An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios.

For highly autonomous vehicles, human does not need to operate continuously vehicles. The brain-computer interface system in autonomous vehicles will highly depend on the brain states of passengers rather than those of human drivers. It is a meaningful and vital choice to translate the mental activities of human beings, essentially playing the role of advanced sensors, into safe driving. Quantifying the driving risk cognition of passengers is a basic step toward this end. This study reports the creation of an fNIRS dataset focusing on the prefrontal cortex activity in fourteen types of highly automated driving scenarios. This dataset considers age, sex and driving experience factors and contains the data collected from an 8-channel fNIRS device and the data of driving scenarios. The dataset provides data support for distinguishing the driving risk in highly automated driving scenarios via brain-computer interface systems, and it also provides the possibility of preventing potential hazards in some scenarios, in which risk remains at a high value for an extended period, before hazard occurs.

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Assessment of brain injury characterization and influence of modeling approaches.

In this study, using computational biomechanics models, we investigated influence of the skull-brain interface modeling approach and the material property of cerebrum on the kinetic, kinematic and injury outputs. Live animal head impact tests of different severities were reconstructed in finite element simulations and DAI and ASDH injury results were compared. We used the head/brain models of Total HUman Model for Safety (THUMS) and Global Human Body Models Consortium (GHBMC), which had been validated under several loading conditions. Four modeling approaches of the skull-brain interface in the head/brain models were evaluated. They were the original models from THUMS and GHBMC, the THUMS model with skull-brain interface changed to sliding contact, and the THUMS model with increased shear modulus of cerebrum, respectively. The results have shown that the definition of skull-brain interface would significantly influence the magnitude and distribution of the load transmitted to the brain. With sliding brain-skull interface, the brain had lower maximum principal stress compared to that with strong connected interface, while the maximum principal strain slightly increased. In addition, greater shear modulus resulted in slightly higher the maximum principal stress and significantly lower the maximum principal strain. This study has revealed that using models with different modeling approaches, the same value of injury metric may correspond to different injury severity.

Human injury-based safety decision of automated vehicles.

Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protection, we propose an injury risk mitigation-based decision-making algorithm. The algorithm is guided by a real-time, data-driven human injury prediction model and is assessed using detailed first-hand information collected from real-world crashes. The results demonstrate that integrating injury prediction into decision-making is promising for reducing traffic casualties. Because safety decisions involve harm distribution for different participants, we further analyze the potential ethical issues quantitatively, providing a technically critical step closer to settling such dilemmas. This work demonstrates the feasibility of applying mining tools to identify the underlying mechanisms embedded in crash data accumulated over time and opens the way for future AVs to facilitate optimal road traffic safety.

A GAN based approach for inferring progression trajectories of costal cartilage calcification from cross-sectional data at image level.

BACKGROUND: Costal cartilage calcification (CCC) increases with age and presents differently for men and women. In individuals, however, the cross-sectional studies that show such trends do not reveal the geometric trajectories through which calcification might accumulate across a lifetime. Generative adversarial networks have the potential to reveal such trajectories from cross-sectional data by learning population trends and synthesizing individualized images at progressive levels of calcification. METHODS: Chest wall mid-surface CT images with normalized cartilage morphologies were produced for 379 subjects aged 6 to 90, and labeled by sex and calcification severity. A conditional GAN with added loss terms to favor one-way accumulation of CCC was trained using organized image batches. GAN performance was assessed by comparing the distributions of images between the training and synthetic groups. RESULTS: Synthetic images generated from a common seed for a given sex and at successive calcification severity levels showed incremental and regional growth of calcification sites. CCC patterns for synthetic male and female images matched known sex-based differences, and individual CCC growth in synthetic images was consistent with previously observed population trends. These trends in the synthetic images were also quantified by structural similarity scores. Synthetic images generated from different input seeds further showed individual variance in specific regions and trajectories of CCC accumulation. CONCLUSION: This study inferred individual progression of CCC accumulation from uncalcified to severely calcified using cross-sectional image data. This information can inform computational models of the changing chest wall biomechanics with age, and the GAN-based technique shows potential for inferring longitudinal data from population trends in other clinical areas.

Kinetic and Kinematic Features of Pedestrian Avoidance Behavior in Motor Vehicle Conflicts.

