Evaluating the Agreement between Oral, Armpit, and Ear Temperature Readings during Physical Activities in an Outdoor Setting.

Accurate body temperature measurement is essential for monitoring and managing safety during outdoor activities. Physical activities are an essential consideration for public health, with sports taking up an important proportion of these. Athletes' performances can be directly affected by body temperature fluctuations, with overheating or hypothermia posing serious health risks. Monitoring these temperatures allows coaches and medical staff to make decisions that enhance performance and safety. Traditional methods, like oral, axillary, and tympanic readings, are widely used, but face challenges during intense physical activities in real-world environments. This study evaluated the agreement, correlation, and interchangeability of oral, axillary, and tympanic temperature measurements in outdoor exercise conditions. Systems developed for specific placements might generate different sensor readouts. Conducted as an observational field study, it involved 21 adult participants (11 males and 10 females, average age 25.14 ± 5.80 years) that underwent the Yo-Yo intermittent recovery test protocol on an outdoor court. The main outcomes measured were the agreement and correlation between temperature readings from the three methods, both before and after exercise. The results indicate poor agreement between the measurement sites, with significant deviations observed post-exercise. Although the Spearman correlation coefficients showed consistent temperature changes post-exercise across all methods, the standard deviations in the pairwise comparisons exceeded 0.67 °C. This study concluded that widely used temperature measurement methods are challenging to use during outdoor exercises and should not be considered interchangeable. This variability, especially after exercise, underscores the need for further research using gold standard temperature measurement methods to determine the most suitable site for accurate readings. Care should thus be taken when temperature screening is done at scale using traditional methods, as each measurement site should be considered within its own right.

Clinician and parent views on urine collection in precontinent children in the UK: a qualitative interview study.

OBJECTIVE: To explore the experiences of healthcare professionals (HCPs) and parents of urine collection methods, to identify barriers to successful sampling and what could improve the process. DESIGN: Qualitative research, using individual semistructured interviews with HCPs and parents. The interviews were audiorecorded, transcribed and thematically analysed. SETTING: UK-based HCPs from primary and secondary care settings and parents with experience with urine collection in primary and/or secondary care settings. PARTICIPANTS: HCPs who were involved in aiding, supervising or ordering urine samples. Parents who had experience with urine collection in at least one precontinent child. RESULTS: 13 HCPs and 16 parents were interviewed. 2 participating HCPs were general practitioners (GPs), 11 worked in paediatric secondary care settings (8 were nurses and 3 were doctors). Two parents had children with underlying conditions where frequent urine collection was required to rule out infections.HCPs and parents reported that there were no straightforward methods of urine collection for precontinent children. Each method-'clean catch', urine bag and urine pad-had limitations and problems with usage. 'Clean catch', regarded as the gold standard by HCPs with a lower risk of contamination, often proved difficult for parents to achieve. Other methods had elevated risk of contamination but were more acceptable to parents because they were less challenging. Many of the parents expressed the need for more information about urine collection. CONCLUSIONS: Current methods of urine collection are challenging to use and may be prone to contamination. A new device is required to assist with urine collection in precontinent children, to simplify and reduce the stress of the situation for those involved. Parents are key partners in the process of urine collection with young children. Meeting their expressed need for more information could be an important way to achieve better-quality samples while awaiting a new device.

A modular, deep learning-based holistic intent sensing system tested with Parkinson's disease patients and controls.

People living with mobility-limiting conditions such as Parkinson's disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpreting the outputs using time-segmented deep learning. It is tested on a new data set consisting of a mix of non-disabled control volunteers and participants with Parkinson's disease, and used to classify three activities of daily living as quickly and accurately as possible. Results showed intent could be determined with an accuracy of 97.4% within 0.5 s of inception of the idea to act, which subsequently improved monotonically to a maximum of 99.9918% over the course of the activity. This evidence supports the conclusion that intent sensing is viable as a potential input for assistive medical devices.

