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The use of finite element models as research tools in biomechanics and orthopedics has grown exponentially over the last 20 years. However, the attention to mesh quality, model validation and appropriate energy balance methods and the reporting of these metrics has not kept pace with the general use of finite element modeling. Therefore, the purpose of this review was to summarize the current state of finite element modeling validation practices from the literature in biomechanics and orthopedics and to present specific methods and criteria limits that can be used as guidelines to assess mesh quality, validate simulation results and address energy balance issues. Of the finite element models reviewed from the literature, approximately 42% of them were not adequately validated, while 95% and 98% of the models did not assess the quality of the mesh or energy balance, respectively. A review of the methods that can be used to assess the quality of a mesh (e.g., aspect ratios, angle idealization and element Jacobians), measure the balance of energies (e.g., hour glass energy and mass scaling), and quantify the accuracy of the simulations (e.g., validation metrics, corridors, statistical techniques) are presented.

The purpose of this study was to develop a multivariate distal radius injury risk prediction model that incorporates dynamic loading variables in multiple directions, and interpret the distal radius failure data in order to establish injury probability thresholds. Repeated impacts with increasing intensity were applied to the distal third of eight human cadaveric radius specimens (mean (SD) age=61.9 (9.7)) until injury occurred. Crack (non-propagating damage) and fracture (specimen separated into at least two fragments) injury events were recorded. Best subsets analysis was performed to find the best multivariate injury risk model. Force-only risk models were also determined for comparison. Cumulative distribution functions were developed from the parameters of a Weibull analysis and the forces and risk scores (i.e., values calculated from the injury risk models) from 10% to 90% probability were calculated. According to the adjusted R(2), variance inflation factor and p-values, the model that best predicted the crack event included medial/lateral impulse, Fz load rate, impact velocity and the natural logarithm of Fz (Adj. R(2)=0.698), while the best predictive model of the fracture event included medial/lateral impulse, impact velocity and peak Fz (Adj. R(2)=0.845). The multivariate models predicted injury risk better than both the Fz-only crack (Adj. R(2)=0.551) and fracture (Adj. R(2)=0.293) models. Risk scores of 0.5 and 0.6 corresponded to 10% failure probability for the crack and fracture events, respectively. The inclusion of medial/lateral impulse and impact velocity in both crack and fracture models, and Fz load rate in the crack model, underscores the dynamic nature of these events. This study presents a method capable of developing a set of distal radius fracture prediction models that can be used in the assessment and development of distal radius injury prevention interventions.

Quantification of segment soft and rigid tissue masses in living people is important for a variety of clinical and biomechanical research applications including wobbling mass modeling. Although Dual-energy X-ray Absorptiometry (DXA) is widely accepted as a valid method for this purpose, the reliability of manual segmentation from DXA scans using custom regions of interest (ROIs) has not been evaluated to date. Upper and lower extremity images of 100 healthy adults who underwent a full body DXA scan in the supine position were manually segmented by 3 measurers independently using custom ROIs. Actual tissue masses (fat mass, lean mass, bone mineral content) of the arm, arm with shoulder, forearm, forearm and hand, thigh, leg, and leg and foot segments were quantified bilaterally from the ROIs. There were significant differences between-measurers, however, percentage errors were relatively small overall (1-5.98%). Intraclass correlation coefficients (ICCs) were very high between and within-measurers, ranging from 0.990 to 0.999 and 0.990 to 1.00 for the upper and lower extremities, respectively, suggesting excellent reliability. Between and within-measurer errors were comparable in general, and differences between the tissue types were small on average (maximum of 42 and 53g for upper and lower extremities, respectively). These results suggest that manual segmentation of DXA images using ROIs is a reliable method of estimating soft and rigid tissues in living people.

Accurate body segment parameter (BSP) information is required for dynamic analyses of motion and the current methods available for obtaining these BSPs have been criticized. The purpose of this study was to determine whether dual energy X-ray absorptiometry (DXA) could accurately measure the BSPs of scanned objects and thus be used as a tool for measuring the BSPs of human subjects. Whole body mass (WBM) of 11 males was measured from a DXA scan and the values were compared to criterion scale-measured values by calculating the mean percent error. Two objects (plastic cylinder, human cadaver leg) were also scanned and DXA measurements of mass, length, centre of mass location (CM) and moment of inertia about the centre of mass ( I CM ) were made using custom software. Criterion BSP measurements were then made and compared to DXA BSP values by calculating the percent error. Criterion I CM measurements of the two objects were made using a pendulum technique and a second criterion I CM calculation was made for the cylinder using a geometric formula. A mean percent error of −1.05% ±1.32% was found for WBM measurements of the human subjects. Errors for the cylinder and cadaver leg were under 3.2% for all BSPs except for I CM when DXA was compared to the pendulum method (14.3% and 8.2% for cylinder and leg, respectively). The errors between DXA and the pendulum method were attributed to uncertainty in the pendulum technique (J. Biomech. 2002, in Review). I CM error of the cylinder when DXA was compared to the geometric calculation was 2.63%. This error, combined with the low errors for all other BSPs, indicated that DXA can be used as a simple and accurate means of obtaining direct BSP information on living humans.

