Objective:​ Objective evaluation methods of time history signals are used to quantify how well simulated human body responses match experimental data. As the use of simulations grows in the field of biomechanics, there is a need to establish standard approaches for comparisons. There are 2 aims of this study. The first is to apply 3 objective evaluation methods found in the literature to a set of data from a human body finite element model. The second is to compare the results of each method, examining how they are correlated to each other and the relative strengths and weaknesses of the algorithms.

Methods:​ In this study, the methods proposed by Sprague and Geers (magnitude and phase error, SGM and SGP), Rhule et al. (cumulative standard deviation, CSD), and Gehre et al. (CORrelation and Analysis, or CORA, size, phase, shape, corridor) were compared. A 40 kph frontal sled test presented by Shaw et al. was simulated using the Global Human Body Models Consortium midsized male full-body finite element model (v. 3.5). Mean and standard deviation experimental data (n = 5) from Shaw et al. were used as the benchmark. Simulated data were output from the model at the appropriate anatomical locations for kinematic comparison. Force data were output at the seat belts, seat pan, knee, and foot restraints.

Results:​ Objective comparisons from 53 time history data channels were compared to the experimental results. To compare the different methods, all objective comparison metrics were cross-plotted and linear regressions were calculated. The following ratings were found to be statistically significantly correlated (P < .01):​ SGM and CORrelation and Analysis (CORA) size, R² = 0.73;​ SGP and CORA shape, R² = 0.82;​ and CSD and CORA's corridor factor, R² = 0.59. Relative strengths of the correlated ratings were then investigated. For example, though correlated to CORA size, SGM carries a sign to indicate whether the simulated response is greater than or less than the benchmark signal. A further analysis of the advantages and drawbacks of each method is discussed.

Conclusions:​ The results demonstrate that a single metric is insufficient to provide a complete assessment of how well the simulated results match the experiments. The CORA method provided the most comprehensive evaluation of the signal. Regardless of the method selected, one primary recommendation of this work is that for any comparison, the results should be reported to provide separate assessments of a signal's match to experimental variance, magnitude, phase, and shape. Future work planned includes implementing any forthcoming International Organization for Standardization standards for objective evaluations.