Name: a test bed to examine helmet fit and retention and biomechanical measures of head and neck injury in simulated impact
Uploaded: 2017-09-21T15:00:00.000+00:00
Duration: 7 min 30 s
Description: Watch this Scientific Journal Video about A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact at JoVE.com

We gratefully acknowledge funding from the Natural Science and Engineering Research Council (NSERC) of Canada (Discovery Grants 435921), the Pashby Sport Safety Fund (2016: RES0028760), the Banting Research Foundation (Discovery Award 31214), NBEC Inc. (Canada), and the Faculty of Engineering and Department of Mechanical Engineering at the University of Alberta.

1. Helmet Fit Scenarios Arrangement
<ol>
	<li>Define fit scenarios to be studied on an anthropometric test device head and neck (Hybrid III 50th percentile male) with a head circumference of 575 mm. 
	NOTE: An example of four fit scenarios is shown in Table 1 with helmet positions corresponding to Figure 1. The forward and backward fit scenarios were based on definitions of correct helmet use from previous epidemiological studies, which specified proper helmet position as not covering the eyebrows or exposing the forehead3.</li>
	<li>For each scenario, mark each helmet position on the headform to ensure the helmet fit scenario is consistently repeated.</li>
	<li>Use a CPSC certified helmet, available in universal and extra-large sizes, for all fit scenarios. 
	NOTE: According to the manufacturer provided fit guide, a universal size most appropriately fits the headform circumference.
	<ol>
		<li>For each fit scenario, keep other fit parameters consistent. Specifically, tighten the chinstrap to leave approximately one finger width of space under the chin and hand-tighten the adjustable dial to maintain a secure fit.</li>
	</ol>
	</li>
</ol>
2. Fit Force Measurement
<ol>
	<li>Arrange five fit sensors on the skin of the headform, positioned on the front, back, left, right and top (Figure 2). 
	NOTE: The sensors are a modified version of Bragg grating force transducers developed within the research group19,20,21,22, optimized to measure fit forces over a range of 0 to 50 N. The modified sensors have a thickness and diameter of 2.6 mm and 14 mm respectively.</li>
	<li>Take a reference measurement with the transducers on the un-helmeted headform under no load. Take this reference measurement prior to each fit force measurement.</li>
	<li>Place the helmet onto the headform and measure force data for 3 s at a rate of 2.5 kHz. Repeat the same fit scenario six times for repeated measurements.</li>
	<li>Repeat the same measurement procedure for all fit scenarios.</li>
	<li>Convert wavelength shift data to force measurements by multiplying the measured wavelengths from the transducer by the pre-determined calibration constant for the fit force transducer.</li>
</ol>
3. Drop Tower for Impact Simulation
<ol>
	<li>Simulate impact to the helmeted head by linearly guiding the headform to hit an impact surface19,23. The equipment required to do this is context specific, as detailed below.
	<ol>
		<li>Assemble a drop tower to consist of an adjustable drop gimbal, an anthropometric test device head and neck, and a variable impact surface. 
		NOTE: The total drop assembly mass is approximately 11 kg. The added mass of the gimbal accounts for the exclusion of the full human body as an effective torso mass to better simulate a realistic impact24.</li>
		<li>Arrange 9 uni-axial accelerometers in a 3-2-2-2 configuration within the headform to allow linear and angular accelerations of the headform to be determined at the center of gravity25.</li>
		<li>Arrange a purpose built velocity gate on the impact tower to measure impact velocity immediately before impact.</li>
	</ol>
	</li>
	<li>Collect head acceleration and neck force/moment data using the data acquisition system. Filter analog voltages, sampled at 100 kHz for all channels. Prior to the data acquisition system, include a hardware anti-aliasing low pass filter with a corner frequency of 4 kHz26.</li>
	<li>Arrange the impact scenario.
	<ol>
		<li>For all impacts, remove the helmet visor to allow for better visibility during motion tracking. The effect of the visor during impact is assumed to be negligible due to its loose attachment.</li>
		<li>Arrange all drops to impact the forehead. This is a common impact location in cycling27, although other scenarios could also be simulated.</li>
		<li>Simulate six different impact scenarios by varying impact speed, impact surface, and either head-first or torso-first impacts as per Table 2.</li>
		<li>Raise the headform to the appropriate height, corresponding to specified impact velocities. Drop the headform from an appropriate height, typically 0.82 m and 1.83 m, to achieve velocities of 4 m/s and 6 m/s, respectively. 
		NOTE: Add height as necessary to overcome friction losses. Two impact velocities of 4 m/s and 6 m/s can be chosen based off previous literature and standards28.</li>
		<li>Arrange the impact surface.
		<ol>
			<li>Arrange either a flat or a 45&#176; angled anvil (Figure 4). The flat anvil simulates falls on a flat surface, while the angled anvil simulates impacts with a tangential velocity component.</li>
			<li>Cover both of the surfaces of the anvils in abrasive tape to simulate an asphalt surface. Adjust the anvil position as necessary between impacts to ensure the helmet to be impacted contacts only the flat surface of the anvil.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Arrange the drop tower for either head-first or torso-first impact. Simulate both head-first and torso-first impacts, with torso impacts similar to the combined loading impact configuration presented in Smith et al.18
	<ol>
		<li>To simulate a head-first impact, do not adjust the drop tower.</li>
		<li>To simulate the torso hitting the ground before the head, place a wooden block in the path of the drop gimbal. Place this wooden block at a height such that the head is approximately 25 mm away from impacting the anvil at the torso-impact. The head will then continue to hit the anvil by means of neck flexion only.</li>
		<li>Include a layer of foam to minimize vibrations from the drop tower (Figure 5).</li>
		<li>In contrast to head-first impacts, adjust the angle of the neck in torso-first impacts. 
		NOTE: This neck angle adjustment allows for the head to impact the anvil on the forehead after flexion, so that impact location is comparable to the head-first impact case (Figure 6). In addition to forehead impacts, this torso-first scenario would certainly be relevant in side impacts as well. In both head-first and torso-first impacts, this gimbal system allows for movement of the head and neck along the track after impact.</li>
	</ol>
	</li>
	<li>Trigger the data acquisition system, high speed cameras (see section 4), and drop of the headform simultaneously. Repeat the same impact and fit scenario configuration 3 times with new helmets each time. 