The active behaviors of pedestrians, such as avoidance motions, affect the resultant injury risk in vehicle-pedestrian collisions. However, the biomechanical features of these behaviors remain unquantified, leading to a gap in the development of biofidelic research tools and tailored protection for pedestrians in real-world traffic scenarios. In this study, we prompted subjects ("pedestrians") to exhibit natural avoidance behaviors in well-controlled near-real traffic conflict scenarios using a previously developed virtual reality (VR)-based experimental platform. We quantified the pedestrian-vehicle interaction processes in the pre-crash phase and extracted the pedestrian postures immediately before collision with the vehicle; these were termed the "pre-crash postures." We recorded the kinetic and kinematic features of the pedestrian avoidance responses-including the relative locations of the vehicle and pedestrian, pedestrian movement velocity and acceleration, pedestrian posture parameters (joint positions and angles), and pedestrian muscle activation levels-using a motion capture system and physiological signal system. The velocities in the avoidance behaviors were significantly different from those in a normal gait (p < 0.01). Based on the extracted natural reaction features of the pedestrians, this study provides data to support the analysis of pedestrian injury risk, development of biofidelic human body models (HBM), and design of advanced on-vehicle active safety systems.

A data-driven, kinematic feature-based, near real-time algorithm for injury severity prediction of vehicle occupants.

Accurate real-time prediction of occupant injury severity in unavoidable collision scenarios is a prerequisite for enhancing road traffic safety with the development of highly automated vehicles. Specifically, a safety prediction model provides a decision reference for the trajectory planning system in the pre-crash phase and the adaptive restraint system in the in-crash phase. The main goal of the current study is to construct a data-driven, vehicle kinematic feature-based model to realize accurate and near real-time prediction of in-vehicle occupant injury severity. A large-scale numerical database was established focusing on occupant kinetics. A first-step deep-learning model was established to predict occupant kinetics and injury severity using a convolutional neural network (CNN). To reduce the computational time for real-time application, the second step was to extract simplified kinematic features from vehicle crash pulses via a feature extraction method, which was inspired by a visualization approach applied to the CNN-based model. The features were incorporated with a low-complexity machine-learning algorithm and achieved satisfactory accuracy (85.4 % on the numerical database, 78.7 % on a 192-case real-world dataset) and decreased computational time (1.2 ± 0.4 ms) on the prediction tasks. This study demonstrated the feasibility of using data-driven and feature-based approaches to achieve accurate injury risk estimation prior to collision. The proposed model is expected to provide a decision reference for integrated safety systems in the next generation of automated vehicles.

Safety envelope of pedestrians upon motor vehicle conflicts identified via active avoidance behaviour.

Human reaction plays a key role in improved protection upon emergent traffic situations with motor vehicles. Understanding the underlying behaviour mechanisms can combine active sensing system on feature caption and passive devices on injury mitigation for automated vehicles. The study aims to identify the distance-based safety boundary ("safety envelope") of vehicle-pedestrian conflicts via pedestrian active avoidance behaviour recorded in well-controlled, immersive virtual reality-based emergent traffic scenarios. Via physiological signal measurement and kinematics reconstruction of the complete sequence, we discovered the general perception-decision-action mechanisms under given external stimulus, and the resultant certain level of natural harm-avoidance action. Using vision as the main information source, 70% pedestrians managed to avoid the collision by adapting walking speeds and directions, consuming overall less "decision" time (0.17-0.24 s vs. 0.41 s) than the collision cases, after that, pedestrians need enough "execution" time (1.52-1.84 s) to take avoidance action. Safety envelopes were generated by combining the simultaneous interactions between the pedestrian and the vehicle. The present investigation on emergent reaction dynamics clears a way for realistic modelling of biomechanical behaviour, and preliminarily demonstrates the feasibility of incorporating in vivo pedestrian behaviour into engineering design which can facilitate improved, interactive on-board devices towards global optimal safety.

Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models.

