Jonathan S. Dufour

An EMG-assisted model calibration technique that does not require MVCs

As personalized biologically-assisted models of the spine have evolved, the normalization of raw electromyographic (EMG) signals has become increasingly important. The traditional method of normalizing myoelectric signals, relative to measured maximum voluntary contractions (MVCs), is susceptible to error and is problematic for evaluating symptomatic low back pain (LBP) patients. Additionally, efforts to circumvent MVCs have not been validated during complex free-dynamic exertions. Therefore, the objective of this study was to develop an MVC-independent biologically-assisted model calibration technique that overcomes the limitations of previous normalization efforts, and to validate this technique over a variety of complex free-dynamic conditions including symmetrical and asymmetrical lifting. The newly developed technique (non-MVC) eliminates the need to collect MVCs by combining gain (maximum strength per unit area) and MVC into a single muscle property (gain ratio) that can be determined during model calibration. Ten subjects (five male, five female) were evaluated to compare gain ratio prediction variability, spinal load predictions, and model fidelity between the new non-MVC and established MVC-based model calibration techniques. The new non-MVC model calibration technique demonstrated at least as low gain ratio prediction variability, similar spinal loads, and similar model fidelity when compared to the MVC-based technique, indicating that it is a valid alternative to traditional MVC-based EMG normalization. Spinal loading for individuals who are unwilling or unable to produce reliable MVCs can now be evaluated. In particular, this technique will be valuable for evaluating symptomatic LBP patients, which may provide significant insight into the underlying nature of the LBP disorder.

A biologically-assisted curved muscle model of the lumbar spine: Model structure.

BACKGROUND Biomechanical models have been developed to assess the spine tissue loads of individuals. However, most models have assumed trunk muscle lines of action as straight-lines, which might be less reliable during occupational tasks that require complex lumbar motions. The objective of this study was to describe the model structure and underlying logic of a biologically-assisted curved muscle model of the lumbar spine. METHODS The developed model structure including curved muscle geometry, separation of active and passive muscle forces, and personalization of muscle properties was described. An example of the model procedure including data collection, personalization, and data evaluation was also illustrated. FINDINGS Three-dimensional curved muscle geometry was developed based on a predictive model using magnetic resonance imaging and anthropometric measures to personalize the model for each individual. Calibration algorithms were able to reverse-engineer personalized muscle properties to calculate active and passive muscle forces of each individual. INTERPRETATION This biologically-assisted curved muscle model will significantly increase the accuracy of spinal tissue load predictions for the entire lumbar spine during complex dynamic occupational tasks. Personalized active and passive muscle force algorithms will help to more robustly investigate person-specific muscle forces and spinal tissue loads.

A biologically-assisted curved muscle model of the lumbar spine: Model validation

BACKGROUND Biomechanical models have been developed to predict spinal loads in vivo to assess potential risk of injury in workplaces. Most models represent trunk muscles with straight-lines. Even though straight-line muscles behave reasonably well in simple exertions, they could be less reliable during complex dynamic exertions. A curved muscle representation was developed to overcome this issue. However, most curved muscle models have not been validated during dynamic exertions. Thus, the objective of this study was to investigate the fidelity of a curved muscle model during complex dynamic lifting tasks, and to investigate the changes in spine tissue loads. METHODS Twelve subjects (7 males and 5 females) participated in this study. Subjects performed lifting tasks as a function of load weight, load origin, and load height to simulate complex exertions. Moment matching measures were recorded to evaluate how well the model predicted spinal moments compared to measured spinal moments from T12/L1 to L5/S1 levels. FINDINGS The biologically-assisted curved muscle model demonstrated better model performance than the straight-line muscle model between various experimental conditions. In general, the curved muscle model predicted at least 80% of the variability in spinal moments, and less than 15% of average absolute error across levels. The model predicted that the compression and anterior-posterior shear load significantly increased as trunk flexion increased, whereas the lateral shear load significantly increased as trunk twisted more asymmetric during lifting tasks. INTERPRETATION A curved muscle representation in a biologically-assisted model is an empirically reasonable approach to accurately predict spinal moments and spinal tissue loads of the lumbar spine.