	NOTE: The high-speed cameras will need to be set up concurrently with the drop tower, detailed in section 4.</li>
	<li>Subject each of the four fit scenarios to each of the 6 different impact scenarios. Perform a total of 72 drops after 3 trials of each configuration.</li>
	<li>Post-process the headform kinematic and kinetic data.
	<ol>
		<li>Filter analog signals for acceleration and force/moment subsequently using a 4th order Butterworth filter in post processing to meet industry suggested practice26. Filter head accelerations and neck forces as per Channel Frequency Class (CFC) 1000. Filter neck moments as per CFC 600.</li>
	</ol>
	</li>
</ol>
4. Motion Capture Using a High Speed Dual Camera System
NOTE: Recording marker positions from two high speed cameras allow three-dimensional marker positions to be determined with the DLT method16 in post-processing. To determine head-helmet displacements, track markers on both the headform and helmet during impact.
<ol>
	<li>Arrange high-speed cameras around the drop tower.
	<ol>
		<li>Arrange two high-speed cameras around the drop tower to capture synchronized images of the helmet and headform movement during impact.
		<ol>
			<li>Place a master camera to the side of the drop tower and place a slave camera at approximately 45&#176; from the master (Figure 7). Setup a 250 W light between the cameras to allow for sufficient exposure.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Configure high speed cameras.
	<ol>
		<li>Equip each camera with either a 50 mm f/1.4 or 100 mm f/2.0 macro lens, depending on the field of view required. Set the apertures on the lenses at f/8.0. 
		NOTE: This aperture allows for sufficiently sharp focus in the desired depth of field. The required field of view ranged from 30-60 cm, depending on the impact scenario.</li>
		<li>Configure both cameras to record at 1280 x 800 pixels at a frame rate of 1000 frames per second or faster. Thus, the maximum exposure time per frame will be 600 &#181;s.</li>
		<li>Synchronize the two cameras in frames and internal clock. Set up a trigger so that both cameras trigger simultaneously.</li>
	</ol>
	</li>
	<li>Calibrate the space by taking a still image of a calibration frame from each camera. 
	NOTE: For the direct linear transformation (DLT) method, the space must be initially calibrated.
	<ol>
		<li>Move a calibration cage with 17 known calibration point locations into the field of view of both cameras and take a single image from each camera. A minimum of 11 common points must be visible from both cameras.</li>
		<li>Find the two-dimensional coordinates of each marker with tracking software. 
		NOTE: A coordinate measuring machine (CMM) determines the point locations of the calibration cage prior to DLT calibration.</li>
		<li>Using a series of calculations performed with the calibration markers&#39; coordinates (known as DLT)16, transform any two dimensional marker locations into three-dimensional coordinates relative to the calibration cage coordinate system in post-processing.</li>
	</ol>
	</li>
	<li>To quantify helmet displacement, track the distance between a point on the headform forehead and the brim of the helmet using the tracking software. 
	NOTE: Because these points are not visible from both cameras, track a set of three visible markers on each the headform and helmet instead. The points on the forehead and helmet can then be indirectly tracked.</li>
	<li>Place motion tracking markers on the headform and take a still reference image of the headform from each camera.
	<ol>
		<li>For this method of indirect marker tracking, take a headform reference image with each camera. Ensure that this reference image consists of three markers and a reference marker defined on the head.</li>
		<li>Maximize the distance between markers using three reference point locations while remaining in both cameras&#39; field of views. 
		NOTE: Maximizing the distance allows for better accuracy by decreasing indirect marker tracking sensitivity to tracking errors. The three markers allow for the three-dimensional reconstruction of motion in post processing, as well as the estimation of the forehead location.</li>
		<li>Hold the reference marker between the eyes on the lower forehead and the other markers spread across the headform. Ensure that these three other markers are visible from both cameras throughout an impact (Figure 8).</li>
	</ol>
	</li>
	<li>Place motion tracking markers on the helmet and take still reference images of the helmet from each camera as described for the headform reference (section 4.5).
	<ol>
		<li>Ensure that the reference consists of viewing at least four motion tracking markers. Hold one marker on the bottom of the helmet brim as a reference and spread the other three markers on the helmet. Ensure that these three markers are visible from both cameras throughout an impact. Take a single image from each camera for the helmet reference (Figure 9).</li>
	</ol>
	</li>
	<li>Trigger the data acquisition system, high speed cameras, and drop of the headform simultaneously as described in section 3. 
	NOTE: The drop tower will need to be set up concurrently with the high-speed cameras. After taking reference images, a drop may be performed.
	<ol>
		<li>Arrange the helmet fit scenario. Record the drop. Signal a trigger to the cameras manually upon impact. Arrange recording so that 3 s is recorded prior to the trigger and 8 s is recorded after the trigger. Manually review and bracket the synchronized camera images to contain the impact only.</li>
	</ol>
	</li>
</ol>
5. Head-helmet Marker Tracking and Post-processing
<ol>
	<li>Track head and helmet markers throughout the impact, using camera-specific software.
	<ol>
		<li>Track six points per drop: three on both the helmet and headform (Figure 10). With the software, determine the transient two-dimensional pixel coordinates of each marker.</li>
	</ol>
	</li>
	<li>Use the DLT method to calculate three-dimensional coordinates of tracked markers during a drop. 
	NOTE: With the calibration data from the calibration cage and the drop data from the two cameras, the DLT method can determine the three-dimensional coordinates of the tracked markers during a drop.</li>
	<li>Use the SVD (singular value decomposition) method17 to calculate the 3-D dimensional coordinates of the headform forehead and helmet brim. The difference between these two points is head-helmet displacement.
	<ol>
		<li>Use the SVD method to estimate the location of a reference point on each the headform forehead and helmet brim from the tracked markers.</li>
		<li>Use the SVD method to find the transformation matrix of the three markers between the reference frame and each individual frame of a drop. This transformation can be applied to find either the forehead or helmet brim locations.</li>
	</ol>
	</li>
	<li>Perform this indirect tracking on both the helmet and headform. The displacement between the forehead and helmet brim can then be monitored (Figure 11).</li>
</ol>