OBJECTIVE: The objectives of this study were to develop a method for modeling obese pedestrians and to investigate effects of obesity on pedestrian impact responses and injury outcomes. METHODS: The GHBMC (Global Human Body Model Consortium) 50th percentile male pedestrian model was morphed into geometries with 4 body mass index (BMI) levels (25/30/35/40 kg/m2) predicted by statistical body shape models. Each of the 4 morphed models was further morphed from a standing posture into 2 other gaits (toe-off and mid-swing), which resulted in a total of 12 (4 BMIs × 3 postures) models. Each model was used to simulate vehicle-to-pedestrian impact under 2 impact velocities. Pedestrian kinematics and injury measures were analyzed focusing on lower extremities. Statistical analyses were performed to examine significance of obesity on concerned injury measures. RESULTS: Peak values of the bending moment at tibia, force at medial collateral ligament (MCL), bending angle at knee joint, and contact force between vehicle and pedestrian increased significantly (P < .05) with increased BMI. By analyzing kinematics of the lower extremity, the overall vehicle-to-pedestrian impact was divided into 2 phases: "initial contact" and "tibia rebound." For obese pedestrians, the added mass caused a higher tibia bending moment in the initial contact phase, and the greater moment of inertia led to greater bending angle and MCL force in the tibia rebound phase. Statistical analyses also revealed that pre-impact posture and impact velocity had significant effects on all injury measures. CONCLUSIONS: Obesity could significantly increase the risk of pedestrian lower extremity injuries due to the inertial effect from the added mass. Pre-impact posture and impact velocity also significantly affect pedestrian injury measures. Future vehicle designs for pedestrian protection should consider populations with obesity. This study demonstrated the feasibility of using parametric human modeling to account for population diversity in injury prediction.

Seating preferences in highly automated vehicles and occupant safety awareness: A national survey of Chinese perceptions.

Objective: The potential challenge for providing occupant protection accompanying seating preferences is an essential safety prerequisite for highly automated vehicle (HAV) popularization. This research is aimed toward identifying Asia-specific individualized seating preferences in HAVs and occupant safety awareness via a national survey in China.Methods: An online questionnaire survey was performed to investigate seating preferences (i.e., sitting posture, seating orientation, and position) and occupant safety awareness (i.e., seat belt usage and receptiveness to extended or additional restraints beyond the conventional three-point seat belt). We assessed whether perceptions were modulated by individual characteristics via bivariate and correlation analyses. The possibility of wearing seat belts was estimated by binary logistic regression.Results: The final survey data set includes 1,018 respondents after a rigorous validity check (response rate: 59.2%). The results show that preferred sitting postures and seating orientation were significantly associated with sociodemographic characteristics (e.g., gender, age, city tier) (p < 0.05). The rear seat was preferred in both the conventional (65.6%) and "face-to-face mode" seating configurations (77.6%), largely due to the fact that customers subjectively viewed it as being safer than sitting in a front seat in case of collisions. Despite the current trend of an increasing usage rate of seat belts, 48.5% of respondents preferred to be unrestrained in rear seats, especially for the subgroups who were from less developed cities and with a higher usage rate of public transport (p < 0.01). Low receptiveness to extended restraint and high comfort requirements were confirmed for the young, high-frequency road users, and for those who were from developed areas (p < 0.05).Conclusions: Diversified and specific seating preferences of Chinese occupants were identified facing emerging use of HAVs. Next generation occupant protection systems shall be adapted to account for the individualized expectations and needs on seating designs from certain population groups. Balanced restraint design between safety and comfort was required to exceed the existing strong dependence on exogenous causes of restraint use (e.g., legal restrictions) in Asia.

Influence of population variability in ligament material properties on the mechanical behavior of ankle: a computational investigation.

Biomechanical behavior of ankle ligaments varies among individuals, with the underlying mechanism at multiple scales remaining unquantified. The present probabilistic study investigated how population variability in ligament material properties would influence the joint mechanics. A previously developed finite element ankle model with parametric ligament properties was used. Taking the typical external rotation as example loading scenario, joint stability of the investigated population was consistently shared by specific ligaments within a narrow tolerance range, i.e. 62.8 ± 8.2 Nm under 36.1 ± 5.7° foot rotation. In parallel, the inherent material variability significantly alters the consequent injury patterns. Three most vulnerable ligaments and the consequent rupture sequences were identified, with the structural weak spot and the following progressive stability loss dominated by the relative stiffness among ligaments. This study demonstrated the feasibility of biofidelic models in investigating individual difference at the material level, and emphasized the importance of probabilistic description of individual difference when identifying the injury mechanism of a broad spectrum.

Are riders of electric two-wheelers safer than bicyclists in collisions with motor vehicles?