Non-Contact Infrared Thermometers and Thermal Scanners for Human Body Temperature Monitoring: A Systematic Review.

In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic review sought to critically evaluate the performance of NCITs and IRT in body temperature monitoring, synthesizing evidence from a total of 72 unique settings from 32 studies. The studies incorporated in our review ranged from climate-controlled room investigations to clinical applications. Our primary findings showed that NCITs and IRT can provide accurate and reliable body temperature measurements in specific settings and conditions. We revealed that while both NCITs and IRT displayed a consistent positive correlation with conventional, contact-based temperature measurement tools, NCITs demonstrated slightly superior accuracy over IRT. A total of 29 of 50 settings from NCIT studies and 4 of 22 settings from IRT studies achieved accuracy levels within a range of ±0.3 °C. Furthermore, we found that several factors influenced the performance of these devices. These included the measurement location, the type of sensor, the reference and tool, individual physiological attributes, and the surrounding environmental conditions. Our research underscores the critical need for further studies in this area to refine our understanding of these influential factors and to develop standardized guidelines for the use of NCITs and IRT.

Evaluating the Potential of an Oral-Based Bioguard to Estimate Heart Rate Using Photoplethysmography.

The reliable monitoring of heart rate during intense exercise is imperative to effectively manage training loads while providing insights from a healthcare perspective. However, current technologies perform poorly in contact sports settings. This study aims to evaluate the best approach for heart rate tracking using photoplethysmography sensors embedded into an instrumented mouthguard (iMG). Seven adults wore iMGs and a reference heart rate monitor. Several sensor placements, light sources and signal intensities were explored for the iMG. A novel metric related to the positioning of the sensor in the gum was introduced. The error between the iMG heart rate and the reference data was assessed to obtain insights into the effect of specific iMG configurations on measurement errors. Signal intensity was found to be the most important variable for error prediction, followed by the sensor light source, sensor placement and positioning. A generalized linear model combining an infrared light source, at an intensity of 5.08 mA, and a frontal placement high in the gum area resulted in a heart rate minimum error of 16.33%. This research shows promising preliminary results for the use of oral-based heart rate monitoring, but highlights the need for the careful consideration of sensor configurations within these systems.

Performance of Oral Cavity Sensors: A Systematic Review.

Technological advancements are enabling new applications within biomedical engineering. As a connection point between the outer environment and the human system, the oral cavity offers unique opportunities for sensing technologies. This paper systematically reviews the performance of measurement systems tested in the human oral cavity. Performance was defined by metrics related to accuracy and agreement estimation. A comprehensive search identifying human studies that reported on the accuracy or agreement of intraoral sensors found 85 research papers. Most of the literature (62%) was in dentistry, followed by neurology (21%), and physical medicine and rehabilitation (12%). The remaining papers were on internal medicine, obstetrics, and aerospace medicine. Most of the studies applied force or pressure sensors (32%), while optical and image sensors were applied most widely across fields. The main challenges for future adoption include the lack of large human trials, the maturity of emerging technologies (e.g., biochemical sensors), and the absence of standardization of evaluation in specific fields. New research should aim to employ robust performance metrics to evaluate their systems and incorporate real-world evidence as part of the evaluation process. Oral cavity sensors offer the potential for applications in healthcare and wellbeing, but for many technologies, more research is needed.

Central Sleep Apnea Detection by Means of Finger Photoplethysmography.