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M., Rowson, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+analysis+of+helmeted+impact+response+of+Hybrid+III+and+National+Operating+Committee+on+Standards+for+Athletic+Equipment+headforms.">Comparative analysis of helmeted impact response of Hybrid III and National Operating Committee on Standards for Athletic Equipment headforms.</a> Proc. Inst. Mech. Eng. Part P J. Sports Eng. Technol. 230 (1), 50-60 (2016).</li><li>de Jager, M., Sauren, A., Thunnissen, J., Wismans, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+and+a+Detailed+Mathematical+Model+for+Head-Neck+Dynamics.">Global and a Detailed Mathematical Model for Head-Neck Dynamics.</a> Proc. Stapp Car Crash Conf. 40, 269-281 (1996).</li><li>Aare, M., Halldin, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+New+Laboratory+Rig+for+Evaluating+Helmets+Subject+to+Oblique+Impacts.">A New Laboratory Rig for Evaluating Helmets Subject to Oblique Impacts.</a> Traffic Inj. Prev. 4 (3), 240-248 (2003).</li></ol>

The authors have no conflicts to disclose and do not stand to gain financially from the publication of this work.

Here, methods for investigating helmet fit in simulated helmeted head impacts are presented. Helmet fit was quantified with fit force sensors, impacts were simulated with an ATD headform and neck on a guided drop tower, and helmet movement was tracked with high speed video. Different impact scenarios were simulated under different fit scenarios to investigate the effects on biomechanical measures of helmet fit.
The helmet fit sensors are capable of distinguishing differences in fit forces between different helmet fit scenarios (Figure 12). Trends in fit forces between different fit scenarios do not strongly correlate with helmet performance. A helmet fit with poor stability (e.g. backward fit, as shown in Figure 1) is expected to exhibit significantly lower fit forces. Despite increased amounts of helmet movement (backward fit, Figure 19), a backward fit helmet exhibits significantly lower fit forces at only one sensor location when compared to a regular fit. This result suggests that helmet tightness on the head may not be the sole determinant of fit that guarantees dynamic stability of the helmet on the head. In this study, the fit forces were measured with the head inverted. The forces could have also been measured with the head in a right side up position, which would result in higher measured forces at the head vertex than reported in this study. However, the protocol of comparing fit forces between different fit scenarios seeks to quantify relative changes in fit force. Regardless of whether the head is upright or inverted, the relative changes in forces are the same.
The test bed and presented methods are capable of determining linear and angular kinematics including acceleration and velocity as well as neck forces and moments over the impact duration. Contemporary biomechanical injury measures are based on impact kinematics and time duration. For example, the head injury criterion (HIC) integrates linear acceleration over time12, while the brain injury criterion (BrIC) is based on peak angular velocity11. Other kinematic-based injury measures include the generalized acceleration model for brain injury threshold (GAMBIT)29, based on peak linear acceleration and peak angular acceleration, and the head impact power (HIP), which includes linear and angular acceleration, time duration, and directional considerations30. Alternatively, neck forces and moments are used to compute neck injury criterion Nij12. As this experimental protocol is capable of measuring all relevant kinematics and kinetics, it is possible to compute any biomechanical injury measures that are of interest. Potential injury risk can then be determined based on the literature associated with each injury measure. As a result, the setup proved capable of detecting alterations in biomechanical measures of head and neck injury based on helmet fit. Therefore, the test bed can be used to study fit and retention and their relation to focal and diffuse head injury and osteoligamentous neck injury. For example, in a torso-first impact onto a flat anvil at 6 m/s, a regular fit and backward fit scenario were compared. For the regular fit scenario, peak resultant linear accelerations, peak angular accelerations, and change in angular velocities were 158.2 g, 4647.5 rad/s2, and 22.39 rad/s respectively. Compared to the regular fit, a backward fit scenario exhibited higher values of 177.9 g, 6246.4 rad/s2 and 45.91 rad/s, suggesting a higher risk of head injury (Figure 13 through Figure 17) with t-test p-values of 0.012, 0.070, and 0.005, respectively. Because integration of noise in angular acceleration created an offset in angular velocity, the change in angular velocity is reported instead to account for this offset. For the same impact scenario, the Neck Injury Criterion (Nij) was determined from neck force and moment. For a regular helmet fit scenario, a peak Nij of 1.23 was determined, while a backward helmet fit measured 1.28 (Figure 18) with a t-test p-value of 0.099. Again, a higher value of Nij would suggest a greater risk of neck injury.
The high speed video analysis techniques proved capable of detecting alterations in dynamic stability and retention. For the same torso-first impact onto a flat anvil at 6 m/s, a regular fit and backward fit scenario were compared in terms of helmet displacements. The regular fit scenario experienced a maximum change in head-helmet displacement of 6.52 cm while the backward fit scenario experienced 12.18 cm (Figure 19) with a t-test p-value of 0.006. With nearly twice as much helmet movement, these trends suggest that a backward fit scenario results in increased head exposure and, perhaps, greater exposure to forehead injury in a subsequent impact following the first.
Absolute displacement and relative displacement (Figure 19) convey the amount of facial and forehead exposure and head-helmet relative motion, respectively, both of which are important when examining retention and dynamic stability. The proposed method of tracking helmet displacements relative to the head allows head exposure and helmet stability during impact to be represented and can evaluate helmet retention for subsequent impacts. The method can show helmet movement throughout an impact, which can be characterized as absolute displacements and changes in displacement (Figure 19). A poorly retained helmet would exhibit greater displacements, while a well-retained helmet would exhibit lesser displacements. In this study, absolute displacement indicates the amount of facial exposure and relative change in displacement indicates the maximum relative motion between the brow and helmet brim (Figure 19). This reported displacement value is determined from the distance between two markers, connected by a single axis. Using the same experimental methods, it would also be possible to measure relative displacement in three component directions to more thoroughly characterize fit and retention. A single component was chosen for simplicity, as well as providing a good comparison to HPI. In other impact conditions, such as side impacts, more component directions or head-helmet rotation could be particularly valuable.
A drawback with the currently proposed sensors and fit force measurement is the limited spatial resolution with which forces are measured. With a 5-sensor array, the distribution of force across the entire helmet may not be fully represented. Because the design of bicycle helmets often includes open vents, a sensor may not always contact the helmet and measure zero force as a result. One potential solution is to place the force sensors on the helmet instead of the head. In the presented protocol, the force sensors were placed on the head to maintain consistency and repeatability of the experiment. Having sensors placed on the helmet could require a different protocol for different helmet types. However, the small size of the sensors and multiplexing ability of Fiber Bragg Grating (FBG) sensors allow a greater number of sensors to be feasibly distributed around the head. Additional sensors could discern the locations of high and low fit force fluctuations and provide further insight on helmet stability. In addition to the magnitude of force in representing tightness, it may also be valuable to consider contact area between the helmet and head. Especially in the case of helmets with open vents, the total contact area or its distribution may be important for characterizing fit. Although changes in overall average tightness were not as apparent in different scenarios of helmet positioning, significant changes in the distribution of forces could be identified, as seen in Figure 12.
As with all biomechanical work based on ATDs, there are limitations in the presented methods. Unlike real world impacts, parameters such as impact speed, impact location on the helmet, and impact surfaces are controlled. Therefore, the work presented will not capture the variability of these parameters from cyclist to cyclist and from incident to incident leading to head impact.
The Hybrid III was developed for automotive crash testing, as opposed to helmet research. Unlike a National Operating Committee on Standards for Athletic Equipment (NOCSAE) headform31, it was not designed for use with a helmet. In contrast, the NOCSAE headform was designed with size and shape specifications based on cadaver heads for an average adult football player and some consider it to more accurately approximate head anthropometry. Because headform geometry has a significant role in studying helmet fit, the headform may have certain shortcomings for different helmet types. In particular, the headform has notable geometrical differences to the NOCSAE head in the base of the skull, cheeks, jaw, and chin32,33. Because there is minimal contact between these features and bicycle helmets, shape differences between the headform and an actual head may have minimal influence on head-helmet interaction. Therefore, we argue that the headform is an appropriate model to use in comparative studies between fit scenarios, like that presented here. Any influence due to shape differences would be most apparent in the interface between the retention ratchet system and the bottom ledge of the skull cap, particularly in the backward fit scenario. Related to the headform head, the neck has been criticized for its greater stiffness compared to a human neck, and some hypothesize that the lack of realistic stiffness can contribute to head motions that differ from those of a real human suffering head impact34. These effects would be considerably more significant in the torso-first impacts because the trajectory and kinematics of the head are dependent on the neck. For a torso-first impact, an overly stiff neck could attenuate the head&#39;s motion after the torso contact and unrealistically slow the head&#39;s impact velocity at head contact. With limited existing literature investigating torso-first impacts, the biofidelity of the kinematic traces are difficult to validate with real-world cyclists impacts. However, head angular acceleration from the torso traces are comparable to similar combined loading scenarios performed by Smith et al.18. As such, the trends in angular acceleration and neck load in different fit scenarios should be emphasized, rather than reported absolute magnitudes. We feel the neck is an appropriate model for the presented study because we compare neck kinetics and head motions between cases of fit and, instead of commenting on absolute magnitudes of head kinematics and neck kinetics, we note changes in these measures.
Another limitation of using the headform in studying helmet fit is the dissimilarity of the headform vinyl skin with that of a human scalp. With practical variations such as hair, oil, and moisture, an accurate simulation of all these variables would be difficult. Although efforts in creating an artificial scalp for helmet research have been pursued35, validations of head helmet interaction between artificial and human scalps have been minimal. Since it is generally accepted that the headform skin exhibits a higher coefficient of friction than a human scalp, helmet retention could be misleadingly improved. With varying dependence on head-helmet friction in different fit scenarios, the effect of the headform vinyl skin could also be more or less pronounced. For instance, a normal fit scenario may retain a helmet due to head shape while a forward fit may retain a helmet due to the increased head-helmet friction of the vinyl skin. However, the helmet displacements are dependent on the headform scalp in this study. As such, findings should be based on changes and trends between different fit scenarios.
Though four fit scenarios were investigated, more variables exist in characterizing helmet fit. These proposed methods could allow for the study of other helmet fit scenarios, such as more helmet sizes or different levels of ratchet retention tightness. In this study, the ratchet retention system was tightened to a consistent level of tightness, subjective to the researcher. A more realistic tightness could be achieved by measuring the fit forces on volunteers, similar to Jadischke&#39;s helmet fitment study5. The retention system could then be arranged on the headform and tightened to a level exhibiting the same fit forces. In future work, fit scenarios with different helmet sizes or ratchet retention tightness will be considered.
We present a novel test bed for evaluating helmet fit, dynamic retention, and the effects of both on biomechanical measures of head and neck injury. The presented methods are capable of detecting significant alterations in fit forces, relative head helmet motion, and all the contemporary biomechanical measures of head and neck injury. The proposed methods were used to investigate a regular and backward fit, finding significant changes in head angular velocity and the amount of head exposure. With these proposed methods, distinct differences in helmet performance due to helmet fit can be revealed.

