Choosing Optimal Cutoff Frequencies for Filtering Linear Acceleration and Angular Velocity Signals Associated with Head Impacts Measured by Instrumented Mouthguards.

Head impact sensors worn in the mouth are popular because they couple directly to the teeth and provide six-degree-of-freedom head measurements. Mouthpiece signal filters have conventionally used cutoff frequencies lower than recommended practices (Society of Automotive Engineers, SAE J211-1) to eliminate extraneous noise when measuring with live subjects. However, there is little information about the effects of filter choice on the accuracy of signals measured by instrumented mouthpieces. Lack of standardization in head impact measurement device post-processing techniques can result in data that are not comparable across studies or device brands. This study sought optimal filter cutoff frequencies for six-degree-of-freedom measurements made at the teeth using instrumented mouthguards. We collected linear acceleration and angular velocity signals at the head center of gravity (CG) using laboratory-grade instrumentation. We also collected and filtered similar six-degree-of-freedom measurements from an instrumented mouthguard using 24 cutoff frequencies, from 25 to 600 Hz. We transformed the measurements to linear acceleration at the center of gravity of the head (CG) using all kinematic variables at the teeth, optimizing linear and angular mouthguard cutoff frequencies with one equation. We calculated the percent error in transformed peak resultant linear acceleration and minimized the mean and standard deviation in error. The optimal cutoff frequencies were 175 Hz for linear acceleration and 250 Hz for angular velocity. Rigid impacts (3-5 ms duration) had higher optimal cutoff frequencies (175 Hz linear acceleration, 275 Hz angular velocity) than padded impacts (10-12 ms duration; 100 Hz linear acceleration, 175 Hz angular velocity), and all impacts together (3-12 ms duration; 175 Hz linear acceleration, 250 Hz angular velocity). Instrumented mouthpiece manufacturers and researchers using these devices should consider these optimal filter cutoff frequencies to minimize measurement error. Sport-specific filter criteria for teeth-based sensors may be warranted to account for the difference in optimal cutoff frequency combination by impact duration.

The Influence of Headform Friction and Inertial Properties on Oblique Impact Helmet Testing.

Helmet-testing headforms replicate the human head impact response, allowing the assessment of helmet protection and injury risk. However, the industry uses three different headforms with varying inertial and friction properties making study comparisons difficult because these headforms have different inertial and friction properties that may affect their impact response. This study aimed to quantify the influence of headform coefficient of friction (COF) and inertial properties on oblique impact response. The static COF of each headform condition (EN960, Hybrid III, NOCSAE, Hybrid III with a skull cap, NOCSAE with a skull cap) was measured against the helmet lining material used in a KASK prototype helmet. Each headform condition was tested with the same helmet model at two speeds (4.8 & 7.3 m/s) and two primary orientations (y-axis and x-axis rotation) with 5 repetitions, totaling 100 tests. The influence of impact location, inertial properties, and friction on linear and rotational impact kinematics was investigated using a MANOVA, and type II sums of squares were used to determine how much variance in dependent variables friction and inertia accounted for. Our results show significant differences in impact response between headforms, with rotational head kinematics being more sensitive to differences in inertial rather than frictional properties. However, at high-speed impacts, linear head kinematics are more affected by changes in frictional properties rather than inertial properties. Helmet testing protocols should consider differences between headforms' inertial and frictional properties during interpretation. These results provide a framework for cross-comparative analysis between studies that use different headforms and headform modifiers.

Whitewater Helmet STAR: Evaluation of the Biomechanical Performance and Risk of Head Injury for Whitewater Helmets.

