Abstract
Computational human body models (HBMs) of drivers for pre-crash simulations need active shoulder muscle control, and volunteer data are lacking. The goal of this paper was to build shoulder muscle dynamic spatial tuning patterns, with a secondary focus to present shoulder kinematic evaluation data. 8M and 9F volunteers sat in a driver posture, with their torso restrained, and were exposed to upper arm dynamic perturbations in eight directions perpendicular to the humerus. A dropping 8-kg weight connected to the elbow through pulleys applied the loads; the exact timing and direction were unknown. Activity in 11 shoulder muscles was measured using surface electrodes, and upper arm kinematics were measured with three cameras. We found directionally specific muscle activity and presented dynamic spatial tuning patterns for each muscle separated by sex. The preferred directions, i.e. the vector mean of a spatial tuning pattern, were similar between males and females, with the largest difference of 31° in the pectoralis major muscle. Males and females had similar elbow displacements. The maxima of elbow displacements in the loading plane for males was 189 ± 36 mm during flexion loading, and for females, it was 196 ± 36 mm during adduction loading. The data presented here can be used to design shoulder muscle controllers for HBMs and evaluate the performance of shoulder models.
Highlights
The computational human body models (HBMs) used to improve automotive safety increasingly include feedback-controlled active muscles (Östh et al, 2015b; Kato et al, 2018; Devane et al, 2019; Larsson et al, 2019; Wochner et al, 2019)
The spatial tuning patterns recorded for both sexes were visually confirmed to be unimodal, and directionally dependent activity was generally seen for each muscle (Figure 4)
We found that muscle activity varied with direction for all 11 shoulder muscles studied
Summary
The computational human body models (HBMs) used to improve automotive safety increasingly include feedback-controlled active muscles (Östh et al, 2015b; Kato et al, 2018; Devane et al, 2019; Larsson et al, 2019; Wochner et al, 2019). Current HBM shoulder muscle controllers rely on anatomical descriptions of muscle lines of action to derive muscle load sharing patterns (Kato et al, 2018; Devane et al, 2019), but this may not reflect how humans use their shoulder muscles. In current shoulder muscle controllers, intermuscular load sharing is determined by Shoulder Dynamic Spatial Tuning Patterns
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