Although the safety performance of guardrail end terminals is tested using crash tests in the U.S., occupant injury risks are evaluated based on the flail-space model. This approach developed in the early 1980s neglects the influence of safety features (e.g. seatbelt, airbags, etc.) installed in late model vehicles. In this study, a vehicle (sedan, 1100 kg), a guardrail end terminal (ET-Plus) and a human body model (Global Human Body Model Consortium, GHBMC) were integrated to simulate car-to-end terminal crashes. Five velocities, two offsets, and two angles were used as pre-impact conditions. In all the 20 simulations, kinematics and kinetic data were recorded in GHBMC and vehicle models to calculate the GHBMC injury probabilities and vehicle-based injury metrics, correspondingly. Pre-impact velocity was observed to have the largest effect on the occupant injury measures. All the body-region and full-body injury risks increased with the increasing velocity. Meanwhile, the angles had a larger effect than offset to the change of full-body injury risk (9.1% vs. 0.3%). All the vehicle-based metrics had good correlations to full-body injury probabilities. Occupant Impact Velocity (OIVx), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) had a good correlation to chest, thigh, upper tibia, and lower tibia injuries. All the other correlations (e.g. brain/head injuries) were not statistically significant. The results pointed out that more vehicle-based metrics (ASI and THIV) could help improve the predictability in terms of occupant injury risks in the tests. Numerical methodology could be used to assess head and brain injury probabilities, which were not predictable by any vehicle-based metrics.

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A common cause of fatalities for e-scooter riders is a collision between a car and an e-scooter. To develop a better understanding of the complex injury mechanisms in these collisions, four crashes between an e-scooter and a family car/sedan and a sports utility vehicle were simulated using finite element models. The vehicles impacted the e-scooter at a speed of 30 km/hr in a perpendicular collision, and at 15 degrees towards the vehicle, to simulate a rider being struck by a turning vehicle. The risks of serious injury to the rider were low for the head, brain, and neck, but femur/tibia fractures were observed in all simulations. The primary cause of head and brain injuries was found to be the head-ground impact in cases where such an impact occurred.

High-speed train may collide with many obstacles, which can cause serious occupant injury. This study aims to investigate the dynamic characteristic of occupant during the frontal collision between high-speed train and obstacle. The finite element method was used to establish the collision model between the head vehicle of the train and obstacle. The frontal collision simulation tests under three collision conditions were established. The dynamic characteristics of occupants under different collision speeds and collision angles were explored. According to the above research, the influences of collision angle and speed on occupant injuries were systematically studied, and the risk boundaries for Railway Group Standard GMRT2100: Rail Vehicle Structures and Passive Safety (GM/RT2100) and Abbreviated injury scale ≥ 3 (AIS 3 + ) injury risk ≤ 5 % were finally proposed. The results show that the occupant injuries increased with the increase of collision speed, and most of the injury values at the collision angle of 20° were the minimum. The risk boundary for AIS 3 + injury risk ≤ 5 % was higher than that for GM/RT2100. The findings in this study are helpful to understand the occupant injury mechanism during the frontal collision between high-speed train and obstacle.

Introduction: Non-combat musculoskeletal injuries (MSKIs) during military training significantly impede the US military\'s functionality, with an annual cost exceeding $3.7 billion. This study aimed to investigate the effectiveness of a markerless motion capture system and full-body biomechanical movement pattern assessments to predict MSKI risk among military trainees. Methods: A total of 156 male United States Air Force (USAF) airmen were screened using a validated markerless biomechanics system. Trainees performed multiple functional movements, and the resultant data underwent Principal Component Analysis and Uniform Manifold And Projection to reduce the dimensionality of the time-dependent data. Two approaches, semi-supervised and supervised, were then used to identify at-risk trainees. Results: The semi-supervised analysis highlighted two major clusters with trainees in the high-risk cluster having a nearly five times greater risk of MSKI compared to those in the low-risk cluster. In the supervised approach, an AUC of 0.74 was produced when predicting MSKI in a leave-one-out analysis. Discussion: The application of markerless motion capture systems to measure an individual\'s kinematic profile shows potential in identifying MSKI risk. This approach offers a novel way to proactively address one of the largest non-combat burdens on the US military. Further refinement and wider-scale implementation of these techniques could bring about substantial reductions in MSKI occurrence and the associated economic costs.