More than six million people participate in whitewater kayaking and rafting in the United States each year. Unfortunately, with these six million whitewater participants come 50 deaths annually, making it one of the highest fatality rates of all sports. As the popularity in whitewater activities grows, the number of injuries, including concussions, also increases. The objective of this study was to create a new rating system for whitewater helmets by evaluating the biomechanical performance and risk of head injury of whitewater helmets using the Summation of Tests for the Analysis of Risk (STAR) system. All watersport helmets that passed the EN: 1385: 2012 standard and that were clearly marketed for whitewater use were selected for this study. Two samples of each helmet model were tested on a custom pendulum impactor under conditions known to be associated with the highest risk of head injury and death. A 50th percentile male NOCSAE headform instrumented with three linear accelerometers and a triaxial angular rate sensor coupled with a Hybrid III 50th percentile neck were used for data collection. A total of 126 tests were performed using six different configurations. These included impacts to the front, side, and rear using two speeds of 3.1 and 4.9 m/s that modeled whitewater river flow rates. Each helmet's STAR score was calculated using the combination of exposure and injury risk that was determined from the linear and rotational head accelerations. The resulting head impact accelerations predicted a very high risk of concussion for all impact locations at the 4.9 m/s speed. The STAR score varied between helmets indicating that some helmets provide better protection than others. Overall, these results show a clear need for improvement in whitewater helmets, and the methodologies developed in this research project should provide manufacturers a design tool for improving these products.

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

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Ready for impact? A validity and feasibility study of instrumented mouthguards (iMGs).

OBJECTIVES: Assess the validity and feasibility of current instrumented mouthguards (iMGs) and associated systems. METHODS: Phase I; four iMG systems (Biocore-Football Research Inc (FRI), HitIQ, ORB, Prevent) were compared against dummy headform laboratory criterion standards (25, 50, 75, 100 g). Phase II; four iMG systems were evaluated for on-field validity of iMG-triggered events against video-verification to determine true-positives, false-positives and false-negatives (20±9 player matches per iMG). Phase III; four iMG systems were evaluated by 18 rugby players, for perceptions of fit, comfort and function. Phase IV; three iMG systems (Biocore-FRI, HitIQ, Prevent) were evaluated for practical feasibility (System Usability Scale (SUS)) by four practitioners. RESULTS: Phase I; total concordance correlation coefficients were 0.986, 0.965, 0.525 and 0.984 for Biocore-FRI, HitIQ, ORB and Prevent. Phase II; different on-field kinematics were observed between iMGs. Positive predictive values were 0.98, 0.90, 0.53 and 0.94 for Biocore-FRI, HitIQ, ORB and Prevent. Sensitivity values were 0.51, 0.40, 0.71 and 0.75 for Biocore-FRI, HitIQ, ORB and Prevent. Phase III; player perceptions of fit, comfort and function were 77%, 6/10, 55% for Biocore-FRI, 88%, 8/10, 61% for HitIQ, 65%, 5/10, 43% for ORB and 85%, 8/10, 67% for Prevent. Phase IV; SUS (preparation-management) was 51.3-50.6/100, 71.3-78.8/100 and 83.8-80.0/100 for Biocore-FRI, HitIQ and Prevent. CONCLUSION: This study shows differences between current iMG systems exist. Sporting organisations can use these findings when evaluating which iMG system is most appropriate to monitor head acceleration events in athletes, supporting player welfare initiatives related to concussion and head acceleration exposure.

Proposed Injury Threshold for Drone Blade Lacerations.

Drones have been increasing in popularity and are able to cause skin injuries ranging from minor abrasions to severe lacerations. The objective of this study was to determine the aspects of drone blades that cause injuries, and to help manufacturers design safer drones by suggesting an injury threshold. The blade tip thickness, blade length, angular velocity, and blade tip speed of a variety of popular drones were measured. The injury caused by each drone blade contacting a fetal bovine skin surrogate at different speeds was recorded. Blade tip speed had the highest correlation to injury severity, while blade tip thickness, blade length, and rpm had little to no correlation with the resulting injury. Blade tip speeds above 25 m/s resulted in minor abrasions, and speeds above 60 m/s resulted in minor lacerations. To prevent severe injuries, drone manufacturers should design drones with blade tip speeds below the threshold of 60 m/s.

Shear-deformation based continuum-damage constitutive modeling of brain tissue.