Most epidemiological evidence suggests bicycle helmets provide protection against head injuries for cyclists of all ages1. The biomechanical literature presents the consistent theme that the helmeted head sustains relatively less severe head/brain injuries secondary to impact, relative to the unprotected (un-helmeted) head2. Some research suggests that poor helmet fit is associated with an increased risk of head injury3, implying that helmets are most effective when fit properly. Depending on the criteria used to define good helmet fit, incorrect helmet use was found to be as high as 64% among helmeted cyclists3. Despite epidemiological evidence suggesting that helmet fit is relevant in the severity or likelihood of head injury in an impact, there is minimal experimental work assessing in a controlled laboratory setting whether or not correct helmet fit or helmet retention has a significant effect on biomechanical measures of injury. One related study investigates the effect of motorcycle helmet sizing during helmeted impacts simulated with a finite element model4. Another related study investigates the effect of helmet sizing during experimental impacts5 while using pressure sensitive film to quantify fit forces in football helmets. The effect of retention systems in bicycle and motorcycle helmet impacts have been investigated6,7, as well as a backward fit scenario for preadolescents6.
Our work proposes methods to study the effect of bicycle helmet fit on the risk of injury with helmet fit force sensors, simulated impacts with an anthropometric head and neck, and stereoscopic high-speed cameras. The goals of our proposed methods are to quantify fit and evaluate the risk of injury in different realistic impact scenarios. In contrast to related methods, our work investigates bicycle helmet fit, where proper helmet use is varied. Similar to previous methods, head kinematics are determined; however, neck loading and head-helmet displacements are also quantified. Although the epidemiology of neck injury in cycling suggests that neck injuries are uncommon, they tend to be associated with more severe head impacts and hospitalization8,9. The evidence is mixed on whether or not helmet use reduces rates of neck injury8 and none of the cited epidemiological studies quantify aspects of helmet fit. Considering the fact that neck injury in cycling tends to be associated with more severe accidents and that helmet fit has not been examined in neck injury epidemiology, methods for examining both head and neck injury are valuable in biomechanical research. Such experimental methods could be used in biomechanical studies that complement epidemiological studies which cannot in all cases control for impact severity or helmet fit.
In our work, a novel method of monitoring relative motions between the head and helmet during impact has been developed. The ability to monitor whether or not the helmet moves on the head can give valuable insight into both helmet stability and exposure of the unprotected head to injury during impact. In a study investigating helmet fit, helmet stability and head exposure are particularly valuable in evaluating helmet performance. In contrast to related work, different impact and fit scenarios emphasizing varied helmet positioning will also be tested.
Currently, correct helmet fit is subjective and nonspecifically defined. Generally, good helmet fit is characterized by stability and position. The helmet should be resistant to movement once secured on the head, and should be positioned such that the eyebrows are not covered and the forehead is not excessively exposed. Furthermore, approximately one-finger width of space should fit between the chin and chinstrap3. Measures of quantifying helmet fit are not widespread; other than force, methods may compare helmet fit based on comparing head and helmet geometry. One such method is the Helmet Fit Index proposed by Ellena et al.10. Our proposed method of quantifying helmet fit, fit force sensors, creates an objective means of comparing different helmet fit scenarios in the form of average and standard deviation of forces exerted on the head. These fit force values represent the tightness of a helmet, as well as the variation of tightness experienced on the head. These sensors provide a quantified comparison of forces that can be made between different fit scenarios. A secure tight fitting helmet would show higher forces while a loose helmet would show lower forces. This method of fit force measurement is similar to the Average Fit Index proposed by Jadischke5. However, Jadischke&#39;s methods utilize pressure sensitive film. The optical sensors we present allow unobtrusive measurement of fit force around the head or helmet.
For certification of helmets, a helmet is secured on an instrumented headform, which is then raised to a certain height to be dropped. The head and helmet is then subject to a free fall drop onto an anvil while recording linear accelerations. Although not typically used in helmet industry standards, a Hybrid III head (headform) and neck assembly were used in this work, with a guided drop tower to simulate impacts. In contrast to standards that typically use linear kinematics, the headform accelerometer array also allows the determination of rotational kinematics, a key parameter in predicting the likelihood of diffuse brain injuries, including concussion11. Through measurement of both linear acceleration and rotational acceleration and velocity, estimates of severe focal and diffuse head injury can be made by comparing kinematics to the several proposed kinematics-based injury assessment methods in the literature12,13. While the headform was originally developed for automotive crash testing, its use in helmet assessment and estimation of head injury risk in helmeted impact is well documented2,14. The impact simulation setup also includes an upper neck load cell, allowing the forces and moments associated with neck injury to be measured. Neck injury risk can then be estimated by comparing neck kinetics to injury assessment data from automotive injury data12,13.
A method of tracking helmet movement relative to the head during impact with high speed video is also proposed. Currently, no quantitative methods exist to evaluate helmet stability during impact. The Consumer Product Safety Commission (CPSC)15 bicycle helmet standard calls for a positional stability test, but is not representative of an impact. Furthermore, whether or not the helmet comes off the headform is the only result measured by the test. Regardless of exposure of the head to injury, a helmet may still pass as long as it stays on the headform during tests. The proposed method of tracking helmet movement is similar to Helmet Position Index (HPI)15 and measures the distance between the brim of a helmet and the forehead. This head-helmet displacement is tracked using high-speed video footage throughout an impact in order to obtain a representation of helmet stability and head exposure during impact. Using Direct Linear Transform (DLT)16 and Single Value Decomposition (SVD)17 methods, markers are tracked from two cameras to determine point locations in three-dimensional space and then the relative displacement between helmet and head.
Several impact severity and fit parameters are investigated. The impact scenarios include two impact speeds, two impacting anvil surfaces, and both torso-first and head-first impacts. In addition to a typical flat anvil surface, an angled anvil impact is also simulated to induce a tangential force component. A torso-first impact, as opposed to a head-first impact, is included to simulate a scenario in which a rider&#39;s shoulder impacts the ground before the head, similarly performed in previous work18. Finally, these four helmet fit scenarios are investigated: a regular fit, an oversized fit, a forward fit, and a backward fit. Unlike previous work, helmet positioning on the head is an investigated parameter, as well as helmet fit and helmet sizing.