Association between spinal loads and the psychophysical determination of maximum acceptable force during pushing tasks

The objective of this study was to investigate potential associations between an individuals psychophysical maximum acceptable force (MAF) during pushing tasks and biomechanical tissue loads within the lumbar spine. Ten subjects (eight males, two females) pushed a cart with an unknown weight at one push every two minute for a distance of 3.9 m. Two independent variables were investigated, cart control and handle orientation while evaluating their association with the MAF. Dependent variables of hand force and tissue loads for each MAF determination and preceding push trial were assessed using a validated, electromyography-assisted biomechanical model that calculated spinal load distribution throughout the lumbar spine. Results showed no association between spinal loads and the MAF. Only hand forces were associated with the MAF. Therefore, MAFs may be dependent upon tactile sensations from the hands, not the loads on the spine and thus may be unrelated to risk of low back injury. Practitioner Summary: Pushing tasks have become common in manual materials handling (MMH) and these tasks impose different tissue loads compared to lifting tasks. Industry has commonly used the psychophysical tables for job assent and decision of MMH tasks. However, due to the biomechanical complexity of pushing tasks, psychophysics may be misinterpreting risk.

Development and testing of a moment-based coactivation index to assess complex dynamic tasks for the lumbar spine

Background Many methods exist to describe coactivation between muscles. However, most methods have limited capability in the assessment of coactivation during complex dynamic tasks for multi‐muscle systems such as the lumbar spine. The ability to assess coactivation is important for the understanding of neuromuscular inefficiency. In the context of this manuscript, inefficiency is defined as the effort or level of coactivation beyond what may be necessary to accomplish a task (e.g., muscle guarding during postural stabilization). The objectives of this study were to describe the development of an index to assess coactivity for the lumbar spine and test its ability to differentiate between various complex dynamic tasks. Methods The development of the coactivation index involved the continuous agonist/antagonist classification of moment contributions for the power‐producing muscles of the torso. Different tasks were employed to test the range of the index including lifting, pushing, and Valsalva. Findings The index appeared to be sensitive to conditions where higher coactivation would be expected. These conditions of higher coactivation included tasks involving higher degrees of control. Precision placement tasks required about 20% more coactivation than tasks not requiring precision, lifting at chest height required approximately twice the coactivation as mid‐thigh height, and pushing fast speeds with turning also required at least twice the level of coactivity as slow or preferred speeds. Interpretation Overall, this novel coactivation index could be utilized to describe the neuromuscular effort in the lumbar spine for tasks requiring different degrees of postural control. HighlightsA method to assess coactivation from a systems‐perspective is proposed.The method was tested on various complex dynamic manual materials handling tasks.The index could distinguish between tasks of differing degrees of postural control.High levels of postural control (i.e., precision tasks) result in a higher index.

Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume

Optical motion capture is commonly used in biomechanics to measure human kinematics. However, no studies have yet examined the accuracy of optical motion capture in a large capture volume (>100m3), or how accuracy varies from the center to the extreme edges of the capture volume. This study measured the dynamic 3D errors of an optical motion capture system composed of 42 OptiTrack Prime 41 cameras (capture volume of 135m3) by comparing the motion of a single marker to the motion reported by a ThorLabs linear motion stage. After spline interpolating the data, it was found that 97% of the capture area had error below 200μm. When the same analysis was performed using only half (21) of the cameras, 91% of the capture area was below 200μm of error. The only locations that exceeded this threshold were at the extreme edges of the capture area, and no location had a mean error exceeding 1mm. When measuring human kinematics with skin-mounted markers, uncertainty of marker placement relative to underlying skeletal features and soft tissue artifact produce errors that are orders of magnitude larger than the errors attributed to the camera system itself. Therefore, the accuracy of this OptiTrack optical motion capture system was found to be more than sufficient for measuring full-body human kinematics with skin-mounted markers in a large capture volume (>100m3).

Curved muscles in biomechanical models of the spine: a systematic literature review

Abstract Early biomechanical spine models represented the trunk muscles as straight-line approximations. Later models have endeavoured to accurately represent muscle curvature around the torso. However, only a few studies have systematically examined various techniques and the logic underlying curved muscle models. The objective of this review was to systematically categorise curved muscle representation techniques and compare the underlying logic in biomechanical models of the spine. Thirty-five studies met our selection criteria. The most common technique of curved muscle path was the ‘via-point’ method. Curved muscle geometry was commonly developed from MRI/CT database and cadaveric dissections, and optimisation/inverse dynamics models were typically used to estimate muscle forces. Several models have attempted to validate their results by comparing their approach with previous studies, but it could not validate of specific tasks. For future needs, personalised muscle geometry, and person- or task-specific validation of curved muscle models would be necessary to improve model fidelity. Practitioner Summary: The logic underlying the curved muscle representations in spine models is still poorly understood. This literature review systematically categorised different approaches and evaluated their underlying logic. The findings could direct future development of curved muscle models to have a better understanding of the biomechanical causal pathways of spine disorders.