Traumatic brain injury (TBI) is a leading cause of death in the United States. Depending on the severity of injury, complications such as memory loss and emotional changes are common. While the exact mechanisms are still unclear, these cognitive deficiencies are thought to arise from microstructural damages to the brain tissue, such as in diffuse-axonal injury where neuron fibers are sheared. Constitutive models can predict such damage at a microstructural level and allow for insight into the mechanisms of injury initiating at lower length scales. In this study, we developed a continuum damage model of brain tissue that is validated by experimental quasi-static stress-strain tests in tension, compression, and shear. The present work shows that damage is most present in the shear stress state, making the tissue suitable for damage modeling via shear interaction terms. Using this model, new insights into microstructural breakdown due to shear stresses and strains can be gained by application to simulations.

Age and gender related differences in load-strain response in C57Bl/6 mice.

We examined the changes in mechanical strain response of male and female mouse tibia and ulna, using axial compression tests, to assess age-related changes in tibiae and ulnae by a non-contact strain measurement technique called the digital image correlation (DIC) and the standard strain gage. A unique aspect of the study was to compare bones from the same animal to study variations in behavior with aging. This study was conducted using male and female C57Bl/6 mice at 6, 12 and 22 months of age (N=6-7 per age and sex) using three load levels. The DIC technique was able to detect a greater number of statistically significant differences in comparison to the strain gaging method. Male ulna showed significantly higher DIC strains compared to strains captured from strain gage at all three levels of load at 6 months and in the lowest load at 12 months. DIC measurements revealed that the ulna becomes stiffer with aging for both males and females, which resulted in 0.4 to 0.8 times reduced strains in the 22-month group compared to the 6 month group. Male tibia showed three-fold increased strains in the 22 months group at 11.5 N load compared to 6 months group (p<.05).

A Two-Phased Approach to Quantifying Head Impact Sensor Accuracy: In-Laboratory and On-Field Assessments.

Measuring head impacts in sports can further our understanding of brain injury biomechanics and, hopefully, advance concussion diagnostics and prevention. Although there are many head impact sensors available, skepticism on their utility exists over concerns related to measurement error. Previous studies report mixed reliability in head impact sensor measurements, but there is no uniform approach to assessing accuracy, making comparisons between sensors and studies difficult. The objective of this paper is to introduce a two-phased approach to evaluating head impact sensor accuracy. The first phase consists of in-lab impact testing on a dummy headform at varying impact severities under loading conditions representative of each sensor's intended use. We quantify in-lab accuracy by calculating the concordance correlation coefficient (CCC) between a sensor's kinematic measurements and headform reference measurements. For sensors that performed reasonably well in the lab (CCC ≥ 0.80), we completed a second phase of evaluation on-field. Through video validation of impacts measured by sensors on athletes, we classified each sensor measurement as either true-positive and false-positive impact events and computed positive predictive value (PPV) to summarize real-world accuracy. Eight sensors were tested in phase one, but only four sensors were assessed in phase two. Sensor accuracy varied greatly. CCC from phase one ranged from 0.13 to 0.97, with an average value of 0.72. Overall, the four devices that were implemented on-field had PPV that ranged from 16.3 to 91.2%, with an average value of 60.8%. Performance in-lab was not always indicative of the device's performance on-field. The methods proposed in this paper aim to establish a comprehensive approach to the evaluation of sensors so that users can better interpret data collected from athletes.

Osteocyte lacunar strain determination using multiscale finite element analysis.