Electric two-wheelers (E2Ws) have become newly popular transportation tools with the associated growing traffic safety concerns. E2W riders and bicyclists behave similarly as vulnerable road users (VRUs), while exhibited dissimilarities in riding postures and interactions with the two-wheelers. Existing epidemiology reveals prominent differences in injury risks between E2W riders and other vulnerable road users in collisions with motor vehicles. The objective of this study is to investigate the factors influencing kinematics and head injury risks of two-wheeler rides in two-wheeler-vehicle collisions and compare between E2W-vehicle and bicycle-vehicle collisions. Via multi-body modeling of two two-wheeler types, two vehicle types, and three rider statures in MADYMO, twelve collision scenarios were developed. A simulation matrix considering a range of impact velocities and relative positions was performed for each scenario. A subsequent parametric analysis was conducted with focus on the kinematics and head injury risks of two-wheeler riders. Results show that the head injury risk increased with vehicle moving velocity, while the two-wheeler velocity and relative location between rider and vehicle prior to the collision exhibited highly non-linear influence on the kinematical response. The rider with larger stature had higher possibilities to miss head impact on the vehicle. In collisions with the sedan, E2W riders would sustain lower head injury risks with lower contacting velocity on the windshield than bicyclists. While in collisions with the SUV, E2W riders would sustain increasing head injury risks due to the higher structural stiffness at contact, and the risk level was about the same as bicyclists. The findings revealed the loading mechanisms behind the different head injury risks between E2W riders and bicyclists.

A Cortical Thickness Mapping Method for the Coxal Bone Using Morphing.

As human body finite element models become more integrated with the design of safety countermeasures and regulations, novel models need to be developed that reflect the variation in the population's anthropometry. However, these new models may be missing information which will need to be translated from existing models. During the development of a 5th percentile female occupant model (F05), cortical thickness information of the coxal bone was unavailable due to resolution limits in the computed tomography (CT) scans. In this study, a method for transferring cortical thickness information from a source to a target model with entirely different geometry and architecture is presented. The source and target models were the Global Human Body Models Consortium (GHBMC) 50th percentile male (M50) and F05 coxal bones, respectively. To project the coxal bone cortical thickness from the M50 to the F05, the M50 model was first morphed using a Kriging method with 132 optimized control points to the F05 anthropometry. This technique was found to be accurate with a mean nodal discrepancy of 1.27 mm between the F05 and morphed M50 (mM50) coxal bones. Cortical thickness at each F05 node was determined by taking the average cortical thickness of every mM50 node, non-linearly weighted by its distance to the F05 nodes. The non-linear weighting coefficient, ß, had a large effect on the accuracy and smoothness of the projected cortical bone thickness. The optimal projection had ß = 4 and was defined when the tradeoff between projection accuracy and smoothness was equal. Finally, a quasi-static pelvis compression was simulated to examine to effect of ß. As ß, increased from 0 to 4, the failure force decreased by ~100 N, whereas the failure displacement increased by 0.9 mm. Results from quasi-static compression tests of the F05 pelvis were comparable to experimental results. This method could be applied to other anatomical regions where cortical thickness variation is important, such as the femur and ribs and is not limited to GHBMC-family models. Furthermore, this process will aid the development of subject-specific finite element models where accurate cortical bone thickness measurements cannot be obtained.

Propagation of Syndesmotic Injuries During Forced External Rotation in Flexed Cadaveric Ankles.

