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Human Body Models

Rear Impact

  • Katagiri, 20191: Biofidelity Evaluation of GHBMC Male Occupant Models in Rear Impact

The NHTSA Biofidelity Ranking System was used to evaluate the HBMs. The HBMs exhibited better biofidelity 1) at 17 km/h than 24 km/h, and 2) in the head to T1 region, which is relevant to rear-impact-related injuries, than the T1 to pelvis region. The detailed HBM received a better biofidelity score than the simplified HBM in every studied component. Limitations for the HBMs’ biofidelity were indicated in the modelling of their spines and surrounding flesh.

Frontal impact

  • Devane, 20192: Validation of a simplified human body model in relaxed and braced conditions in low-speed frontal sled tests #GHBMC

  • Hu, 20193: Frontal crash simulations using parametric human models representing a diverse population

Frontal crash simulations based on U.S. New Car Assessment Program (U.S. NCAP) were conducted. Body region injury risks were calculated based on the risk curves used in the US NCAP, except that scaling was used for the neck, chest, and knee–thigh–hip injury risk curves based on the sizes of the bony structures in the corresponding body regions. Age effects were also considered for predicting chest injury risk.

Results: The simulations demonstrated that driver stature and body shape affect occupant interactions with the restraints and consequently affect occupant kinematics and injury risks in severe frontal crashes. U-shaped relations between occupant stature/weight and head injury risk were observed. Chest injury risk was strongly affected by age and sex, with older female occupants having the highest risk. A strong correlation was also observed between BMI and knee–thigh–hip injury risk, whereas none of the occupant parameters meaningfully affected neck injury risks.

  • @Schap2018: Objective Evaluation of Whole Body Kinematics in a Simulated, Restrained Frontal Impact

Side Impact

  • Perez-Rapela, 2020[@PerezRapela2020]: Methodology for the Evaluation of Human Response Variability to Intrinsic and Extrinsic Factors Including Uncertainties

  • Hwang, 2020[@Hwang2020]: Diverse Human Body Models against Side Impact Tests with Post-Mortem Human Subjects

    • Dual-sled model validated with SID, SID-II: @Hwang2016 (Development, Evaluation, and Sensitivity Analysis of Parametric Finite Element Whole-Body Human Models in Side Impacts)

With weight, stature, sex, and age of PMHS, seven FE HBMs were developed by morphing the midsize male THUMS model into the target geometries predicted by the statistical skeleton and external body shape models. The model-predicted force histories, accelerations a long the spine, and deflections in the chest and abdomen were compared to the test data. For comparison, simulations in all testing conditions were also conducted with the original midsize male THUMS, and the results from the THUMS simulations were scaled to the weight and stature from each PMHS.

[@Hwang2019]

Oblique Impact

Perez-Rapela, 2019[@Perez-rapela2019] : Comparison of the simplified GHBMC to PMHS kinematics in far-side impact

Results show that, in general, the simplified GHBMC captures lateral excursion in oblique impact conditions but overpredicts in purely lateral impact conditions. The simplified GHBMC shows post-mortem human subject like sensitivities to changes in ΔV and the use of pretensioner but no sensitivity to changes in impact direction. The human body model performs similarly to other previously published HBMs and obtains a “good” CORA score. However, the surrogate does not represent post-mortem human subject shoulder-to-belt interaction in all configurations.

Pedestrian

  • Shi, 20204: Evaluation of injury thresholds for predicting severe head injuries in vulnerable road users resulting from ground impact via detailed accident reconstructions

  • Decker, 20195: Evaluation of finite element human body models for use in a standardized protocol for pedestrian safety assessment

Vulnerable population

  • Larsson, 20196: Evaluation of the Benefits of Parametric Human Body Model Morphing for Prediction of Injury to Elderly Occupants in Side Impact

Side-impact sled tests conducted with these PMHS were recreated by means of simulations with the baseline and morphed HBMs. Results showed that the parametrically morphed models showed improved correlation with PMHS kinematics compared with the baseline HBM predictions and performed as well as the further personalized models. Both parametric and personalized HBMs failed to predict the PMHS chestband deflection magnitudes and predicted no risk for rib fractures. In contrast, both PMHS sustained multiple fractured ribs during testing. In conclusion, parametric HBM morphing alone improved prediction of individual kinematics, but neither morphing method improved individual injury risk prediction.

