  This study investigates the effects of anthropometric attributes, biological
sex, and posture on translational body kinematic responses in translational
vibrations. In total, 35 participants were recruited. Perturbations were
applied on a standard car seat using a motion-based platform with 0.1 to 12.0
Hz random noise signals, with 0.3 m/s2 rms acceleration, for 60 seconds.
Multiple linear regression models (three basic models and one advanced model,
including interactions between predictors) were created to determine the most
influential predictors of peak translational gains in the frequency domain per
body segment (pelvis, trunk, and head). The models introduced experimentally
manipulated factors (motion direction, posture, measured anthropometric
attributes, and biological sex) as predictors. Effects of included predictors
on the model fit were estimated. Basic linear regression models could explain
over 70% of peak body segments' kinematic body response (where the R2 adjusted
was 0.728). The inclusion of additional predictors (posture, body height and
weight, and biological sex) did enhance the model fit, but not significantly
(R2 adjusted was 0.730). The multiple stepwise linear regression, including
interactions between predictors, accounted for the data well with an adjusted
R2 of 0.907. The present study shows that perturbation direction and body
segment kinematics are crucial factors influencing peak translational gains.
Besides the body segments' response, perturbation direction was the strongest
predictor. Adopted postures and biological sex do not significantly affect
kinematic responses.