BACKGROUND: Forced external rotation of the foot is a mechanism of ankle injuries. Clinical observations include combinations of ligament and osseous injuries, with unclear links between causation and injury patterns. By observing the propagation sequence of ankle injuries during controlled experiments, insight necessary to understand risk factors and potential mitigation measures may be gained. HYPOTHESIS: Ankle flexion will alter the propagation sequence of ankle injuries during forced external rotation of the foot. STUDY DESIGN: Controlled laboratory study. METHODS: Matched-pair lower limbs from 9 male cadaveric specimens (mean age, 47.0 ± 11.3 years; mean height, 178.1 ± 5.9 cm; mean weight, 94.4 ± 30.9 kg) were disarticulated at the knee. Specimens were mounted in a test device with the proximal tibia fixed, the fibula unconstrained, and foot translation permitted. After adjusting the initial ankle position (neutral, n = 9; dorsiflexed, n = 4; plantar flexed, n = 4) and applying a compressive preload to the tibia, external rotation was applied by rotating the tibia internally while either lubricated anteromedial and posterolateral plates or calcaneal fixation constrained foot rotation. The timing of osteoligamentous injuries was determined from acoustic sensors, strain gauges, force/moment readings, and 3-dimensional bony kinematics. Posttest necropsies were performed to document injury patterns. RESULTS: A syndesmotic injury was observed in 5 of 9 (56%) specimens tested in a neutral initial posture, in 100% of the dorsiflexed specimens, and in none of the plantar flexed specimens. Superficial deltoid injuries were observed in all test modes. CONCLUSION: Plantar flexion decreased and dorsiflexion increased the incidence of syndesmotic injuries compared with neutral matched-pair ankles. Injury propagation was not identical in all ankles that sustained a syndesmotic injury, but a characteristic sequence initiated with injuries to the medial ligaments, particularly the superficial deltoid, followed by the propagation of injuries to either the syndesmotic or lateral ligaments (depending on ankle flexion), and finally to the interosseous membrane or the fibula. CLINICAL RELEVANCE: Superficial deltoid injuries may occur in any case of hyper-external rotation of the foot. A syndesmotic ankle injury is often concomitant with a superficial deltoid injury; however, based on the research detailed herein, a deep deltoid injury is then concomitant with a syndesmotic injury or offloads the syndesmosis altogether. A syndesmotic ankle injury more often occurs when external rotation is applied to a neutral or dorsiflexed ankle. Plantar flexion may shift the injury to other ankle ligaments, specifically lateral ligaments.

Active muscle response contributes to increased injury risk of lower extremity in occupant-knee airbag interaction.

OBJECTIVE: Recent field data analysis has demonstrated that knee airbags (KABs) can reduce occupant femur and pelvis injuries but may be insufficient to decrease leg injuries in motor vehicle crashes. An enhanced understanding of the associated injury mechanisms requires accurate assessment of physiological-based occupant parameters, some of which are difficult or impossible to obtain from experiments. This study sought to explore how active muscle response can influence the injury risk of lower extremities during KAB deployment using computational biomechanical analysis. METHODS: A full-factorial matrix, consisting of 48 finite element simulations of a 50th percentile occupant human model in a simplified vehicle interior, was designed. The matrix included 32 new cases in combination with 16 previously reported cases. The following influencing factors were taken into account: muscle activation, KAB use, KAB design, pre-impact seating position, and crash mode. Responses of 32 lower extremity muscles during emergency braking were replicated using one-dimensional elements of a Hill-type constitutive model, with the activation level determined from inverse dynamics and validated by existing volunteer tests. Dynamics of unfolding and inflating of the KABs were represented using the state-of-the-art corpuscular particle method. Abbreviated Injury Scale (AIS) 2+ injury risks of the knee-thigh-hip (KTH) complex and the tibia were assessed using axial force and resultant bending moments. With all simulation cases being taken together, a general linear model was used to assess factor significance (P <.05). RESULTS: As estimated by the regression model across all simulation cases, use of KABs significantly reduced axial femur forces by 4.74 ± 0.43 kN and AIS 2+ injury risk of KTH by 47 ± 6% (P <.05) but did not provide substantial change to injury risk of leg fractures. Muscle activation significantly increased axial force and bending moment of the femur (3.87 ± 0.38 kN and 64.3 ± 5.9 Nm), the tibia (1.49 ± 0.12 kN and 43.0 ± 6.4 Nm), and the resultant probability of AIS 2+ tibia injuries by 36 ± 6% regardless of KAB use and crash scenario. Specifically, when counting on a relative scale, muscle activation exhibited more prominent elevation of injury risk for in-position occupants than out-of-position occupants. In a representative crash scenario-that is, using a bottom-deployed KAB in a nearside oblique impact-muscle bracing of the right leg may lead to 2.6 times higher tibia fracture risk than being relaxed for an out-of-position occupant and 5.4 times higher for an in-position occupant. DISCUSSION AND CONCLUSIONS: The mechanism of higher leg injuries in the presence of KAB deployment in real-world crashes can be interpreted by the increased effective body mass, axial compression along the shafts of long bones, and altered pre-impact posture due to muscle contraction. The present analysis suggests that active muscle response can increase the risk of lower extremity injury during occupant-KAB interaction. This study demonstrated the feasibility of advanced human models to investigate the influence of physiologically based parameters on injury outcomes evidenced in field study and insight from computational examination on human variability for development of future restraint systems. Future efforts are recommended on realistic vehicle and restraint environment and advanced modeling strategies toward a full understanding of KAB efficacy.

