Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Recent research has suggested a possible link between sports-related concussions and neurodegenerative processes, highlighting the importance of developing methods to accurately quantify head impact tolerance. The use of kinematic parameters of the head to predict brain injury has been suggested because they are indicative of the inertial response of the brain. The objective of this study is to characterize the rotational kinematics of the head associated with concussive impacts using a large head acceleration dataset collected from human subjects. The helmets of 335 football players were instrumented with accelerometer arrays that measured head acceleration following head impacts sustained during play, resulting in data for 300,977 sub-concussive and 57 concussive head impacts. The average sub-concussive impact had a rotational acceleration of 1230 rad/s2 and a rotational velocity of 5.5 rad/s, while the average concussive impact had a rotational acceleration of 5022 rad/s2 and a rotational velocity of 22.3 rad/s. An injury risk curve was developed and a nominal injury value of 6383 rad/s2 associated with 28.3 rad/s represents 50% risk of concussion. These data provide an increased understanding of the biomechanics associated with concussion and they provide critical insight into injury mechanisms, human tolerance to mechanical stimuli, and injury prevention techniques.

/pdf/rotational-head-kinematics-in-football-impacts-an-injury-2swmxzfyn0.pdf

Rotational Head Kinematics in Football Impacts: An Injury Risk Function for Concussion

Rotational motion of the head as a mechanism for brain injury was proposed back in the 1940s. Since then a multitude of research studies by various institutions were conducted to confirm/reject this hypothesis. Most of the studies were conducted on animals and concluded that rotational kinematics experienced by the animal's head may cause axonal deformations large enough to induce their functional deficit. Other studies utilized physical and mathematical models of human and animal heads to derive brain injury criteria based on deformation/pressure histories computed from their models. This study differs from the previous research in the following ways: first, it uses two different detailed mathematical models of human head (SIMon and GHBMC), each validated against various human brain response datasets; then establishes physical (strain and stress based) injury criteria for various types of brain injury based on scaled animal injury data; and finally, uses Anthropomorphic Test Devices (ATDs) (Hybrid III 50th Male, Hybrid III 5th Female, THOR 50th Male, ES-2re, SID-IIs, WorldSID 50th Male, and WorldSID 5th Female) test data (NCAP, pendulum, and frontal offset tests) to establish a kinematically based brain injury criterion (BrIC) for all ATDs. Similar procedures were applied to college football data where thousands of head impacts were recorded using a six degrees of freedom (6 DOF) instrumented helmet system. Since animal injury data used in derivation of BrIC were predominantly for diffuse axonal injury (DAI) type, which is currently an AIS 4+ injury, cumulative strain damage measure (CSDM) and maximum principal strain (MPS) were used to derive risk curves for AIS 4+ anatomic brain injuries. The AIS 1+, 2+, 3+, and 5+ risk curves for CSDM and MPS were then computed using the ratios between corresponding risk curves for head injury criterion (HIC) at a 50% risk. The risk curves for BrIC were then obtained from CSDM and MPS risk curves using the linear relationship between CSDM - BrIC and MPS - BrIC respectively. AIS 3+, 4+ and 5+ field risk of anatomic brain injuries was also estimated using the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS) database for crash conditions similar to the frontal NCAP and side impact conditions that the ATDs were tested in. This was done to assess the risk curve ratios derived from HIC risk curves. The results of the study indicated that: (1) the two available human head models - SIMon and GHBMC - were found to be highly correlated when CSDMs and max principal strains were compared; (2) BrIC correlates best to both - CSDM and MPS, and rotational velocity (not rotational acceleration) is the mechanism for brain injuries; and (3) the critical values for angular velocity are directionally dependent, and are independent of the ATD used for measuring them. The newly developed brain injury criterion is a complement to the existing HIC, which is based on translational accelerations. Together, the two criteria may be able to capture most brain injuries and skull fractures occurring in automotive or any other impact environment. One of the main limitations for any brain injury criterion, including BrIC, is the lack of human injury data to validate the criteria against, although some approximation for AIS 2+ injury is given based on the angular velocities calculated at 50% probability of concussion in college football players instrumented with 5 DOF helmet system. Despite the limitations, a new kinematic rotational brain injury criterion - BrIC - may offer a way to capture brain injuries in situations when using translational accelerations based HIC alone may not be sufficient.

