lamination of the electret, dielectric elastomer and copper electrode. In this paper, we estimated the power generation with a piezoelectric generator and the proposed electrostatic generator. Then, the generators were designed and fabricated. The generated power of the prototype electrostatic generator was 18.0 μW. It was about 560 times larger than that of a piezoelectric generator with the area equivalent to four pairs of molars. Although the generated power was still smaller than the required power to drive the mouthguard sensor, we considered they can be comparable with each other by optimizing the design parameters of the generator.

Cavitas electrochemical sensor toward detection of N-epsilon (carboxymethyl)lysine in oral cavity

Advanced glycated end markers are involved in oxidative stress by producing long-term damage to proteins in ageing processes, atherosclerosis, and diabetes. The present work describes a cavitas printed electrochemical sensor for the direct salivary detection of N€(Carboxymethyl)lysine, a major advanced glycated end compound. The highly flexible and bendable sensor is integrated on a customized mouthguard placed on a phantom jaw that imitated the structure of the human oral cavity. The disposable sensor is readily attached on the mouthguard before usage and detached after the electrochemical investigation. Thus, it can be replaced whenever required. The sensor exhibits high selectivity and sensitivity in the phosphate buffer with a limit of detection of 166 ng/ml (equivalent to 0.81 μM) (over a range of 0.5–2500 μg/ml (equivalent to 2.45 μM–12.24 mM) N€(Carboxymethyl)lysine by employing a differential pulse voltammetry analytical procedure. Moreover, the cavitas sensor was tested in raw, untreated human saliva, and promising recoveries were obtained. The cost advantages of the printing technique, short timescale of the measurements, long storage stability and ease of use are attractive properties of the new mouthguard sensor. This non-invasive oral sensor for salivary N€(Carboxymethyl)lysine monitoring could potentially provide useful real-time information regarding a wearer's health condition, and thus, it holds considerable promise for the improved management of chronic diseases.

Comparison of video-based and sensor-based head impact exposure.

Previous research has sought to quantify head impact exposure using wearable kinematic sensors. However, many sensors suffer from poor accuracy in estimating impact kinematics and count, motivating the need for additional independent impact exposure quantification for comparison. Here, we equipped seven collegiate American football players with instrumented mouthguards, and video recorded practices and games to compare video-based and sensor-based exposure rates and impact location distributions. Over 50 player-hours, we identified 271 helmet contact periods in video, while the instrumented mouthguard sensor recorded 2,032 discrete head impacts. Matching video and mouthguard real-time stamps yielded 193 video-identified helmet contact periods and 217 sensor-recorded impacts. To compare impact locations, we binned matched impacts into frontal, rear, side, oblique, and top locations based on video observations and sensor kinematics. While both video-based and sensor-based methods found similar location distributions, our best method utilizing integrated linear and angular position only correctly predicted 81 of 217 impacts. Finally, based on the activity timeline from video assessment, we also developed a new exposure metric unique to American football quantifying number of cross-verified sensor impacts per player-play. We found significantly higher exposure during games (0.35, 95% CI: 0.29–0.42) than practices (0.20, 95% CI: 0.17–0.23) (p<0.05). In the traditional impacts per player-hour metric, we observed higher exposure during practices (4.7) than games (3.7) due to increased player activity in practices. Thus, our exposure metric accounts for variability in on-field participation. While both video-based and sensor-based exposure datasets have limitations, they can complement one another to provide more confidence in exposure statistics.

Cavitas Sensors: Contact Lens Type Sensors &amp; Mouthguard Sensors

Abstract“Cavitas sensors” attached to body cavities such as the contact lens and mouthguard (“no implantable”, “no wearable”) are attracted attention as self‐detachable devices for daily medicine. Many types of contact lens (CL) sensors using electrical and optical methods have been developed for monitoring not only chemicals (glucose, lactate) and electrical conductivity in tear fluid, but also transcutaneous gases at eyelid mucosa. A CL intraocular pressure telemetric sensor has been also commercialized and applied to patients for monitoring the intraocular pressure. Some mouthguard sensors have been investigated for a real‐time measurement of salivary chemicals. A graphene based sensor on tooth enamel was reported to be capable of salivary bacterium detection. Here we review the challenges regarding the integration of biosensors into monitoring for biological information of body cavities. The self‐detachable cavitas sensors are expected to improve the quality of life in the near future.

A headform for testing helmet and mouthguard sensors that measure head impact severity in football players.

A headform is needed to validate and compare helmet- and mouthguard-based sensors that measure the severity and direction of football head impacts. Our goal was to quantify the dynamic response of a mandibular load-sensing headform (MLSH) and to compare its performance and repeatability to an unmodified Hybrid III headform. Linear impactors in two independent laboratories were used to strike each headform at six locations at 5.5 m/s and at two locations at 3.6 and 7.4 m/s. Impact severity was quantified using peak linear acceleration (PLA) and peak angular acceleration (PAA), and direction was quantified using the azimuth and elevation of the PLA. Repeatability was quantified using coefficients of variation (COV) and standard deviations (SD). Across all impacts, PLA was 1.6±1.8 g higher in the MLSH than in the Hybrid III (p=0.002), but there were no differences in PAA (p=0.25), azimuth (p=0.43) and elevation (p=0.11). Both headforms exhibited excellent or acceptable repeatability for PLA (HIII:COV=2.1±0.8%, MLSH:COV=2.0±1.2%, p=0.98), but site-specific repeatability ranging from excellent to poor for PAA (HIII:COV=7.2±4.0%, MLSH:COV=8.3±5.8%, p=0.58). Direction SD were generally <1° and did not vary between headforms. Overall, both headforms are similarly suitable for validating PLA in sensors that measure head impact severity in football players, however their utility for validating sensor PAA values varies with impact location.