Osteocytes are thought to be the primary mechanosensory cells within bone, regulating both osteoclasts and osteoblasts to control load induced changes in bone resorption and formation. Osteocytes initiate intracellular responses including activating the Wnt/ß-catenin signaling pathway after experiencing mechanical forces. In response to changing mechanical loads (strain) the osteocytes signal to cells on the bone surface. However, this process of osteocyte activation appears heterogeneous since it occurs in sub-populations of osteocytes, even within regions predicted to be experiencing similar global strain magnitudes determined based on traditional finite element modeling approaches. Several studies have investigated the strain responses of osteocyte lacunae using finite element (FE) models, but many were limited by the use of idealized geometries (e.g., ellipsoids) and analysis of a single osteocyte. Finite element models by other groups included more details, such as canaliculi, but all were done on models consisting of a single osteocyte. We hypothesized that variation in size and orientation of the osteocyte lacunae within bone would give rise to micro heterogeneity in the strain fields that could better explain the observed patterns of osteocyte activation following load. The osteocytes in our microscale and nanoscale models have an idealized oval shape and some are based on confocal scans. However, all the FE models in this preliminary study consist of multiple osteocytes. The number of osteocytes in the 3D confocal scan models ranged from five to seventeen. In this study, a multi-scale computational approach was used to first create an osteocyte FE model at the microscale level to examine both the theoretical lacunar and perilacunar strain responses based on two parameters: 1) lacunar orientation and 2) lacunar size. A parametric analysis was performed by steadily increasing the perilacunar modulus (5, 10, 15, and 20 GPa). Secondly, a nanoscale FE model was built using known osteocyte dimensions to determine the predicted strains in the perilacunar matrix, fluid space, and cell body regions. Finally, 3-D lacunar models were created using confocal image stacks from mouse femurs to determine the theoretical strain in the lacunae represented by realistic geometries. Overall, lacunar strains decreased by 14% in the cell body, 15% in the fluid space region and 25% in the perilacunar space as the perilacunar modulus increased, indicating a stress shielding effect. Lacunar strains were lower for the osteocytes aligned along the loading axis compared to those aligned perpendicular to axis. Increases in lacuna size also led to increased lacunar strains. These finite element model findings suggest that orientation and lacunar size may contribute to the heterogeneous initial pattern of osteocyte strain response observed in bone following in vivo applied mechanical loads. A better understanding of how mechanical stimuli directly affect the lacunae and perilacunar tissue strains may ultimately lead to a better understanding of the process of osteocyte activation in response to mechanical loading.

Age-related and sex-specific effects on architectural properties and biomechanical response of the C57BL/6N mouse femur, tibia and ulna.

Aging is known to reduce bone quality and bone strength. We sought to determine how aging affects the biomechanical and architectural properties of various long bones, and if sex influences age related differences/changes. While researchers have extensively studied these changes in individual bones of mice, there is no comprehensive study of the changes in the bones from the same mice to study the changes with aging. We performed three point bending tests and microcomputed tomography (microCT) analysis on femurs, tibiae and ulnae. Three point bending tests were utilized to calculate biomechanical parameters and imaging was also performed using high resolution microCT to reveal both cortical and trabecular microarchitecture C57BL/6N mice were divided into three age groups: 6, 12 and 22 months. Each age and sex group consisted of 6-7 mice. The ultimate load to failure (UL), elastic stiffness (ES), modulus of elasticity (E) and the moment of inertia about bending axis (MOI) for each bone was calculated using three point bending test. MicroCT scans of all the bones were analyzed to determine cortical bone volume per tissue volume (C.BV/TV), trabecular bone volume per tissue volume (Tb.BV/TV), cortical bone area (B.Ar) using CTAn's microCT analysis and tested for correlation with the biomechanical parameters. Mean (standard error) values of UL in femur decreased from 19.8(0.6) N to 12.8(1.1) N (p < .01) and 17.9(0.6) N to 14.6(1.0) N (p = .02) from 6 to 22 months groups in males and females respectively. Similarly, UL in tibia decreased from 19.8(0.5) N to 14.3(0.2) N (p < .01) and 14.4(0.6) N to 9.5(1.0) N (p < .01) from 6 to 22 months group in males and females respectively. ES in femur decreased from 113.2(7) N/mm to 69.6(6.7) N/mm (p < .01) from 6 to 22 months in males only. ES in tibia decreased from 78.6(3.2) N/mm to 65.0(2.3) N/mm (p = .01) and 53.1(2.9) N/mm to 44.0(1.7) N/mm (p = .02) from 6 to 22 months in males and females respectively. Interestingly, ES in ulna increased from 8.2(0.8) N/mm to 10.9(1.0) N/mm (p = .051) from 6 to 22 months of age in females only. E in femur decreased from 4.0(0.4) GPa to 2.8(0.2) GPa (p = .01) and 6.7(0.5) GPa to 4.5(0.4) GPa (p = .01) from 6 to 22 months of age in males and females respectively while tibia showed no change. However, E in ulna increased from 7.0(0.8) GPa to 11.0(1.1) GPa (p = .01) from 6 to 22 months of age in females only. Changes in age and sex-related bone properties were more pronounced in the femur and tibia, while the ulna showed fewer overall differences. Most of the changes were observed in biomechanical compared to architectural properties and female bones are more severely affected by aging. In conclusion, our data demonstrate that care must be taken to describe bone site and sex-specific, rather than making broad generalizations when describing age-related changes on the biomechanical and architectural properties of the skeleton.