Active (Muscle) HBM

  • Correia, 20207: Optimization of Muscle Activation Schemes in a Finite Element Neck Model Simulating Volunteer Frontal Impact Scenarios

  • Larsson, 20198: Active Human Body Model Predictions Compared to Volunteer Response in Experiments with Braking, Lane Change, and Combined Manoeuvres

Future Seat Configurations

  • Boyle, 20199: A Human Modelling Study on Occupant Kinematics in Highly Reclined Seats during Frontal Crashes

  • Rawska, 201910: Submarining sensitivity across varied anthropometry in an autonomous driving system environment

Others FE

  • Ye, 202011: Lumbar Spine Response of Computational Finite Element Models in Multidirectional Spaceflight Landing Conditions

Multi-body

  • Kaminishi, 201912: Postural control of a musculoskeletal model against multidirectional support surface translations

  • Modenese, 2020[@Modenese2020]: Automated Generation of Three-Dimensional Complex Muscle Geometries for Use in Personalised Musculoskeletal Models Github

  1. Maika Katagiri, Jay Zhao, Sungwoo Lee, Kevin Moorhouse, and Yun-Seok Kang. Biofidelity Evaluation of GHBMC Male Occupant Models in Rear Impact. In IRCOBI, number September. Florence, Italy, 2019. ↩

  2. Karan Devane, Dale Johnson, and F. Scott Gayzik. Validation of a simplified human body model in relaxed and braced conditions in low-speed frontal sled tests. Traffic Injury Prevention, pages 1–6, sep 2019. doi:10.1080/15389588.2019.1655733. ↩

  3. Jingwen Hu, Kai Zhang, Matthew P. Reed, Jenne-Tai Wang, Mark Neal, and Chin-Hsu Lin. Frontal crash simulations using parametric human models representing a diverse population. Traffic Injury Prevention, 20sup1:S97–S105, jun 2019. doi:10.1080/15389588.2019.1581926. ↩

  4. Liangliang Shi, Yong Han, Hongwu Huang, Johan Davidsson, and Robert Thomson. Evaluation of injury thresholds for predicting severe head injuries in vulnerable road users resulting from ground impact via detailed accident reconstructions. Biomechanics and Modeling in Mechanobiology, mar 2020. doi:10.1007/s10237-020-01312-9. ↩

  5. William Decker, Bharath Koya, Wansoo Pak, Costin D. Untaroiu, and F. Scott Gayzik. Evaluation of finite element human body models for use in a standardized protocol for pedestrian safety assessment. Traffic Injury Prevention, 20sup2:S32–S36, jul 2019. doi:10.1080/15389588.2019.1637518. ↩

  6. Karl-johan Larsson, Bengt Pipkorn, Johan Iraeus, John H Bolte Iv, Amanda M Agnew, Jingwen Hu, Matthew P Reed, and Cecilia Sunnevång. Evaluation of the Benefits of Parametric Human Body Model Morphing for Prediction of Injury to Elderly Occupants in Side Impact. In IRCOBI, volume 46, 150–174. Florence, Italy, 2019. ↩

  7. Matheus A. Correia, Stewart D. McLachlin, and Duane S. Cronin. Optimization of muscle activation schemes in a finite element neck model simulating volunteer frontal impact scenarios. Journal of Biomechanics, pages 109754, mar 2020. doi:10.1016/j.jbiomech.2020.109754. ↩

  8. Emma Larsson, Johan Iraeus, Jason Fice, Bengt Pipkorn, Lotta Jakobsson, Erik Brynskog, Karin Brolin, and Johan Davidsson. Active human body model predictions compared to volunteer response in experiments with braking, lane change, and combined manoeuvres. In IRCOBI. 2019. ↩

  9. Kyle J Boyle, Matthew P Reed, Lauren W Zaseck, and Jingwen Hu. A human modelling study on occupant kinematics in highly reclined seats during frontal crashes. In IRCOBI. 2019. ↩

  10. Katarzyna Rawska, Bronislaw Gepner, Shubham Kulkarni, Kalle Chastain, Junjun Zhu, Rachel Richardson, Daniel Perez-rapela, Jason Forman, R Kerrigan, Junjun Zhu, Rachel Richardson, Daniel Perez-rapela, Jason Forman, Jason R Kerrigan, Katarzyna Rawska, Bronislaw Gepner, Shubham Kulkarni, Kalle Chastain, Junjun Zhu, and Rachel Richardson. Submarining sensitivity across varied anthropometry in an autonomous driving system environment. Traffic Injury Prevention, 00:1–5, 2019. URL: https://doi.org/10.1080/15389588.2019.1655734, doi:10.1080/15389588.2019.1655734. ↩

  11. Xin Ye, Derek A. Jones, James P. Gaewsky, Bharath Koya, Kyle P. McNamara, Mona Saffarzadeh, Jacob B. Putnam, Jeffrey T. Somers, F. Scott Gayzik, Joel D. Stitzel, and Ashley A. Weaver. Lumbar spine response of computational finite element models in multidirectional spaceflight landing conditions. Journal of Biomechanical Engineering, jan 2020. doi:10.1115/1.4045401. ↩

  12. Kohei Kaminishi, Ping Jiang, Ryosuke Chiba, Kaoru Takakusaki, and Jun Ota. Postural control of a musculoskeletal model against multidirectional support surface translations. PLOS ONE, 143:e0212613, mar 2019. doi:10.1371/journal.pone.0212613. ↩