Predicting Cervical Spine Compression and Shear in Helicopter Helmeted Conditions Using Artificial Neural Networks

OCCUPATIONAL APPLICATIONSMilitary helicopter pilots around the globe are at high risk of neck pain related to their use of helmet-mounted night vision goggles. Unfortunately, it is difficult to design alternative helmet configurations that reduce the biomechanical exposures on the cervical spine during flight because the time and resource costs associated with assessing these exposures in vivo are prohibitive. Instead, we developed artificial neural networks (ANNs) to predict cervical spine compression and shear given head-trunk kinematics and joint moments in the lower neck, data readily available from digital human models. The ANNs detected differences in cervical spine compression and anteroposterior shear between helmet configuration conditions during flight-relevant head movement, consistent with results from a detailed model based on in vivo electromyographic data. These ANNs may be useful in helping to prevent neck pain related to military helicopter flight by facilitating virtual biomechanical assessment of helmet configurations upstream in the design process.