Quantifying Youth Football Helmet Performance: Assessing Linear and Rotational Head Acceleration.

Youth football helmet testing standards have served to largely eliminate catastrophic head injury from the sport. These standards, though, do not presently consider concussion and do not offer consumers the capacity to differentiate the impact performance of youth football helmets. This study adapted the previously developed Summation of Tests for the Analysis of Risk (STAR) equation for youth football helmet assessment. This adaptation made use of a youth-specific testing surrogate, on-field data collected from youth football players, and a concussion risk function developed for youth athletes. Each helmet is subjected to 48 laboratory impacts across 12 impact conditions. Peak linear head acceleration and peak rotational head acceleration values from each laboratory impact are aggregated into a single STAR value that combines player exposure and risk of concussion. This single value can provide consumers with valuable information regarding the relative performance of youth football helmets.

Compressive Mechanical Properties of Porcine Brain: Experimentation and Modeling of the Tissue Hydration Effects.

Designing protective systems for the human head-and, hence, the brain-requires understanding the brain's microstructural response to mechanical insults. We present the behavior of wet and dry porcine brain undergoing quasi-static and high strain rate mechanical deformations to unravel the effect of hydration on the brain's biomechanics. Here, native 'wet' brain samples contained ~80% (mass/mass) water content and 'dry' brain samples contained ~0% (mass/mass) water content. First, the wet brain incurred a large initial peak stress that was not exhibited by the dry brain. Second, stress levels for the dry brain were greater than the wet brain. Third, the dry brain stress-strain behavior was characteristic of ductile materials with a yield point and work hardening; however, the wet brain showed a typical concave inflection that is often manifested by polymers. Finally, finite element analysis (FEA) of the brain's high strain rate response for samples with various proportions of water and dry brain showed that water played a major role in the initial hardening trend. Therefore, hydration level plays a key role in brain tissue micromechanics, and the incorporation of this hydration effect on the brain's mechanical response in simulated injury scenarios or virtual human-centric protective headgear design is essential.

Development of a Methodology for Simulating Complex Head Impacts With the Advanced Combat Helmet.

Blunt impact assessment of the Advanced Combat Helmet (ACH) is currently based on the linear head response. The current study presents a methodology for testing the ACH under complex loading that generates linear and rotational head motion. Experiments were performed on a guided, free-fall drop tower using an instrumented National Operating Committee for Standards on Athletic Equipment (NOCSAE) head attached to a Hybrid III (HIII) or EuroSID-2 (ES-2) dummy neck and carriage. Rear and lateral impacts occurred at 3.0 m/s with peak linear accelerations (PLA) and peak rotational accelerations (PRA) measured at the NOCSAE head center-of-gravity. Experimental data served as inputs for the Simulated Injury Monitor (SIMon) computational model to estimate brain strain. Rear ACH impacts had 22% and 7% higher PLA and PRA when using the HIII neck versus the ES-2 neck. Lateral ACH impacts had 33% and 35% lower PLA and PRA when using HIII neck versus the ES-2 neck. Computational results showed that total estimated brain strain increased by 25% and 76% under rear and lateral ACH impacts when using the ES-2 neck. This methodology was developed to simulate complex ACH impacts involving the rotational head motion associated with diffuse brain injuries, including concussion, in military environments.