Obstructive Sleep Apnea (OSA) and Central Sleep Apnea (CSA) are two types of Sleep Apnea (SA) with different etiologies and treatment options. Home sleep apnea testing based on photoplethysmography-derived peripheral arterial tonometry (PAT HSAT) has become the most widely deployed outpatient SA diagnostic method. Being able to differentiate between CSA and OSA based solely on photoplethysmography-data would further increase PAT HSAT's clinical utility. The present work proposes a method to detect CSA using finger photoplethysmography (PPG) data and evaluates the proposed method against simultaneous in-lab polysomnography (PSG). METHODS: For 266 patients with a suspicion of SA, concurrent in-lab PSG and PPG data were acquired. The respiratory information embedded in the PPG data was extracted and used to train an ensemble of trees classifiers that predicts the central or obstructive nature of each respiratory event. The classifier performance was evaluated using patient-wise leave-one-out cross-validation where an expert analysis of the PSG served as ground truth. A second, independent analysis of the PSG was also evaluated against the ground truth to allow benchmarking of the PPG-based method. RESULTS: The method achieved a sensitivity of 81%, a specificity of 99%, a positive predictive value of 90%, and a negative predictive value of 98% at the central apnea-hypopnea index cutoff of 10 events per hour of sleep. CONCLUSION AND SIGNIFICANCE: The present study aimed to evaluate a method to detect CSA in SA patients using only PPG data which could be used to flag CSA which in turn may aid in more optimal therapy decision making.

Comparison of a Scaled Cadaver-Based Musculoskeletal Model With a Clinical Upper Extremity Model.

Reliably and accurately estimating joint/segmental kinematics from optical motion capture data has remained challenging. Studies objectively characterizing human movement patterns have typically involved inverse kinematics and inverse dynamics techniques. Subsequent research has included scaled cadaver-based musculoskeletal (MSK) modeling for noninvasively estimating joint and muscle loads. As one of the ways to enhance confidence in the validity of MSK model predictions, the kinematics from the preceding step that drives such a model needs to be checked for agreement or compared with established/widely used models. This study rigorously compares the upper extremity (UE) joint kinematics calculated by the Dutch Shoulder Model implemented in the AnyBody Managed Model Repository (involving multibody kinematics optimization (MKO)) with those estimated by the Vicon Plug-in Gait model (involving single-body kinematics optimization (SKO)). Ten subjects performed three trials of (different types of) reaching tasks in a three-dimensional marker-based optical motion capture laboratory setting. Joint angles, processed marker trajectories, and reconstruction residuals corresponding to both models were compared. Scatter plots and Bland-Altman plots were used to assess the agreement between the two model outputs. Results showed the largest differences between the two models for shoulder, followed by elbow and wrist, with all root-mean-squared differences less than 10 deg (although this limit might be unacceptable for clinical use). Strong-to-excellent Spearman's rank correlation coefficients were found between the two model outputs. The Bland-Altman plots showed a good agreement between most of the outputs. In conclusion, results indicate that these two models with different kinematic algorithms broadly agree with each other, albeit with few key differences.

Determining the performance of a temperature sensor embedded into a mouthguard.

OBJECTIVE: This study aimed to determine the steady-state errors of oral-based temperature sensors, that are embedded in mouthguards, using a robust assessment process. MATERIALS AND METHODS: Four electronic boards with temperature sensors were encapsulated in mouthguards made from ethylene-vinyl acetate (EVA). The error and time to reach steady-state temperature were determined using a thermostatic water bath during three different conditions (34, 38.5 and 43 °C). Subsequently, a case study of one volunteer wearing the instrumented mouthguard is presented. RESULTS: The water bath tests showed that a mean absolute error of 0.2 °C was reached after a maximum of 690 s across all test conditions. The case study yielded an absolute error was 0.2 °C after 1110 s. CONCLUSION: These results show that an instrumented mouthguard with temperature sensing capabilities can yield a consistent steady-state error that is close to the clinical requirements across a range of temperatures. However, the time it takes to reach steady-state temperature needs to be considered for these systems to correctly interpret the outcomes.

A multicentric validation study of a novel home sleep apnea test based on peripheral arterial tonometry.