/pdf/development-of-brain-injury-criteria-bric-2ki69urs47.pdf

Development of Brain Injury Criteria (BrIC)

Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

/pdf/brain-injury-prediction-assessing-the-combined-probability-10frx8age8.pdf

Brain Injury Prediction: Assessing the Combined Probability of Concussion Using Linear and Rotational Head Acceleration

This study is aimed to develop a next-generation, high quality, extensively validated finite element (FE) human head model for enhanced head injury prediction and prevention. The geometry of the model was based on CT and MRI scans of an adult male. A new feature-based multi-block technique was adopted to develop hexahedral brain meshes including the cerebrum, cerebellum, brainstem, corpus callosum, ventricles, and thalamus. Conventional meshing methods were used to create the bridging veins, cerebrospinal fluid (CSF), skull, facial bones, flesh, skin, and membranes - including falx, tentorium, pia, arachnoid, and dura. The head model has 270,552 elements in total. A total of 49 loading cases were selected from a range of experimental and real world head impacts to check the robustness of the model predictions based on responses including the brain pressure, relative skull-brain motion, intracranial strain, skull response, facial response, and bridging vein elongation. The brain pressure was validated against intracranial pressure data reported by Nahum et al. (1977) and Trosseille et al. (1992). The brain motion was validated against brain displacements under sagittal, coronal, and horizontal blunt impacts performed by Hardy et al. (2001, 2007). The facial bone responses were validated under nasal impact (Nyquist et al., 1986), zygoma and maxilla impact (Allsop et al., 1988). The skull bones were validated under frontal angled impact, vertical impact, and occipital impact (Yoganandan et al., 1995) and frontal horizontal impact (Hodgson et al., 1970). The FE head model was further used to study injury mechanisms and tolerances for brain contusion (Nahum et al., 1976), bridging vein rupture (Depreitere et al., 2006), and brain strains for real-world brain injury cases (Franklyn et al. 2005). Studies from 49 loading cases demonstrated that the FE head model had good biofidelity in predicting head responses under various impact scenarios. Furthermore, tissue-level injury tolerances were proposed. A maximum principal strain of 0.42% was adopted for skull cortical layer fracture and maximum principal stress of 20 MPa was used for skull diploe layer fracture. Additionally, a plastic strain threshold of 1.2% was used for facial bone fracture. Average of 17% of engineering tensile strain indicates bridging vein rupture. For brain contusion, 277 kPa of brain pressure was calculated from reconstruction of one contusion case. Lastly, the high strains predicted by the FE head model match the trend of brain injuries reported in four real-world cases. Language: en

Development of a finite element human head model partially validated with thirty five experimental cases.

High-speed biplane x-ray and neutral density targets were used to examine brain displacement and deformation during impact Relative motion, maximum principal strain, maximum shear strain, and intracranial pressure were measured in thirty-five impacts using eight human cadaver head and neck specimens The effect of a helmet was evaluated During impact, local brain tissue tends to keep its position and shape with respect to the inertial frame, resulting in relative motion between the brain and skull and deformation of the brain The local brain motions tend to follow looping patterns Similar patterns are observed for impact in different planes, with some degree of posterior-anterior and right-left symmetry Peak coup pressure and pressure rate increase with increasing linear acceleration, but coup pressure pulse duration decreases Peak average maximum principal strain and maximum shear are on the order of 009 for CFC 60 Hz data for these tests Peak average maximum principal strain and maximum shear increase with increasing linear acceleration, coup pressure, and coup pressure rate Linear and angular acceleration of the head are reduced with use of a helmet, but strain increases These results can be used for the validation of finite element models of the human head

A Study of the Response of the Human Cadaver Head to Impact

Validation of three-dimensional model-based tibio-femoral tracking during running.

This study investigated the mechanisms of traumatic rupture of the aorta (TRA) Eight unembalmed human cadavers were tested using various dynamic blunt loading modes Impacts were conducted using a 32-kg impactor with a 152-mm face, and high-speed seatbelt pretensioners High-speed biplane x-ray was used to visualize aortic motion within the mediastinum, and to measure deformation of the aorta An axillary thoracotomy approach was used to access the peri-isthmic region to place radiopaque markers on the aorta The cadavers were inverted for testing Clinically relevant TRA was observed in seven of the tests Peak average longitudinal Lagrange strain was 0644, with the average peak for all tests being 0208 +/- 0216 Peak intraluminal pressure of 165 kPa was recorded Longitudinal stretch of the aorta was found to be a principal component of injury causation Stretch of the aorta was generated by thoracic deformation, which is required for injury to occur The presence of atherosclerosis was demonstrated to promote injury The isthmus of the aorta moved dorsocranially during frontal impact and submarining loading modes The aortic isthmus moved medially and anteriorly during impact to the left side The results of this study provide a better understanding of the mechanisms associated with TRA, and can be used for the validation of finite element models developed for the examination and prediction of TRA

Jennifer Bishop

Papers

A Study of the Response of the Human Cadaver Head to Impact

Validation of three-dimensional model-based tibio-femoral tracking during running.

Mechanisms of traumatic rupture of the aorta and associated peri-isthmic motion and deformation.