Comparison of NOCSAE head kinematics using the Hybrid III and EuroSID-2 necks.

Anthropomorphic test devices (ATDs) are designed for specific loading scenarios and possess uniquely designed individual components including the neck. The purpose of this study was to determine the influence of the neck surrogate on head kinematics. Inertial loads were generated using a pendulum system with an anthropomorphic head attached to a Hybrid III (HIII) or EuroSID-2 (ES-2) neck. The ATD head-neck assemblies were tested under extension, flexion, lateral bending, oblique extension, and oblique flexion at 3.4 m/s. Peak head kinematics were found to be statistically different with the ES-2 versus HIII neck under certain cases. For extension, the resultant peak linear acceleration (PLA) and resultant peak angular acceleration (PAA) were statistically higher with the ES-2 versus HIII neck. For flexion and lateral bending, there were no statistical differences in the resultant PLA based on neck selection although the resultant PAA was statistically higher with the ES-2 versus HIII neck. For oblique extension, the resultant PLA and PAA statistically increased with the ES-2 versus HIII neck. Furthermore, the acceleration components ax, αx, and αy were statistically higher with the ES-2 neck while ay showed no statistical difference due to neck selection. For oblique flexion, the resultant PLA and PAA were statistically higher with the ES-2 versus HIII neck. Additionally, the acceleration components ax, ay, αx, and αy were statistically higher with the ES-2 versus HIII neck. These findings indicate that for certain loading directions and acceleration components, head kinematics were influenced by the neck surrogate used.

Determination of Elastic Modulus in Mouse Bones Using a Nondestructive Micro-Indentation Technique Using Reference Point Indentation.

The determination of the elastic modulus of bone is important in studying the response of bone to loading and is determined using a destructive three-point bending method. Reference point indentation (RPI), with one cycle of indentation, offers a nondestructive alternative to determine the elastic modulus. While the elastic modulus could be determined using a nondestructive procedure for ex vivo experiments, for in vivo testing, the three-point bending technique may not be practical and hence RPI is viewed as a potential alternative and explored in this study. Using the RPI measurements, total indentation distance (TID), creep indentation distance, indentation force, and the unloading slope, we have developed a numerical analysis procedure using the Oliver-Pharr (O/P) method to estimate the indentation elastic modulus. Two methods were used to determine the area function: (1) Oliver-Pharr (O/P-based on a numerical procedure) and (2) geometric (based on the calculation of the projected area of indentation). The indentation moduli of polymethyl methacrylate (PMMA) calculated by the O/P (3.49-3.68 GPa) and geometric (3.33-3.49 GPa) methods were similar to values in literature (3.5-4 GPa). In a study using femurs from C57Bl/6 mice of different ages and genders, the three-point bending modulus was lower than the indentation modulus. In femurs from 4 to 5 months old TOPGAL mice, we found that the indentation modulus from the geometric (5.61 ± 1.25 GPa) and O/P (5.53 ± 1.27 GPa) methods was higher than the three-point bending modulus (5.28 ± 0.34 GPa). In females, the indentation modulus from the geometric (7.45 ± 0.86 GPa) and O/P (7.46 ± 0.92 GPa) methods was also higher than the three-point bending modulus (7.33 ± 1.13 GPa). We can conclude from this study that the RPI determined values are relatively close to three-point bending values.

Influence of ATD versus PMHS reference sensor inputs on computational brain response in frontal impacts to advanced combat helmet (ACH).