<table><tbody><tr><td>Hybrid III Headform</td><td>Humanetics or Jasti-Utama</td><td>N/A</td><td>50th Percentile ATD, for impact simulation</td></tr><tr><td>Hybrid III Neck</td><td>Humanetics or Jasti-Utama</td><td>N/A</td><td>50th Percentile ATD, for impact simulation</td></tr><tr><td>Linear Accelerometers</td><td>Measurement Specialties</td><td>64C-2000-360</td><td>for head acceleration measurement</td></tr><tr><td>Upper Neck Load Cell</td><td>mg Sensor</td><td>N6ALB11A</td><td>for neck load measurement</td></tr><tr><td>High Speed Camera</td><td>Vision Research</td><td>v611</td><td>for motion capture</td></tr><tr><td>Camera Lens</td><td>Carl Zeiss</td><td>N/A</td><td>50 mm f1/.4, for motion capture</td></tr><tr><td>Camera Lens</td><td>Carl Zeiss</td><td>N/A</td><td>100 mm f/2.0, for motion capture</td></tr><tr><td>Bicycle Helmet</td><td>Bell</td><td>N/A</td><td>Traverse</td></tr><tr><td>Data Acquisition System</td><td>National Instruments</td><td>PXI 6251</td><td>for Hybrid III signal acquisition</td></tr><tr><td>Head Impact Drop Tower</td><td>University of Alberta</td><td>N/A</td><td>Custom-designed, for impact simulation</td></tr><tr><td>Optical Interrogator</td><td>Smart Fibres Ltd.</td><td>N/A</td><td>SmartScan, for optical sensor force measurement</td></tr><tr><td>Fit Force Sensor</td><td>University of Alberta</td><td>N/A</td><td>Custom-designed, for measuring helmet fit forces</td></tr></tbody></table>

a test bed to examine helmet fit and retention and biomechanical measures of head and neck injury in simulated impact

Conventional wisdom and the language in international helmet testing and certification standards suggest that appropriate helmet fit and retention during an impact are important factors in protecting the helmet wearer from impact-induced injury. This manuscript aims to investigate impact-induced injury mechanisms in different helmet fit scenarios through analysis of simulated helmeted impacts with an anthropometric test device (ATD), an array of headform acceleration transducers and neck force/moment transducers, a dual high speed camera system, and helmet-fit force sensors developed in our research group based on Bragg gratings in optical fiber. To simulate impacts, an instrumented headform and flexible neck fall along a linear guide rail onto an anvil. The test bed allows simulation of head impact at speeds up to 8.3 m/s, onto impact surfaces that are both flat and angled. The headform is fit with a crash helmet and several fit scenarios can be simulated by making context specific adjustments to the helmet position index and/or helmet size. To quantify helmet retention, the movement of the helmet on the head is quantified using post-hoc image analysis. To quantify head and neck injury potential, biomechanical measures based on headform acceleration and neck force/moment are measured. These biomechanical measures, through comparison with established human tolerance curves, can estimate the risk of severe life threatening and/or mild diffuse brain injury and osteoligamentous neck injury. To our knowledge, the presented test-bed is the first developed specifically to assess biomechanical effects on head and neck injury relative to helmet fit and retention.

Fit Force Measurement 
For each fit scenario, fit force measurement was performed at each sensor location (Figure 12) and a t-test, assuming unequal variances, was performed to determine significance (p &#60; 0.05). The average standard deviation across all measurements was &#177; 0.14 N. Higher fit forces indicate a tighter fit.
Head Kinematic and Neck Kinetic Data 
The resultant head linear acceleration, head angular acceleration, head angular velocity, upper neck force, and upper neck moment from a typical drop are shown (Figure 13 through Figure 17). Resultant values were computed by taking the absolute norm of the x, y, and z, direction vectors (Figure 3). A neck injury criterion computed from neck axial force and moment13, Nij, was also computed throughout the impact (Figure 18). From the kinematic results, the different events of the impact can also be identified. For instance, head contact with the anvil in the torso-first impacts can be observed as the large peak in resultant linear acceleration (Figure 13). In angular acceleration, two sets of peaks could be observed (Figure 14). The first peak occurs as a result of the torso impact while the second peak occurs as a result of the neck reaching maximum flexion. In sequence, the events of the impact are torso impact, followed by head contact with the anvil, and then the neck reaching maximum flexion. These events can also be observed in high speed video (Figure 6).
Head-Helmet Relative Motion 
The magnitude of the vector between the forehead and helmet brim, indicating relative head-helmet motion, is shown in Figure 19 for two fit scenarios. Relative change in displacement can be an indicator of helmet movement relative to its pre-impact location.