STUDY OBJECTIVES: This paper reports on the multicentric validation of a novel FDA-cleared home sleep apnea test based on peripheral arterial tonometry (PAT HSAT). METHODS: One hundred sixty-seven participants suspected of having obstructive sleep apnea (OSA) were included in a multicentric cohort. All patients underwent simultaneous polysomnography (PSG) and PAT HSAT, and all PSG data were independently double scored using both the recommended 1A rule for hypopnea, requiring a 3% desaturation or arousal (3% Rule), and the acceptable 1B rule for hypopnea, requiring a 4% desaturation (4% Rule). The double-scoring of PSG enabled a comparison of the agreement between PAT HSAT and PSG to the inter-rater agreement of PSG. Clinical endpoint parameters were selected to evaluate the device's ability to determine the OSA severity category. Finally, a correction for near-boundary apnea-hypopnea index values was proposed to adequately handle the inter-rater variability of the PSG benchmark. RESULTS: For both the 3% and the 4% Rules, most endpoint parameters showed a close agreement with PSG. The 4-way OSA severity categorization accuracy of PAT HSAT was strong, but nevertheless lower than the inter-rater agreement of PSG (70% vs 77% for the 3% Rule and 78% vs 81% for the 4% Rule). CONCLUSIONS: This paper reported on a multitude of robust endpoint parameters, in particular OSA severity categorization accuracies, while also benchmarking clinical performances against double-scored PSG. This study demonstrated strong agreement of PAT HSAT with PSG. The results of this study also suggest that different brands of PAT HSAT may have distinct clinical performance characteristics.

Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks.

Intent sensing-the ability to sense what a user wants to happen-has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.

Impact behaviour of 3D printed cellular structures for mouthguard applications.

Ethylene-Vinyl Acetate (EVA) is the most popular material for manufacturing mouthguards. However, EVA mouthguards are problematic, for example inconsistent thicknesses across the mouthguard. Additive manufacturing provides a promising solution to this problem, as it can manufacture mouthguards with a greater precision. This paper compares the energy dissipation of EVA, the current material used for mouthguards, to various designs of a 3D printed material, some of which contain air cells. Impact testing was carried out at three different strain rates. The Split-Hopkinson bar was used for medium and high strain rate tests, and an Instron test rig was used for low strain rate testing. The best performing design dissipated 25% more energy than EVA in the medium and high strain rate testing respectively while the low strain rate testing was inconclusive. This research has shown that additive manufacturing provides a viable method of manufacturing mouthguards. This opens up the opportunity for embedding electronics/sensors into additive manufactured mouthguards.

Impact and workload are dominating on-field data monitoring techniques to track health and well-being of team-sports athletes.

Objective.Participation in sports has become an essential part of healthy living in today's world. However, injuries can often occur during sports participation. With advancements in sensor technology and data analytics, many sports have turned to technology-aided, data-driven, on-field monitoring techniques to help prevent injuries and plan better player management.Approach.This review searched three databases, Web of Science, IEEE, and PubMed, for peer-reviewed articles on on-field data monitoring techniques that are aimed at improving the health and well-being of team-sports athletes.Main results. It was found that most on-field data monitoring methods can be categorized as either player workload tracking or physical impact monitoring. Many studies covered during this review attempted to establish correlations between captured physical and physiological data, as well as injury risk. In these studies, workloads are frequently tracked to optimize training and prevent overtraining in addition to overuse injuries, while impacts are most often tracked to detect and investigate traumatic injuries.Significance. This review found that current sports monitoring practices often suffer from a lack of standard metrics and definitions. Furthermore, existing data-analysis models are created on data that are limited in both size and diversity. These issues need to be addressed to create ecologically valid approaches in the future.

HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors.

Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation.

Correlations between home sleep apnea tests and polysomnography outcomes do not fully reflect the diagnostic accuracy of these tests.

