A systems-level perspective of the biomechanics of the trunk flexion-extension movement: Part I – Normal low back condition

Most of the previous studies of the lumbar region have not considered the influence of pelvic and lower extremity characteristics on the performance of the lumbar region. The goal of the current study was to explore these more systems-level effects by assessing the effects of a pelvic/lower extremity constraint on the biomechanical response of the lumbar spine in an in-vivo experiment. Twelve participants performed full range of motion, sagittal-plane trunk flexion-extension movements under two conditions: unconstrained stoop movement and pelvic/lower extremity constrained stoop movement (six repetitions in each condition over three days). Kinematics and muscle activities of the trunk and lower extremity muscles were monitored. Results showed a significant effect of pelvic/lower-extremity constraint on a number of lumbar performance measures. Trunk flexion angle was, as expected, significantly reduced with the lower extremity constraints (81° (free stoop) vs. 56° (lower extremity constrained)). At a more local level, there was a 6.4% greater peak lumbar flexion angle and a 9.1% increase in the lumbar angle at which the trunk extensor musculature demonstrated flexion-relaxation in the constrained stooping condition as compared to the unconstrained stooping condition. Also, the EMG of the L3/L4 paraspinals was greater in the restricted stooping as compared to the free stooping (16.3% MVC vs. 15.1% MVC). Relevance to industry Low back injuries are a significant challenge to many industries and developing accurate models of spinal stress at full stooping postures can help in the development of appropriate interventions to reduce prevalence

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

Introduction: Military helicopter pilots around the globe experience a high prevalence of neck pain. The requirement for pilots to use night vision goggles (NVGs) has been linked to increases in neck pain and injury prevalence. As a result, next generation helmet designs aim to offset or mitigate NVG-related consequences on cervical spine loading. However, in vivo human-participant experiments are currently required to collect necessary data (e.g., electromyography) to estimate joint contact forces on the cervical spine associated with unique helmet designs. This is costly, and inefficient. Thus, a more time and resource-efficient approach is required. A digital human modelling approach wherein multi-body dynamics (MBD) models, which provide inverse dynamics, are combined with artificial neural networks (ANNs) can provide a surrogate for more costly musculoskeletal joint modeling to predict joint contact forces. Objective: To develop ANNs to predict cervical spine compression and shear, given inputs available through MBD modelling, with enough sensitivity to differentiate between compression and shear exposures associated with different helicopter helmet designs. Methods: ANNs with systematically varied inputs and parameters were developed to predict cervical spine compression and shear given head-trunk kinematics and C6-C7 neck joint moments, data readily available from digital human models. ANN development was driven by a previously collected and processed dataset. Motion capture and electromyography 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 electromyography-driven musculoskeletal model of the neck to generate time series C6-C7 compression and shear outputs. ANNs were trained to predict the electromyography-driven model compression and shear outputs given only the head-trunk kinematics and C6-C7 moments as inputs. Results: Rotation-specific (i.e., yaw and pitch) ANNs yielded stronger predictive performance than ANNs that generalized to both pitch and yaw axes of rotation. ANNs for pitch rotations accurately predicted peak and cumulative compression and shear outputs 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 predicting cumulative C6-C7 compression and shear where absolute error was lower than corresponding differences between relevant helmet conditions. However, they were unable to do so for peak C6-C7 compression and 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 in the cervical spine associated with novel helmet design concepts during upstream design. Improved consideration of joint contact forces during a computer-aided helmet design process will assist in identifying helmet designs that reduce the biomechanical exposures of the cervical spine during helicopter flight

Refining the relationship between the mechanical demands on the spine and injury mechanisms through improved estimates of load exposure and tissue tolerance

The low back loading to which an individual is exposed has been linked to injury and the reporting of low back pain. Despite extensive research on the spine and workplace loading exposures, statistics indicate that efforts to date have not led to large reductions in the reporting of these injuries. One possible cause for the apparent ineffectiveness of interventions may be a poorly defined understanding of the mechanical exposures of the spine during work related activities. There are sophisticated models that can predict spine loads and are responsive to how an individual moves and uses their muscles, however the models are complex and require extensive data collection to be implemented. This fact has prevented these models from being employed in industrial settings and the simplified surrogate methods that are being employed may not be predicting load exposures well. Therefore, this work focused on examining surrogate methods that can produce estimates of spine loading equal to our most complex laboratory based models. In addition, our understanding of spine tolerance to combined motion and load has been based upon in-vitro work that has not accurately represented coupled physiologic compression and flexion or has not investigated potentially beneficial loading scenarios. The result has been a lack of clear data indicating when motion should be treated as the primary influence in injury development or when load is the likely injury causing exposure. As a result, research was conducted to determine the interplay between load and motion in cumulative injury development, as well as investigating the potential of static rest periods in mitigating the effects of cumulative compression. Study one examined the potential utility of artificial neural networks as a data reduction approach in obtaining estimates of time-varying loads and moments equal in magnitude to those of EMG-assisted and rigid link models. It was found that the neural network approach under predicted peak force and moment exposures, but produced strong predictions of average and cumulative exposures. Therefore this method may be a viable approach to document cumulative loads in industrial settings. Study two compared the load and moment estimates from a currently employed, posture match based ergonomic assessment tool (3DMatch) to those obtained with an EMG-assisted model and those predicted with a rigid link modeling approach. The results indicated that 3DMatch over predicted peak moments and cumulative compression. However, simple correction approaches were developed which can adjust the predictions to obtain more physiologic estimates. Study three employed flexion/extension motion with repetitive compression loading profiles in an in-vitro study, with both load and motion profiles being obtained from measures in study 1. It was found that at loads above 30% of a spine’s compressive tolerance, repetitive flexion/extension would not lead to intervertebral disc injury prior to an endplate or vertebral fracture occurring. However, as loads fall below 30% the likelihood of experiencing a herniation increases, while the overall likelihood of an injury occurring decreases. Comparison to relevant studies indicated that while repetitive flexion did not alter the site of injury it appeared to degrade the ability of the spine to tolerate compression. Finally, study four employed dynamic compression while the spine was maintained in a neutral posture to investigate the effects of ‘rest’, or periods of static low level loading, on altering the amount of load tolerated prior to injury. It was found that there was a non-linear relationship between load magnitude and compressive tolerance, with increasing load magnitude exposures leading to decreasing cumulative load tolerances. Periods of low level static loading did not alter the resistance of the spinal unit to cumulative compression or impact the number of cycles tolerated to failure. In summary, this work has examined methods that may allow for better predictions of spine loading in the workplace without the large data demands of sophisticated laboratory approaches. Where possible, suggestions for optimal implementation of these surrogates have been developed. Additionally, in-vitro work has indicated a load threshold of 30%, above which herniation is not likely to occur during dynamic repetitive loading. Furthermore, the insertion of static rest periods into dynamic loading scenarios did not improve the spine’s failure tolerance to loading, indicating that care should be exercised when determining optimal loading paradigms. In combination, the applied methods that have been developed and the information regarding injury development that has been obtained will help to refine our understanding of the exposures and tolerances that define mechanical injury in the spine