<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56288/56288fig1.jpg" /> 
Figure 1: Helmet fit scenarios. Helmet Fit Scenario Comparisons on the headform showing (a) Comparison between normal fit and improperly positioned fits (b) normal fit scenario (c) oversized fit scenario (d) forward fit scenario (e) backward fit scenario. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56288/56288fig2.jpg" /> 
Figure 2: Fiber Bragg grating (FBG) five sensor array on headform with sensor located on the front, back, left, right, and top. Each sensor (bottom left) has a thickness and diameter of 2.6 mm and 14 mm, respectively. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56288/56288fig3.jpg" /> 
Figure 3: Drop tower assembly with associated coordinate axis. (a) Overall Drop Tower Assembly with helmeted headform (b) Instrumented headform and neck load cell. Neck load cell coordinate axis is also shown. (c) Corresponding head coordinate axis. Head accelerations and neck loads are measured with reference to the coordinate axis shown, with positive magnitudes in the axis directions. Moments are based on the right hand rule.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56288/56288fig4.jpg" /> 
Figure 4: Interchangeable (a) flat and (b) 45&#176; angled anvil surfaces covered in abrasive tape. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56288/56288fig5.jpg" /> 
Figure 5: Head first (a) and torso first (b) impact scenario drop configurations. For a torso-first impact scenario, a wooden block is used to stop the drop assembly to simulate a torso impact. The helmet visor was also removed prior to all impact simulations. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56288/56288fig6.jpg" /> 
Figure 6: Sequence of images in a torso-first impact. In a torso first impact, the drop gimbal is stopped, allowing for the head to impact the anvil, followed by neck flexion. In contrast, a head-first impact allows full linear movement of the drop gimbal for the head to contact the anvil first.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/56288/56288fig7.jpg" /> 
Figure 7: Dual high-speed camera arrangement around drop tower. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/56288/56288fig8.jpg" /> 
Figure 8: Head reference image markers for motion tracking. Three markers on the head are tracked during impact while a fourth marker defines the forehead point used to calculate head-helmet displacement. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/56288/56288fig9.jpg" /> 
Figure 9: Helmet reference image markers for motion tracking. Three markers on the helmet are tracked during impact while a fourth marker defines the helmet brim point used to calculate head-helmet displacement. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/56288/56288fig10.jpg" /> 
Figure 10: Tracked markers during impact. Three markers are tracked on both the headform and helmet. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/56288/56288fig11.jpg" /> 
Figure 11: Head-helmet displacement vector between forehead and helmet brim that is tracked throughout impact.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/56288/56288fig12.jpg" /> 
Figure 12: Helmet fit forces exerted on headform under different fit scenarios. Error bars representing standard deviation are also shown. Significant differences (p &#60;0.05) between fit force scenarios are indicated (*). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/56288/56288fig13.jpg" /> 
Figure 13: Resultant head center of gravity (COG) linear acceleration for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/56288/56288fig14.jpg" /> 
Figure 14: Resultant head center of gravity (COG) angular acceleration for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 15" class="xfigimg" src="/files/ftp_upload/56288/56288fig15.jpg" /> 
Figure 15: Resultant head center of gravity (COG) angular velocity for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig15large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 16" class="xfigimg" src="/files/ftp_upload/56288/56288fig16.jpg" /> 
Figure 16: Resultant upper neck force for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig16large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 17" class="xfigimg" src="/files/ftp_upload/56288/56288fig17.jpg" /> 
Figure 17: Resultant upper neck moment for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig17large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 18" class="xfigimg" src="/files/ftp_upload/56288/56288fig18v3.jpg" /> 
Figure 18: Nij for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig18v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 19" class="xfigimg" src="/files/ftp_upload/56288/56288fig19.jpg" /> 
Figure 19: Transient head-helmet displacement for a torso first-impact onto a flat anvil at 6 m/s. A regular fit (solid line) and backward fit (dotted line) scenario are compared. The relative change in displacement, in contrast to absolute displacement, is also shown. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56288/56288fig19large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Fit Scenario</td>
			<td>Helmet Size</td>
			<td>Helmet Position</td>
		</tr>
		<tr>
			<td>Normal (Figure 1b)</td>
			<td>Universal</td>
			<td>Normal</td>
		</tr>
		<tr>
			<td>Oversized (Figure 1c)</td>
			<td>XL</td>
			<td>Normal</td>
		</tr>
		<tr>
			<td>Forward (Figure 1d)</td>
			<td>Universal</td>
			<td>Forward</td>
		</tr>
		<tr>
			<td>Backward (Figure 1e)</td>
			<td>Universal</td>
			<td>Backward</td>
		</tr>
	</tbody>
</table>
Table 1: Helmet Fit Scenarios to be studied. The fit scenarios are based on definitions of correct helmet use from previous epidemiological studies specifying proper helmet position3.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Impact Scenario</td>
			<td>Impact Speed</td>
			<td>Impact Surface</td>
			<td>Head/Torso First</td>
		</tr>
		<tr>
			<td>1</td>
			<td>Low (4 m/s)</td>
			<td>Flat</td>
			<td>Head</td>
		</tr>
		<tr>
			<td>2</td>
			<td>High (6 m/s)</td>
			<td>Flat</td>
			<td>Head</td>
		</tr>
		<tr>
			<td>3</td>
			<td>Low</td>
			<td>Angled</td>
			<td>Head</td>
		</tr>
		<tr>
			<td>4</td>
			<td>High</td>
			<td>Angled</td>
			<td>Head</td>
		</tr>
		<tr>
			<td>5</td>
			<td>Low</td>
			<td>Flat</td>
			<td>Torso</td>
		</tr>
		<tr>
			<td>6</td>
			<td>High</td>
			<td>Flat</td>
			<td>Torso</td>
		</tr>
	</tbody>
</table>
Table 2: Impact Scenarios to be simulated.

Watch this Scientific Journal Video about A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact at JoVE.com

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Using an anthropometric head and neck, optical fiber-based fit force transducers, an array of head acceleration and neck force/moment transducers, and a dual high speed camera system, we present a test bed to study helmet retention and effects on biomechanical measures of head and neck injury secondary to head impact.

Conventional wisdom and the language in international helmet testing and certification standards suggest that appropriate helmet fit and retention during ...

a-test-bed-to-examine-helmet-fit-retention-biomechanical-measures

Nanyang Technological University

Research

JoVE Journal

Bioengineering

8.9K Views.  University of Alberta. Met behulp van een antropometrisch hoofd en de nek, optische vezel gebaseerde pasvorm dwingen omvormers, een array van hoofd versnelling en nek kracht/moment omvormers, en een dubbele hoge snelheid camerasysteem, presenteren wij onze een proefbank helm retentie en effecten op biomechanische te studeren maatregelen van hoofd en nek letsel secundair aan hoofd effect.

Video: A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

One of the most commonly reported injuries in children who participate in sports is concussion or mild traumatic brain injury (mTBI)1. Children and youth involved in organized sports such as competitive hockey are nearly six times more likely to suffer a severe concussion compared to children involved in other leisure physical activities2. While the most common cognitive sequelae of mTBI appear similar for children and adults, the recovery profile and breadth of consequences in children remains largely unknown2, as does the influence of pre-injury characteristics (e.g. gender) and injury details (e.g. magnitude and direction of impact) on long-term outcomes. Competitive sports, such as hockey, allow the rare opportunity to utilize a pre-post design to obtain pre-injury data before concussion occurs on youth characteristics and functioning and to relate this to outcome following injury. Our primary goals are to refine pediatric concussion diagnosis and management based on research evidence that is specific to children and youth. To do this we use new, multi-modal and integrative approaches that will:
 1.Evaluate the immediate effects of head trauma in youth 
2.Monitor the resolution of post-concussion symptoms (PCS) and cognitive performance during recovery 
3.Utilize new methods to verify brain injury and recovery
To achieve our goals, we have implemented the Head Impact Telemetry (HIT) System. (Simbex; Lebanon, NH, USA). This system equips commercially available Easton S9 hockey helmets (Easton-Bell Sports; Van Nuys, CA, USA) with single-axis accelerometers designed to measure real-time head accelerations during contact sport participation 3 - 5. By using telemetric technology, the magnitude of acceleration and location of all head impacts during sport participation can be objectively detected and recorded. We also use functional magnetic resonance imaging (fMRI) to localize and assess changes in neural activity specifically in the medial temporal and frontal lobes during the performance of cognitive tasks, since those are the cerebral regions most sensitive to concussive head injury 6. Finally, we are acquiring structural imaging data sensitive to damage in brain white matter.

This article provides an overview of a multi-modal approach to mild traumatic brain injury diagnosis and recovery in youth. This approach combines neuropsychological testing with functional magnetic resonance imaging and the Head Impact Telemetry System to monitor the relationship between head impacts and brain activity during cognitive testing.

One of the most commonly reported injuries in children who participate in sports is concussion or mild traumatic brain injury (mTBI)1. Children and youth ...

Medicine

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. brain, liver, tendon, fat, etc.) when exposed to high strain rates. This study utilized a Split-Hopkinson Pressure Bar (SHPB) to generate strain rates of 100-1,500 sec-1. The SHPB employed a striker bar consisting of a viscoelastic material (polycarbonate). A sample of the biomaterial was obtained shortly postmortem and prepared for SHPB testing. The specimen was interposed between the incident and transmitted bars, and the pneumatic components of the SHPB were activated to drive the striker bar toward the incident bar. The resulting impact generated a compressive stress wave (i.e. incident wave) that traveled through the incident bar. When the compressive stress wave reached the end of the incident bar, a portion continued forward through the sample and transmitted bar (i.e. transmitted wave) while another portion reversed through the incident bar as a tensile wave (i.e. reflected wave). These waves were measured using strain gages mounted on the incident and transmitted bars. The true stress-strain behavior of the sample was determined from equations based on wave propagation and dynamic force equilibrium. The experimental stress-strain response was three dimensional in nature because the specimen bulged. As such, the hydrostatic stress (first invariant) was used to generate the stress-strain response. In order to extract the uniaxial (one-dimensional) mechanical response of the tissue, an iterative coupled optimization was performed using experimental results and Finite Element Analysis (FEA), which contained an Internal State Variable (ISV) material model used for the tissue. The ISV material model used in the FE simulations of the experimental setup was iteratively calibrated (i.e. optimized) to the experimental data such that the experiment and FEA strain gage values and first invariant of stresses were in good agreement.