Fiber-based modeling of in situ ankle ligaments with consideration of progressive failure.

Ligament sprains account for a majority of injuries to the foot and ankle complex among athletic populations. The infeasibility of measuring the in situ response and load paths of individual ligaments has precluded a complete characterization of their mechanical behavior via experiment. In the present study a fiber-based modeling approach of in situ ankle ligaments was developed and validated for determining the heterogeneous force-elongation characteristics and the consequent injury patterns. Nine major ankle ligaments were modeled as bundles of discrete elements, corresponding functionally to the structure of collagen fibers. To incorporate the progressive nature of ligamentous injury, the limit strain at the occurrence of fiber failure was described by a distribution function ranging from 12% to 18% along the width of the insertion site. The model was validated by comparing the structural kinetic and kinematic response obtained experimentally and computationally under well-controlled foot rotations. The simulation results replicated the 6 degree-of-freedom bony motion and ligamentous injuries and, by implication, the in situ deformations of the ligaments. Gross stiffness of the whole ligament derived from the fibers was comparable to existing experimental data. The present modeling approach provides a biomechanically realistic, interpretable and computationally efficient way to characterize the in situ ligament slack, sequential and heterogeneous uncrimping of collagen fascicles and failure propagation as the external load is applied. Applications of this model include functional ankle joint mechanics, injury prevention and countermeasure design for athletes.

Searching for the "sweet spot": the foot rotation and parallel engagement of ankle ligaments in maximizing injury tolerance.

Ligament sprains, defined as tearing of bands of fibrous tissues within ligaments, account for a majority of injuries to the foot and ankle complex in field-based sports. External rotation of the foot is considered the primary injury mechanism of syndesmotic ankle sprains with concomitant flexion and inversion/eversion associated with particular patterns of ligament trauma. However, the influence of the magnitude and direction of loading vectors to the ankle on the in situ stress state of the ligaments has not been quantified in the literature. The objective of the present study was to search for the maximum injury tolerance of a human foot with an acceptable subfailure distribution of individual ligaments. We used a previously developed and comprehensively validated foot and ankle model to reproduce a range of combined foot rotation experienced during high-risk sports activities. Biomechanical computational investigation was performed on initial foot rotation from [Formula: see text] of plantar flexion to [Formula: see text] of dorsiflexion, and from [Formula: see text] of inversion to [Formula: see text] of eversion prior to external rotation. Change in initial foot rotation shifted injury initiation among different ligaments and resulted in a wide range of injury tolerances at the structural level (e.g., 36-125 Nm of rotational moment). The observed trend was in agreement with a parallel experimental study that initial plantar flexion decreased the incidence of syndesmotic injury compared to a neutral foot. A mechanism of distributing even loads across ligaments subjected to combined foot rotations was identified. This mechanism is potential to obtain the maximum load-bearing capability of a foot and ankle while minimizing the injury severity of ligaments. Such improved understanding of ligament injuries in athletes is necessary to facilitate injury management by clinicians and countermeasure development by biomechanists.

Transient and long-time kinetic responses of the cadaveric leg during internal and external foot rotation.

The purpose of this study was to determine the long-time and transient characteristics of the moment generated by external (ER) and internal (IR) rotation of the calcaneus with respect to the tibia. Two human cadaver legs were disarticulated at the knee joint while maintaining the connective tissue between the tibia and fibula. An axial rotation of 21° was applied to the proximal tibia to generate either ER or IR while the fibula was unconstrained and the calcaneus was permitted to translate in the transverse plane. These boundary conditions were intended to allow natural motion of the fibula and for the effective applied axis of rotation to move relative to the ankle and subtalar joints based on natural articular motions among the tibia, fibula, talus, and calcaneus. A load cell at the proximal tibia measured all components of force and moment. A quasi-linear model of the moment along the tibia axis was developed to determine the transient and long-time loads generated by this ER/IR. Initially neutral, everted, inverted, dorsiflexed, and plantarflexed foot orientations were tested. For the neutral position, the transient elastic moment was 16.5N-m for one specimen and 30.3N-m for the other in ER with 26.3 and 32.1N-m in IR. The long-time moments were 5.5 and 13.2N-m (ER) and 9.0 and 9.5N-m (IR). These loads were found to be transient over time similar to previous studies on other biological structures where the moment relaxed as time progressed after the initial ramp in rotation.