STUDY OBJECTIVES: The clinical performance of home sleep apnea tests (HSATs) can be described by their (diagnostic) accuracy, defined as the percentage agreement with the obstructive sleep apnea severity category (normal, mild, moderate, and severe) based on polysomnography. Rather than reporting on accuracy, there has been a strong reliance in the literature to report correlation coefficients between the apnea-hypopnea index of HSATs and polysomnography to support claims of diagnostic performance. This is surprising, as it has been well described that correlation coefficients are inadequate to evaluate equivalence between 2 parameters. The aim of this study was to systematically investigate the magnitude of the discrepancies between correlation coefficients and diagnostic accuracy reported in or retrievable from HSAT validation studies. METHODS: We compared the discrepancy between accuracy and apnea-hypopnea index correlation coefficients of all validation papers that met the inclusion criteria. A total of 20 papers were retained, representing a participant pool of 1,652. RESULTS: The weighted average apnea-hypopnea index correlation across all 20 papers was 0.82 and the weighted average accuracy was 0.61, highlighting a discrepancy of 0.21 and an overall misdiagnosis rate of 39%. CONCLUSIONS: The results of our study confirm the need for increased scientific rigor in selecting primary performance endpoints to support clinical performance claims of HSATs. CITATION: Massie F, Van Pee B, Bergmann J. Correlations between home sleep apnea tests and polysomnography outcomes do not fully reflect the diagnostic accuracy of these tests. J Clin Sleep Med. 2022;18(3):871-876.

Phenotyping REM OSA by means of peripheral arterial tone-based home sleep apnea testing and polysomnography: A critical assessment of the sensitivity and specificity of both methods.

The clinical relevance of rapid eye movement sleep-related obstructive sleep apnea (REM OSA) is supported by its associated adverse health outcomes and impact on optimal treatment strategies. To date, no assessment of REM OSA phenotyping performance has been conducted for any type of sleep testing technology. The objective of this study was to assess this for polysomnography and peripheral arterial tone-based home sleep apnea testing (PAT HSAT). In a dataset comprising 261 participants, the sensitivity and specificity of the agreement on REM OSA phenotyping was assessed for two independent scorings of polysomnography and a synchronously administered PAT HSAT. The sensitivity and specificity of REM OSA phenotyping were 0.87 and 0.89, respectively, for the polysomnography inter-scorer comparison, and 0.68 and 0.97 for the PAT HSAT on a single-night basis, using the conventional minimum required rapid eye movement sleep time of 30 min. Polysomnography-based REM OSA phenotyping was found to be sensitive and specific even for a single-night testing protocol. Peripheral arterial tone-based REM OSA phenotyping showed a lower sensitivity but a slightly higher specificity compared to polysomnography. In order to increase performance and conclusiveness of peripheral arterial tone-based REM OSA phenotyping, a multi-night protocol of 2-5 nights could be considered. Finally, the minimum required rapid eye movement sleep time could be lowered from the conventional 30 min to 15 min without significantly lowering REM OSA phenotyping sensitivity and specificity, while increasing the level of phenotyping conclusiveness.

Tracking the Presence of Software as a Medical Device in US Food and Drug Administration Databases: Retrospective Data Analysis.

BACKGROUND: Software as a medical device (SaMD) has gained the attention of medical device regulatory bodies as the prospects of standalone software for use in diagnositic and therapeutic settings have increased. However, to date, figures related to SaMD have not been made available by regulators, which limits the understanding of how prevalent these devices are and what actions should be taken to regulate them. OBJECTIVE: The aim of this study is to empirically evaluate the market approvals and clearances related to SaMD and identify adverse incidents related to these devices. METHODS: Using databases managed by the US medical device regulator, the US Food and Drug Administration (FDA), we identified the counts of SaMD registered with the FDA since 2016 through the use of product codes, mapped the path SaMD takes toward classification, and recorded adverse events. RESULTS: SaMD does not seem to be registered at a rate dissimilar to that of other medical devices; thus, adverse events for SaMD only comprise a small portion of the total reported number. CONCLUSIONS: Although SaMD has been identified in the literature as an area of development, our analysis suggests that this growth has been modest. These devices are overwhelmingly classified as moderate to high risk, and they take a very particular path to that classification. The digital revolution in health care is less pronounced when evidence related to SaMD is considered. In general, the addition of SaMD to the medical device market seems to mimic that of other medical devices.