The current study prescribes a coupled experiment-finite element simulation methodology to obtain the uniaxial dynamic mechanical response of soft biomaterials (brain, liver, tendon, fat, etc.). The multiaxial experimental results that arose because of specimen bulging obtained from Split-Hopkinson Pressure Bar testing were rendered to a uniaxial true stress-strain behavior when simulated through iterative optimization of the finite element analysis of the biomaterial.

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. ...

In Vivo Protocol of Controlled Subconcussive Head Impacts for the Validation of Field Study Data

Subconcussive hits pose a threat to neuronal health as they have shown to induce neuronal structural damage and functional impairment without causing outward symptomology and appear to be a key contributor to an irreversible neurodegenerative disease, chronic traumatic encephalopathy (CTE). In addition, athletes can incur more than 1,000 of these hits per season. The subconcussive soccer heading model (SSHM) is a relevant, reproducible, and leading method of isolating and examining the effects of these subconcussive head impacts. By controlling variables such as ball traveling speed, the frequency of impacts, interval, ball placement to the head, as well as by measuring head impact magnitude, the SSHM provides the scientific community with a superior avenue of investigating the acute subconcussive effects on neuronal health. In this paper, we demonstrate the utility of SSHM in studying a time-course expression of neurofilament-light polypeptide (NF-L) in plasma in a repeated measures fashion. NF-L is an axonal injury marker that has previously been shown to be elevated in boxers and football players following subconcussive head trauma. Thirty-four adult aged soccer players were recruited and randomly assigned to either a soccer heading (n = 18) or kicking (n = 16) group. The heading group executed 10 headers with soccer balls projected at a velocity of 25 mph over 10 min. The kicking group followed the same protocol with 10 kicks. Plasma samples were obtained before and at 0 h, 2 h, and 24 h after heading/kicking and assessed for NF-L expressions. The heading group showed a gradual increase in plasma NF-L expression and peaked at 24 h after the heading protocol, whereas the kicking group remained consistent across the time points. These results confirmed the NF-L data from clinical field studies, encouraging the use of SSHM to validate clinical subconcussion data.

The subconcussive soccer heading model is a safe and concise methodological approach to isolate and measure the effects of subconcussive head impacts.

Subconcussive hits pose a threat to neuronal health as they have shown to induce neuronal structural damage and functional impairment without causing ...

Behavior

Modified Drop Tower Impact Tests for American Football Helmets

A modified National Operating Committee on Standards for Athletic Equipment (NOCSAE) test method for American football helmet drop impact test standards is presented that would provide better assessment of a helmet's on-field impact performance by including a faceguard on the helmet. In this study, a merger of faceguard and helmet test standards is proposed. The need for a more robust systematic approach to football helmet testing procedures is emphasized by comparing representative results of the Head Injury Criterion (HIC), Severity Index (SI), and peak acceleration values for different helmets at different helmet locations under modified NOCSAE standard drop tower tests. Essentially, these comparative drop test results revealed that the faceguard adds a stiffening kinematic constraint to the shell that lessens total energy absorption. The current NOCSAE standard test methods can be improved to represent on-field helmet hits by attaching the faceguards to helmets and by including two new helmet impact locations (Front Top and Front Top Boss). The reported football helmet test method gives a more accurate representation of a helmet's performance and its ability to mitigate on-field impacts while promoting safer football helmets.

This article provides a novel technique to assess the performance characteristics of American football helmets by inclusion of faceguards during NOCSAE Standard drop tests. Additionally, two more impact locations are proposed to be added to the NOCSAE certification.

A modified National Operating Committee on Standards for Athletic Equipment (NOCSAE) test method for American football helmet drop impact test standards ...

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

Mild traumatic brain injuries (mTBIs) are a prevalent health issue in North America. There is increasing pressure to utilize ecologically valid models of closed-head mTBI in the preclinical setting to increase translatability to the clinical population. The awake closed-headed injury (ACHI) model uses a modified controlled cortical impactor to deliver closed-headed injury, inducing clinically relevant behavioral deficits without the need for a craniotomy or the use of an anesthetic.
This technique does not normally induce fatalities, skull fractures, or brain bleeds, and is more consistent with being a mild injury. Indeed, the mild nature of the ACHI procedure makes it ideal for studies investigating repetitive mTBI (r-mTBI). Growing evidence indicates that r-mTBI can result in a cumulative injury that produces behavioral symptoms, neuropathological changes, and neurodegeneration. r-mTBI is common in youths playing sports, and these injuries occur during a period of robust synaptic reorganization and myelination, making the younger population particularly vulnerable to the long-term influences of r-mTBI.
Further, r-mTBI occurs in cases of intimate partner violence, a condition for which there are few objective screening measures. In these experiments, synaptic function was assessed in the hippocampus in juvenile rats that had experienced r-mTBI using the ACHI model. Following the injuries, a tissue slicer was utilized to make hippocampal slices to evaluate bidirectional synaptic plasticity in the hippocampus at either 1 or 7 days following the r-mTBI. Overall, the ACHI model provides researchers with an ecologically valid model to study changes in synaptic plasticity following mTBI and r-mTBI.

Here, it is demonstrated how an awake closed-head injury model can be used for examining the effects of repeated mild traumatic brain injury (r-mTBI) on synaptic plasticity in the hippocampus. The model replicates important features of r-mTBI in patients and is used in conjunction with in vitro electrophysiology.

Mild traumatic brain injuries (mTBIs) are a prevalent health issue in North America. There is increasing pressure to utilize ecologically valid models of ...

Neuroscience

Validating Injury Response and Behavioral Assessments in an mTBI Rat Model

Rat Model of Closed-Head Mild Traumatic Injury and its Validation

Animal models are crucial for advancing our understanding of mild traumatic brain injury (mTBI) and guiding clinical research. To achieve meaningful insights, developing a stable and reproducible animal model is essential. In this study, we report a detailed description of a closed-head mTBI model and a representative validation method using Sprague-Dawley rats to verify the modeling effect. The model involves dropping a 550 g mass weight from a height of 100 cm directly onto the head of a rat on a destructible surface, followed by a 180-degree turn. To assess the injury, rats underwent a series of neurobehavioral assessments 10 min post-injury, including time of loss of consciousness, first seeking-behavior time, escape ability, and beam balance ability test. During the acute and subacute stages following the injury, behavioral tests were conducted to assess motor coordination ability (Beam task), anxiety (Open Field test), and learning and memory abilities (Morris Water Maze test). The closed-head mTBI model produced a consistent injury response with minimal mortality and replicated real-life situations. The validation method effectively verified the model development and ensured the stability and consistency of the model.

Author Spotlight: Advancing Traumatic Brain Injury Research - A Closed-Head Model for Accurate Replication and Rapid Assessment 

Here, we present a closed-head mild traumatic brain injury (mTBI) rat model and its validation exhibiting remarkable similarity to human mTBI concerning behavioral manifestations during the acute and subacute stages.

