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

OCCUPATIONAL APPLICATIONS Military 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. TECHNICAL ABSTRACT Background: The use of night vision goggles (NVGs) has been linked to a high prevalence of neck pain and injury in military helicopter pilots. Next generation helmet designs aim to mitigate NVG related consequences on cervical spine loading. Currently, in vivo human-participant experiments are required to collect necessary data, such as electromyography (EMG) to estimate joint contact forces in the cervical spine as a result of unique helmet designs. This is costly and inefficient. Digital human models, which provide inverse dynamics, coupled with artificial neural networks (ANNs), can provide a surrogate for musculoskeletal joint modeling to predict joint contact forces. Purpose: We developed ANNs to predict C6-C7 compression and anteroposterior shear during flight-relevant head movements with sufficient sensitivity to differentiate between candidate helmet designs in terms of associated biomechanical exposures. Methods: Motion capture and EMG data were collected from 26 participants who performed flight-relevant reciprocal head movements about pitch and yaw axes while donning one of four helmet configurations. These data were input into an EMG-driven musculoskeletal model of the neck to generate time series of C6-C7 compression and shear. Rotation-specific ANNs were trained to predict the EMG-driven model outputs, given only the headtrunk kinematics and C6-C7 moments as inputs. Results: ANNs for pitch rotations were successful in estimating peak and cumulative compression and shear, with an absolute error that was lower than absolute differences in joint contact forces between relevant helmet conditions. ANNs for yaw rotations were similarly successful in differentiating between C6-C7 compression and cumulative C6-C7 shear, but less so for peak C6-C7 shear. Conclusions: When combined with biomechanical data readily available from digital human modeling software, use of an ANN surrogate for joint musculoskeletal modeling can permit evaluation of joint contact forces associated with novel helmet designs during upstream design. Improved consideration of joint contact forces during a virtual helmet design process will assist in identifying helmet designs that reduce biomechanical exposures of the cervical spine during helicopter flight.

Automated summary

This study developed artificial neural networks to predict cervical spine compression and shear during helicopter flight, aiming to reduce neck pain risks associated with the use of night vision goggles by pilots. By utilizing data from digital human models to predict the effects of novel helmet designs on the cervical spine, this approach allows for early evaluation of biomechanical exposures and identification of helmet designs that can decrease neck pressure.