Prediction of magnetic resonance imaging-derived trunk muscle geometry with application to spine biomechanical modeling.

BACKGROUND Accurate geometry of the trunk musculature is essential for reliably estimating spinal loads in biomechanical models. Currently, many models employ straight muscle path assumptions that yield far less accurate tissue loads, particularly in extreme postures. Precise muscle moment-arms and physiological cross-sectional areas are important when incorporating curved muscle geometry in biomechanical models. The objective of this study was to develop a predictive model of moment arms and physiological cross-sectional areas of trunk musculature at multiple levels in the thoracic/lumbar spine as a function of anthropometric measures. METHODS Based on magnetic resonance imaging data from thirty subjects (10 male and 20 female) reported in a previous study, a polynomial regression analysis was conducted to estimate the muscle moment-arms and physiological cross-sectional areas of trunk muscles through thoracic/lumbar spine as a function of vertebral level, gender, age, height, and body mass. FINDINGS Gender, body mass, and height were the best predictors of muscle moment-arms and physiological cross-sectional areas. The predictability of muscle parameters tended to be higher for erector spinae than other muscles. Most muscles showed a curved muscle path along the thoracic/lumbar spine. INTERPRETATION The polynomial regression model of the muscle geometry in this study generally showed good predictability compared to previous reports. The predictive model in this study will be useful to develop personalized biomechanical models that incorporate curved trunk muscle geometries.

Validation of a personalized curved muscle model of the lumbar spine during complex dynamic exertions

Previous curved muscle models have typically examined their robustness only under simple, single-plane static exertions. In addition, the empirical validation of curved muscle models through an entire lumbar spine has not been fully realized. The objective of this study was to empirically validate a personalized biologically-assisted curved muscle model during complex dynamic exertions. Twelve subjects performed a variety of complex lifting tasks as a function of load weight, load origin, and load height. Both a personalized curved muscle model as well as a straight-line muscle model were used to evaluate the models fidelity and prediction of three-dimensional spine tissue loads under different lifting conditions. The curved muscle model showed better model performance and different spinal loading patterns through an entire lumbar spine compared to the straight-line muscle model. The curved muscle model generally showed good fidelity regardless of lifting condition. The majority of the 600 lifting tasks resulted in a coefficient of determination (R2) greater than 0.8 with an average of 0.83, and the average absolute error less than 15% between measured and predicted dynamic spinal moments. As expected, increased load and asymmetry were generally found to significantly increase spinal loads, demonstrating the ability of the model to differentiate between experimental conditions. A curved muscle model would be useful to estimate precise spine tissue loads under realistic circumstances. This precise assessment tool could aid in understanding biomechanical causal pathways for low back pain.

Biomechanically determined hand force limits protecting the low back during occupational pushing and pulling tasks

Abstract Though biomechanically determined guidelines exist for lifting, existing recommendations for pushing and pulling were developed using a psychophysical approach. The current study aimed to establish objective hand force limits based on the results of a biomechanical assessment of the forces on the lumbar spine during occupational pushing and pulling activities. Sixty-two subjects performed pushing and pulling tasks in a laboratory setting. An electromyography-assisted biomechanical model estimated spinal loads, while hand force and turning torque were measured via hand transducers. Mixed modelling techniques correlated spinal load with hand force or torque throughout a wide range of exposures in order to develop biomechanically determined hand force and torque limits. Exertion type, exertion direction, handle height and their interactions significantly influenced dependent measures of spinal load, hand force and turning torque. The biomechanically determined guidelines presented herein are up to 30% lower than comparable psychophysically derived limits and particularly more protective for straight pushing. Practitioner Summary: This study utilises a biomechanical model to develop objective biomechanically determined push/pull risk limits assessed via hand forces and turning torque. These limits can be up to 30% lower than existing psychophysically determined pushing and pulling recommendations. Practitioners should consider implementing these guidelines in both risk assessment and workplace design moving forward.