Computational investigation of the effects of knee airbag design on the interaction with occupant lower extremity in frontal and oblique impacts.

OBJECTIVE: The lower extremity of the occupant represents the most frequently injured body region in motor vehicle crashes. Knee airbags (KABs) have been implemented as a potential countermeasure to reduce lower extremity injuries. Despite the increasing prevalence of KABs in vehicles, the biomechanical interaction of the human lower extremity with the KAB has not been well characterized. This study uses computational models of the human body and KABs to explore how KAB design may influence the impact response of the occupant's lower extremities. METHODS: The analysis was conducted using a 50th percentile male occupant human body model with deployed KABs in a simplified vehicle interior. The 2 common KAB design types, bottom-deploy KAB (BKAB) and rear-deploy KAB (RKAB), were both included. A state-of-the-art airbag modeling technique, the corpuscular particle method, was adopted to represent the deployment dynamics of the unfolding airbags. Validation of the environment model was performed based on previously reported test results. The kinematic responses of the occupant lower extremities were compared under both KAB designs, 2 seating configurations (in-position and out-of-position), and 3 loading conditions (static, frontal, and oblique impacts). A linear statistical model was used to assess factor significance considering the impact responses of the occupant lower extremities. RESULTS: The presence of a KAB had a significant influence on the lower extremity kinematics compared to no KAB (P <.05) by providing early restraint and distributing contact force on the legs during airbag deployment. For in-position occupants, the KAB generally tended to decrease tibia loadings. The RKAB led to greater lateral motion of the legs compared to the BKAB, resulting in higher lateral displacement at the knee joint and abduction angle change (51.2 ± 21.7 mm and 15° ± 6.0°) over the dynamic loading conditions. Change in the seating position led to a significant difference in occupant kinematic and kinetic parameters (P <.05). For the out-of-position (forward-seated) occupant, the earlier contact between the lower extremity and the deploying KAB resulted in 28.4° ± 5.8° greater abduction, regardless of crash scenarios. Both KAB types reduced the axial force in the femur relative to no KAB. Overall, the out-of-position occupant sustained a raised axial force and bending moment of the tibia by 0.8 ± 0.2 kN and 21.1 ± 8.7 Nm regardless of restraint use. CONCLUSIONS: The current study provided a preliminary computational examination on KAB designs based on a limited set of configurations in an idealized vehicle interior. Results suggested that the BKAB tended to provide more coverage and less leg abduction compared to the RKAB in oblique impact and/or the selected out-of-position scenario. An out-of-position occupant was associated with larger abduction and lower extremity loads over all occupant configurations. Further investigations are recommended to obtain a full understanding of the KAB performance in a more realistic vehicle environment.

Determination of the in situ mechanical behavior of ankle ligaments.

The mechanical behavior of ankle ligaments at the structural level can be characterized by force-displacement curves in the physiologic phase up to the initiation of failure. However, these properties are difficult to characterize in vitro due to the experimental difficulties in replicating the complex geometry and non-uniformity of the loading state in situ. This study used a finite element parametric modeling approach to determine the in situ mechanical behavior of ankle ligaments at neutral foot position for a mid-sized adult foot from experimental derived bony kinematics. Nine major ankle ligaments were represented as a group of fibers, with the force-elongation behavior of each fiber element characterized by a zero-force region and a region of constant stiffness. The zero-force region, representing the initial tension or slackness of the whole ligament and the progressive fiber uncrimping, was identified against a series of quasi-static experiments of single foot motion using simultaneous optimization. A range of 0.33-3.84mm of the zero-force region was obtained, accounting for a relative length of 6.7±3.9%. The posterior ligaments generally exhibit high-stiffness in the loading region. Following this, the ankle model implemented with in situ ligament behavior was evaluated in response to multiple loading conditions and proved capable of predicting the bony kinematics accurately in comparison to the cadaveric response. Overall, the parametric ligament modeling demonstrated the feasibility of linking the gross structural behavior and the underlying bone and ligament mechanics that generate them. Determination of the in situ mechanical properties of ankle ligaments provides a better understanding of the nonlinear nature of the ankle joint. Applications of this knowledge include functional ankle joint mechanics and injury biomechanics.