Citation

Devane K, Johnson D, Gayzik FS. Traffic Injury Prev. 2019; 20(8): 832-837.

Affiliation

Center for Injury Biomechanics, Virginia Tech-Wake Forest University , Winston-Salem , North Carolina.

Copyright

(Copyright © 2019, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2019.1655733

PMID

31549531

Abstract

Objective: The goal of this study was to implement active musculature into the Global Human Body Models Consortium (GHBMC) average male simplified occupant model (M50-OS v2) and validate its performance in low-speed frontal crash scenarios. Methods: Volunteer and postmortem human subjects (PMHS) data from low-speed frontal sled tests by Beeman et al., including 2.5 and 5.0 g acceleration pulses, were used to simulate events in LS-DYNA. All muscles were modeled as 1D beam elements and assigned a Hill-type muscle material. From the output of proportional-integral-derivative (PID) controllers, the activation level for each muscle was calculated using a sigmoid function, representing the firing rate of motor neurons. The PID controller attempts to preserve the initial posture of the model. Percentage muscle contribution for all skeletal muscles was precalculated using the M50-OS with active muscles (M50-OS + Active). The M50-OS + Active employs varying levels of neural delays to represent volunteer relaxed and braced conditions, taken from literature. Braced condition experiments were simulated using elevated joint angle set values for the PID controller. The M50-OS + Active model was used to simulate 2 muscle conditions (relaxed and braced) at 2 pulse severities (2.5 and 5.0 g). A control set of simulations was conducted to compare the effect of adding active muscle. Ten whole-body simulations were conducted. Results: The results from volunteer simulations showed a strong dependence of reaction loads and kinematics on muscle activation. Compared to baseline, M50-OS, at 5.0 g acceleration, 33.3% and 7.6% decreases were observed in the overall head kinematics of the M50-OS + Active for the braced and relaxed conditions, respectively. Regarding the anterior direction, similar reductions in overall kinematics were observed for both volunteer test conditions. In comparison to control simulations in which no active muscle was implemented, objective evaluation scores increased markedly at both speeds for the braced condition. Little to no gain was found in the relaxed condition. Conclusions: The results justify the need for use of an active human body model for predicting low-speed frontal kinematics, particularly in the braced condition. Head kinematics were reduced when using active modeling for all simulations (braced and relaxed).

Language: en

Keywords

Active muscle; GHBMC; biomechanics; computational modeling; human modeling