OBJECTIVE: This study analyzed the influence of reference sensor inputs from anthropomorphic test devices (ATDs) versus postmortem human subjects (PMHSs) on simulations of frontal blunt impacts to the advanced combat helmet (ACH). METHODS: A rigid-arm pendulum was used to generate frontal impacts to ACHs mounted on ATDs and PMHS. An appropriately sized ACH was selected according to standard fitting guidelines. The National Operating Committee on Standards for Athletic Equipment (NOCSAE) head was selected for ATD tests due to shape features that enabled a realistic helmet fit. A custom procedure was used to mount a reference sensor internally near the center of gravity (CG) of the PMHS. Reference sensor data from the head CG were used as inputs for the Simulated Injury Monitor (SIMon). Brain responses were assessed with the cumulative strain damage measure set at 10%, or CSDM(10). RESULTS: Compared to ATD tests, PMHS tests produced 18.7% higher peak linear accelerations and 5.2% higher peak angular velocities. Average times to peak for linear accelerations were relatively similar between ATDs (5.5 ms) and PMHSs (5.8 ms). However, times to peak for angular velocities were higher by a factor of up to 3.4 for PMHSs compared to ATDs. Values for were also higher by a factor of up to 13.1 when PMHS inputs were used for SIMon. CONCLUSIONS: The preliminary findings of this work indicate that small differences in ATD versus PMHS head kinematics could lead to large differences in strain-derived brain injury metrics such as CSDM.

Comparison of strain measurement in the mouse forearm using subject-specific finite element models, strain gaging, and digital image correlation.

Mechanical loading in bone leads to the activation of bone-forming pathways that are most likely associated with a minimum strain threshold being experienced by the osteocyte. To investigate the correlation between cellular response and mechanical stimuli, researchers must develop accurate ways to measure/compute strain both externally on the bone surface and internally at the osteocyte level. This study investigates the use of finite element (FE) models to compute bone surface strains on the mouse forearm. Strains from three FE models were compared to data collected experimentally through strain gaging and digital image correlation (DIC). Each FE model was assigned subject-specific bone properties and consisted of one-dimensional springs representing the interosseous membrane. After three-point bending was performed on the ulnae and radii, moment of inertia was determined from microCT analysis of the bone region between the supports and then used along with standard beam analyses to calculate the Young's modulus. Non-contact strain measurements from DIC were determined to be more suitable for validating numerical results than experimental data obtained through conventional strain gaging. When comparing strain responses in the three ulnae, we observed a 3-14% difference between numerical and DIC strains while the strain gage values were 37-56% lower than numerical values. This study demonstrates a computational approach for capturing bone surface strains in the mouse forearm. Ultimately, strains from these macroscale models can be used as inputs for microscale and nanoscale FE models designed to analyze strains directly in the osteocyte lacunae.

Non-contact strain measurement in the mouse forearm loading model using digital image correlation (DIC).

This study investigates the use of a non-contact method known as digital image correlation (DIC) to measure strains in the mouse forearm during axial compressive loading. A two camera system was adapted to analyze the medial and lateral forearm displacements simultaneously, and the derived DIC strain measurements were compared to strain gage readings from both the ulna and radius. Factors such as region-of-interest (ROI) location, lens magnification, noise, and out-of-plane motion were examined to determine their influence on the DIC strain measurements. We confirmed that our DIC system can differentiate ROI locations since it detected higher average strains in the ulna compared to the radius and detected compressive strains on medial bone surfaces vs. tensile strains on lateral bone surfaces. Interestingly, the DIC method also captured heterogeneity in surface strain fields which are not detectable by strain gage based methods. A separate analysis of the noise intrinsic to the DIC system also revealed that the noise constituted less than 4.5% of all DIC strain measurements. Furthermore, finite element (FE) simulations of the forearm showed that out-of-plane motion was not a significant factor that influenced DIC measurements. Finally, we observed that average DIC strain measurements can be up to 1.5-2 times greater than average strain gage readings on the medial bone surfaces. These findings suggest that strain experienced in the mouse forearm model by loading is better captured through DIC as opposed to strain gages, which as a result of being glued to the bone surface artificially stiffen the bone and lead to an underestimation of the strain response.