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A primary cause is front wheel collisions with a vertical surface such as a curb or object, generically referred to as a \"stopper.\" In this study, various e-scooter-stopper crashes were simulated numerically across different impact speeds, approach angles, and stopper heights to characterize the influence of crash type on rider injury risk during falls. A finite element (FE) model of a standing Hybrid III anthropomorphic test device was used as the rider model after being calibrated against certification test data. Additionally, an FE model of an e-scooter was developed based on reconstructed scooter geometry. Forty-five FE simulations were run to investigate various e-scooter crash scenarios. Test parameters included impact speed (from 3.2 m/s to 11.16 m/s), approach angle (30 deg to 90 deg), and stopper height (52 mm, 101 mm, and 152 mm). Additionally, the perpendicular (90 deg) impact scenarios were run twice: once with Hybrid-III arm activation to mimic a rider attempting to break a fall with their hands and once without this condition. Overall, the risks of serious injury to the rider varied greatly; however, roughly half the impact scenarios indicated serious risk to the rider. This was expected, as the speeds tested were in the upper 25th percentile of reported scooter speeds. The angle of approach was found to have the greatest effect on injury risk to the rider, and was shown to be positively correlated with injury risk. Smaller approach angles were shown to cause the rider to land on their side, while larger approach angles caused the rider to land on their head and chest. Additionally, arm bracing was shown to reduce the risk of serious injury in two thirds of the impact scenarios.

Pedestrians are one of the most vulnerable road users. In 2019, the USA reported the highest number of pedestrian fatalities number in nearly three decades. To better protect pedestrians in car-to-pedestrian collisions (CPC), pedestrian biomechanics must be better investigated. The pre-impact conditions of CPCs vary significantly in terms of the characteristics of vehicles (e.g., front-end geometry, stiffness, etc.) and pedestrians (e.g., anthropometry, posture, etc.). The influence of pedestrian gait posture has not been well analyzed. The purpose of this study was to numerically investigate the changes in pedestrian kinematics and injuries across various gait postures in two different vehicle impacts. Five finite element (FE) human body models, that represent the 50th percentile male in gait cycle, were developed and used to perform CPC simulations with two generic vehicle FE models representing a low-profile vehicle and a high-profile vehicle. In the impacts with the high-profile vehicle, a sport utility vehicle, the pedestrian models usually slide above the bonnet leading edge and report shorter wrap around distances than in the impacts with a low-profile vehicle, a family car/sedan (FCR). The pedestrian postures influenced the postimpact rotation of the pedestrian and consequently, the impacted head region. Pedestrian posture also influenced the risk of injuries in the lower and upper extremities. Higher bone bending moments were observed in the stance phase posture compared to the swing phase. The findings of this study should be taken into consideration when examining pedestrian protection protocols. In addition, the results of this study can be used to improve the design of active safety systems used to protect pedestrians in collisions.

There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and pressure-sensing insoles) and used these data to develop two multi-sensor algorithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize running impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using a commonly measured impact metric, the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running stride. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to a given loading cycle. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in estimated bone damage. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.

Guardrails were designed to deter vehicle access to off-road areas and consequently prevent hitting rigid fixed objects alongside the road (e.g. trees, utility poles, traffic barriers, etc.). However, guardrails cause 10 % of deaths in vehicle-to-fixed-object crashes, which recently attracted attention in the highway safety community on the vehicle-based injury criteria used in regulations. The objectives of this study were to investigate both full-body and body-region driver injury probabilities using finite element (FE) simulations, to quantify the influence of pre-impact conditions on injury probabilities, and to analyze the relationship between the vehicle-based crash severity metrics currently used in regulations and the injury probabilities assessed using dummy-based injury criteria. A total of 20 FE impact simulations between a car (Toyota Yaris) with a Hybrid III M50 dummy model in the driver seat and an end terminal model (ET-Plus) were performed in various configurations (e.g. pre-impact velocities, offsets, and angles). The driver\'s risk of serious injuries (AIS 3+) was estimated based on kinematic and kinetic responses of the dummy during the crashes. A non-linear regression approach was used to compare the injury probabilities assessed in this study to the vehicle-based crash severity metrics used in the testing regulations. In particular, the US Manual for Assessing Safety Hardware (MASH) guideline and European procedures (EN1317) were used for the study. All the recorded dummy-based injury criteria values pass the Federal Motor Vehicle Safety Standard (FMVSS) 208 limits which indicated a low driver risk of serious injury. Overall, the pre-impact vehicle velocity showed to have the highest influence in almost all injury probabilities (59 %, 79 %, 62 %, and 44 % in full-body, head, neck, and chest injuries, respectively). The offset between vehicle midline and the guardrail barrier was the most important variable for thigh injuries (56 %). The assessed injury probabilities were compared to vehicle-based severity metrics. The full-body and chest injuries showed the highest correlation with Occupant Impact Velocity (OIV), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) (R2 > 0.6). Lower correlations of thigh injuries were recorded to OIV (R2 = 0.59) and THIV (R2 = 0.46). Meanwhile, weak correlations were observed between all the other regressions which indicated that no vehicle-based criteria could be used to predict head and neck injuries. Car-to-end terminal crash FE simulations involving a dummy model were performed for the first time in this study. The results pointed out the limitations of the standard vehicle-based injury methods in terms of head and neck injury prediction. The dummy-based injury assessment methodology presented in this study could supplement the crash tests for various impact conditions. In addition, the models could be used to design new advanced guardrail end terminals.