A Pedestrian Dead Reckoning Method for Head-Mounted Sensors.

Pedestrian dead reckoning (PDR) plays an important role in modern life, including localisation and navigation if a Global Positioning System (GPS) is not available. Most previous PDR methods adopted foot-mounted sensors. However, humans have evolved to keep the head steady in space when the body is moving in order to stabilise the visual field. This indicates that sensors that are placed on the head might provide a more suitable alternative for real-world tracking. Emerging wearable technologies that are connected to the head also makes this a growing field of interest. Head-mounted equipment, such as glasses, are already ubiquitous in everyday life. Whilst other wearable gear, such as helmets, masks, or mouthguards, are becoming increasingly more common. Thus, an accurate PDR method that is specifically designed for head-mounted sensors is needed. It could have various applications in sports, emergency rescue, smart home, etc. In this paper, a new PDR method is introduced for head mounted sensors and compared to two established methods. The data were collected by sensors that were placed on glasses and embedded into a mouthguard. The results show that the newly proposed method outperforms the other two techniques in terms of accuracy, with the new method producing an average end-to-end error of 0.88 m and total distance error of 2.10%.

Comparing thermal discomfort with skin temperature response of lower-limb prosthesis users during exercise.

BACKGROUND: Thermal discomfort is prevalent among prosthesis users. This observational study of thirty unilateral lower-limb prosthesis users compared their skin temperatures and the thermal discomfort experienced during exercise between their residual and contralateral limbs. METHODS: Participants performed a 2-minute interval cycling exercise test. Skin temperature was measured at matched locations on each leg during the 1-minute rest intervals. Average rate-of-change in skin temperature was compared between legs using a repeated measures analysis of variance. Participants rated thermal discomfort on each leg before and after exercise, and a Wilcoxon signed-rank test was used to compare legs. Ordinal regression evaluated the relationship between the rate-of-change in temperature on the residual limb and the perceived thermal discomfort. FINDINGS: After exercise, thermal discomfort ranked higher on the amputated side (P = 0.007). On average, both legs cooled during exercise (P = 0.002), but the difference between legs was not significant. The rate-of change in skin temperature on the residual limb during exercise did not relate to the thermal discomfort experienced (odds ratio of 0.357). INTERPRETATION: These findings indicate that in this patient population, skin temperature does not explain the thermal discomfort experienced, and subjective thermal discomfort is inadequate for detecting thermoregulatory issues, with potential implications for long-term tissue health.

Mechanical Safety of Embedded Electronics for In-body Wearables: A Smart Mouthguard Study.

The growing popularity of contact sports drives the requirement for better design of protective equipment, such as mouthguards. Smart mouthguards with embedded electronics provide a multitude of new ways to provide increased safety and protection to users. Characterisation of how electronic components embedded in typical mouthguard material, ethylene vinyl acetate (EVA), behave under typical sports impacts is crucial for future designs. A novel pendulum impact rig using a hockey ball disc impactor was developed to investigate impact forces and component failure. Two sets of dental models (aluminium and plastic padding chemical metal) were used to manufacture post-thermoformed mouthguards. Seven embedding conditions with varying thickness of EVA (1.5 and 3 mm) and locations of electrical components were tested. Component failures were observed in four out of seven test conditions, and the experimental failure forces at which the electrical component had a 50% chance of failure were reported for those cases. The experimental results showed that an EVA thickness of 3 mm surrounding the electrical component gives the most comprehensive protection even under extreme surface conformity. Computational models on surface conformity of EVA showed that a block of EVA with a minimum thickness of 1.5 mm was better at reducing stress concentration than a shell with an overall thickness of 1.5 mm. This study demonstrated that the thickness of a mouthguard is important when protecting electrical components from extreme dental surface conformity, furthermore the surface geometry should not be overlooked when considering electrical component safety for in-body wearables that are impact prone.