The fundamental nature of impact testing requires a cautious approach to signal processing, to minimize noise while preserving important signal information. However, few recommendations exist regarding the most suitable filter frequency cut-offs to achieve these goals. Therefore, the purpose of this investigation is twofold: to illustrate how residual analysis can be utilized to quantify optimal system-specific filter cut-off frequencies for force, moment, and acceleration data resulting from in-vitro upper extremity impacts, and to show how optimal cut-off frequencies can vary based on impact condition intensity. Eight human cadaver radii specimens were impacted with a pneumatic impact testing device at impact energies that increased from 20 J, in 10 J increments, until fracture occurred. The optimal filter cut-off frequency for pre-fracture and fracture trials was determined with a residual analysis performed on all force and acceleration waveforms. Force and acceleration data were filtered with a dual pass, 4th order Butterworth filter at each of 14 different cut-off values ranging from 60 Hz to 1500 Hz. Mean (SD) pre-fracture and fracture optimal cut-off frequencies for the force variables were 605.8 (82.7) Hz and 513.9 (79.5) Hz, respectively. Differences in the optimal cut-off frequency were also found between signals (e.g. Fx (medial–lateral), Fy (superior–inferior), Fz (anterior–posterior)) within the same test. These optimal cut-off frequencies do not universally agree with the recommendations of filtering all upper extremity impact data using a cut-off frequency of 600 Hz. This highlights the importance of quantifying the filter frequency cut-offs specific to the instrumentation and experimental set-up. Improper digital filtering may lead to erroneous results and a lack of standardized approaches makes it difficult to compare findings of in-vitro dynamic testing between laboratories.

Regression equations for predicting bone mineral content (BMC), fat mass (FM), lean mass (LM), and wobbling mass (WM) of living people from simple anthropometric measures (segment lengths, circumferences, breadths, and skin folds) have been reported in the literature for the lower extremities, but are lacking for the upper extremities. Multiple linear stepwise regression was used to generate such equations for the arm, forearm, and forearm and hand segments of healthy university aged people (38 males, 38 females). Actual tissue masses were obtained from full body Dual-energy X-ray Absorptiometry (DXA) scans and were used to validate the developed equations with an independent sample of 24 participants (12 male, 12 female). Prediction equations exhibited very high adjusted R(2) values (range from 0.854 to 0.968), with more explained variance for LM and WM than for BMC and FM. Scatter plots of actual versus predicted tissue masses revealed a close relationship (R(2) range from 0.681 to 0.951). Relative errors between the predicted and actual tissue masses for the validation group ranged from -2.2% to 15.5%, and the root-mean-squared error (RMS(error)) ranged from 7.92 to 180.26g, for BMC of the forearm and LM of the arm, respectively. These results suggest that accurate estimates of in-vivo tissue masses for the upper extremities can be predicted from simple anthropometric measurements in young adults. Access to tissue masses such as these will enable the development of more accurate models for predicting dynamic in-vivo response of the body to activities involving impact.

Accurate modeling of soft tissue motion effects relative to bone during impact requires knowledge of the mass of soft and rigid tissues in living people. Holmes et al., [2005. Predicting in vivo soft tissue masses of the lower extremity using segment anthropometric measures and DXA. Journal of Applied Biomechanics, 21, 371-382] developed and validated regression equations to predict the individual tissue masses of lower extremity segments of young healthy adults, based on simple anthropometric measurements. However, the reliability of these measurements and the effect on predicted tissue mass estimates from the equations has yet to be determined. In the current study, two measurers were responsible for collecting two sets of unilateral measurements (25 male and 25 female subjects) for the right upper and lower extremities. These included 6 lengths, 6 circumferences, 8 breadths, and 4 skinfold thicknesses. Significant differences were found between measurers and between sexes, but these differences were relatively small in general (75-80% of between-measurer differences were1cm). Within-measurer measurement differences were smaller and more consistent than those between measurers in most cases. Good to excellent reliability was demonstrated for all measurement types, with intra-class correlation coefficients of 0.79, 0.86, 0.85 and 0.86 for lengths, circumferences, breadth and skinfolds, respectively. Predicted tissue mass magnitudes were moderately affected by the measurement differences. The maximum mean errors between measurers ranged from 3.2% to 24.2% for bone mineral content and fat mass, for the leg and foot, and the leg segments, respectively.