BACKGROUND: Rollover crashes of buses occur less frequently than do those involving passenger cars; however, they are associated with higher fatality rates. During rollover crashes, a vehicle experiences multidirectional acceleration and multiple impacts, yielding a complex interaction between structural components and its occupants. A better understanding of vehicle and occupant\'s motion, structural deformation, and vehicle and road interactions are necessary to improve the safety of the occupants during this event. One of the key factors in rollover crashworthiness assessment is to investigate the relationship between the strength of the vehicle\'s structure and the risk of injury outcomes. However, rollover crashes involving buses have received less research attention than have those involving passenger cars. Experimental studies in bus rollover safety have mainly focused on the structural integrity of the passenger compartment without considering the occupant responses. The main goal of this research is to evaluate the rollover mechanism and associated injury risk during two experimental rollover tests for a paratransit cutaway bus that is commonly used by transit agencies.
METHODS: The modified dolly rollover (MDR) and tilt table (TT) tests were conducted using a similar bus and anthropomorphic test device (ATD) configurations. In each test, a 2-point and 3-point belted Hybrid III 50th percent male ATDs were used to quantify the kinematics of the occupants. The deformation index (DI), accelerations and angular velocities of the bus\'s CG were measured as vehicle responses. The collected data were then calibrated and filtered to assess the effects of the test procedure on kinematic responses of the vehicle and occupants. Next, the effectiveness of the 2-point vs 3-point seatbelt to reduce or prevent the injuries, the vulnerable body regions and corresponded injury risk were evaluated.
RESULTS: The residual space remained intact (DI < 1) during both rollover tests, however, the ATD responses were quite different. The results of the injury assessment indicate that the risk of the injuries in the MDR test was significantly higher than the TT test. The highest risk of injuries was identified for the head, neck, and shoulder of 2-point belted ATD during the MDR test. Also, the main source of injuries during the MDR test was partial ejection due to the shattered side window, whereas for the TT test impacts between the ATDs and the side window and/or window frame were the injury causes. From the vehicle point of view, the total energy produced in the MDR was 3.5 times higher than the TT test, but the overall structural deformation in the TT test was higher than MDR test. Overall, the tilt table test provides a more severe scenario compared to the MDR test for the assessment of structural strength. Considering the limited real-world injury data in rollover crashes of buses, the MDR test presented the more realistic occupant responses.

This study aimed to provide a comprehensive strength-based physiological profile of women\'s NCAA Division I basketball and gymnastic athletes; and to make sport-specific comparisons for various strength characteristics of the knee flexor and extensor muscles. A focus on antagonist muscle balance (hamstrings-to-quadriceps ratios, H:Q) was used to elucidate vulnerabilities in these at-risk female athletes. Fourteen NCAA Division I women\'s basketball and 13 gymnastics athletes performed strength testing of the knee extensors and flexors. Outcome measures included absolute and relative (body mass normalised) peak torque (PT), rate of torque development at 50, 100, 200 ms (RTD50 etc.) and H:Q ratios of all variables. The basketball athletes had greater absolute strength for all variables except for isokinetic PT at 240°s-1 and isometric RTD50 for the knee extensors. Gymnasts showed ~20% weaker body mass relative concentric PT for the knee flexors at 60 and 120°·s-1, and decreased conventional H:Q ratios at 60 and 240°·s-1 (~15%). These findings suggest that collegiate level gymnastics athletes may be prone to increased ACL injury risk due to deficient knee flexor strength and H:Q strength imbalance. Coaches may use these findings when implementing injury prevention screening and/or for individualised strength training programming centered around an athletes strength-related deficits.