Animal models are crucial for advancing our understanding of mild traumatic brain injury (mTBI) and guiding clinical research. To achieve meaningful ...

rmTBI Induction in Mice via a Closed-Head Injury Method

Modified Mouse Model of Repetitive Mild Traumatic Brain Injury Incorporating Thinned-Skull Window and Fluid Percussion

Mild traumatic brain injury is a clinically highly heterogeneous neurological disorder. Highly reproducible traumatic brain injury (TBI) animal models with well-defined pathologies are urgently needed for studying the mechanisms of neuropathology after mild TBI and testing therapeutics. Replicating the entire sequelae of TBI in animal models has proven to be a challenge. Therefore, the availability of multiple animal models of TBI is necessary to account for the diverse aspects and severities seen in TBI patients. CHI is one of the most common methods for fabricating rodent models of rmTBI. However, this method is susceptible to many factors, including the impact method used, the thickness and shape of the skull bone, animal apnea, and the type of head support and immobilization utilized. The aim of this protocol is to demonstrate a combination of the thinned-skull window and fluid percussion injury (FPI) methods to produce a precise mouse model of CHI-associated rmTBI. The primary objective of this protocol is to minimize factors that could impact the accuracy and consistency of CHI and FPI modeling, including skull bone thickness, shape, and head support. By utilizing a thinned-skull window method, potential inflammation due to craniotomy and FPI is minimized, resulting in an improved mouse model that replicates the clinical features observed in patients with mild TBI. Results from behavior and histological analysis using hematoxylin and eosin (HE) staining suggest that rmTBI can lead to a cumulative injury that produces changes in both behavior and gross morphology of the brain. Overall, the modified CHI-associated rmTBI presents a useful tool for researchers to explore the underlying mechanisms that contribute to focal and diffuse pathophysiological changes in rmTBI.

Author Spotlight: Optimizing Rodent Models for Investigating the Mechanisms and Rehabilitation in Repeated Mild Traumatic Brain Injury

This protocol presents a modified mouse model of repetitive mild traumatic brain injury (rmTBI) induced via a closed-head injury (CHI) method. The approach features a thinned-skull window and fluid percussion to reduce the inflammation commonly caused by meninges exposure, along with improved reproducibility and accuracy in modeling rmTBI in rodents.

Mild traumatic brain injury is a clinically highly heterogeneous neurological disorder. Highly reproducible traumatic brain injury (TBI) animal models ...

CHILD Model &#8212; Recapitulating Juvenile Concussion and Long-Term Outcomes in Mice

A Pediatric Concussion Model in Mice: Closed Head Injury with Long-Term Disorders (CHILD)

Concussions and mild traumatic brain injuries (mTBI) during childhood represent a significant health endangerment, with many patients later in life exhibiting debilitating physiological, neurological, and psychosocial outcomes. The cellular and molecular pathophysiological mechanisms are relatively unknown, and this significant gap effectively precludes investigations into specific therapeutic strategies to mitigate chronic sequelae. No single animal concussion model recapitulates all the features reported in human subjects, and current models are designed to answer specific research questions. We set out to develop a juvenile concussion that recapitulates clinical early, and long-term symptomology encountered, termed Closed Head Injury with Long-term Disorder (CHILD). In this model, a concussive force via an electromagnetic impactor is delivered to the head of a lightly anesthetized unrestrained post-natal day 17 (P17) mouse, allowing free rotation of the head. Mice are placed on a tightly stretched tin foil across a stereotactic device, and the impactor is carefully aligned with the targeted cortical region. The electromagnetic impactor facilitates the selection of impact depth, dwell, and duration to determine injury severity. A strength of this model is that it allows head rotation, an important clinical feature. After impact, mice are immediately monitored for righting time and time to explore their environment, followed by their return to their dam. Increasing the severity of concussion results in intracranial bleeds in a subset of mice. Mice can be routinely monitored for behavior and neuroimaging over their lifespan, as desired. Various injury severities mimic the heterogeneous nature of juvenile concussions. The current standard CHILD model exhibits no skull fracture, no observable conventional neuroimaging change (similar to clinical), but leads to progressive and persistent neuronal death, altered diffusion MRI, modified neuronal activity and plasticity, increased gliosis, and progressive behavioral perturbations with age. In summary, this CHILD model mimics the early and long-term features observed in many clinical concussion patients.

Pediatric concussions can result in long-term sequelae with physiological and psychological perturbations. This pediatric concussion mouse model results in long-term neuroinflammation, white matter alterations, and neuronal modifications with behavioral dysfunction. The model can be adapted to differential impact locations or repeated concussions to assess lifetime changes.

Concussions and mild traumatic brain injuries (mTBI) during childhood represent a significant health endangerment, with many patients later in life ...

Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging

Mild traumatic brain injury (mTBI), known as concussion, accounts for more than 85% of brain injuries globally. Specifically, uncomplicated mTBI showing negative findings in routine clinical imaging in the acute phase hinders early and appropriate care in these patients. It has been acknowledged that different impact parameters may affect and even accelerate the progress of subsequent neuropsychological symptoms following mTBI. However, the association of impact parameters during concussion to the outcome has not been extensively examined. In the current study, an animal model with closed-head injury (CHI) modified from the weight-drop injury paradigm was described and demonstrated in detail. Adult male Sprague-Dawley rats (n = 20) were randomly assigned to CHI groups with different impact parameters (n = 4 per group). Longitudinal MR imaging studies, including T2-weighted imaging and diffusion tensor imaging, and sequential behavioral assessments, such as modified neurological severity score (mNSS) and the beam walk test, were conducted over a 50-day study period. Immunohistochemical staining for astrogliosis was performed on day 50 post-injury. Worse behavioral performance was observed in animals following repetitive CHI compared to the single injury and sham group. By using longitudinal magnetic resonance imaging (MRI), no significant brain contusion was observed at 24 h post-injury. Nevertheless, cortical atrophy and alteration of cortical fractional anisotropy (FA) were demonstrated on day 50 post-injury, suggesting the successful replication of clinical uncomplicated mTBI. Most importantly, changes in neurobehavioral outcomes and image features observed after mTBI were dependent on impact number, inter-injury intervals, and the selected impact site in the animals. This in vivo mTBI model combined with preclinical MRI provides a means to explore brain injury on a whole-brain scale. It also allows the investigation of imaging biomarkers sensitive to mTBI across varying impact parameters and severity levels.

Here, we present a protocol to establish a closed-head injury animal model replicating the neuroimage outcome of uncomplicated mild traumatic brain injury with the preserved brain structure in the acute phase and long-term brain atrophy. Longitudinal magnetic resonance imaging is the primary method used for evidence.

Mild traumatic brain injury (mTBI), known as concussion, accounts for more than 85% of brain injuries globally. Specifically, uncomplicated mTBI showing ...

Modeling Repetitive Concussive Head Injuries in a Mouse Model

<a href='https://app-jove-com.remotexs.ntu.edu.sg/v/54530/a-repetitive-concussive-head-injury-model-in-mice'>Source: Yang, Z., et.al.&#160;A Repetitive Concussive Head Injury Model in Mice. J. Vis. Exp. (2016).</a>This video demonstrates the induction of repetitive concussive head injuries in a mouse using an electromagnetic impact device. The procedure involves positioning the impact tip between the interfrontal and lambdoid sutures on the skull of an anesthetized mouse and using a probe tip to precisely set the impact depth. The impact is delivered at a controlled velocity and depth to simulate mild traumatic brain injuries, followed by a recovery period, with the injuries repeated at specified intervals.

Source: Yang, Z., et.al.&#160;A Repetitive Concussive Head Injury Model in Mice. J. Vis. Exp. (2016).This video demonstrates the induction of repetitive ...

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

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