BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

Effect of low back pain on the kinetics and kinematics of the lumbar spine - a combined in vivo and in silico investigation.

Lifting is a significant risk factor for low back pain (LBP). Different biomechanical factors including spinal loads, kinematics, and muscle electromyography (EMG) activities have previously been investigated during lifting activities in LBP patients and asymptomatic individuals to identify their association with LBP. However, the findings were contradictory and inconclusive. Accurate and subject-specific prediction of spinal loads is crucial for understanding, diagnosing, planning tailored treatments, and preventing recurrent pain in LBP patients. Therefore, the present study aimed to estimate the L5-S1 compressive and resultant shear loads in 19 healthy and 17 non-specific chronic LBP individuals during various static load-holding tasks (holding a 10 kg box at hip, chest, and head height) using full-body and personalized musculoskeletal models driven by subject-specific in vivo kinematic/kinetic, EMG, and physiological cross-sectional areas (PCSAs) data. These biomechanical characteristics were concurrently analyzed to identify potential differences between the two groups. Statistical analyses showed that LBP had almost no significant effect on the range of motion (trunk, lumbar, pelvis), PCSA, and EMG. There were no significant differences (p > 0.05) in the predicted L5-S1 loads. However, as the task became more demanding, by elevating the hand-load from hip to head, LBP patients experienced significant increases in both compressive (33 %, p = 0.00) and shear (25 %, p = 0.02) loads, while asymptomatic individuals showed significant increases only in compressive loads (30 %, p = 0.01). This suggests that engaging in more challenging activities could potentially magnify the effect of LBP on the biomechanical factors and increase their discrimination capacity between LBP and asymptomatic individuals.

Effect of muscular fatigue on the cumulative lumbar damage during repetitive lifting task: a comparative study of damage calculation methods.

Several methods have been put forward to quantify cumulative loads; however, limited evidence exists as to the subsequent damages and the role of muscular fatigue. The present study assessed whether muscular fatigue could affect cumulative damage imposed on the L5-S1 joint. Trunk muscle electromyographic (EMG) activities and kinematics/kinetics of 18 healthy male individuals were evaluated during a simulated repetitive lifting task. A traditional EMG-assisted model of the lumbar spine was modified to account for the effect of erector spinae fatigue. L5-S1 compressive loads for each lifting cycle were estimated based on varying (i.e. actual), fatigue-modified, and constant Gain factors. The corresponding damages were integrated to calculate the cumulative damage. Moreover, the damage calculated for one lifting cycle was multiplied by the lifting frequency, as the traditional approach. Compressive loads and the damages obtained through the fatigue-modified model were predicted in close agreement with the actual values. Similarly, the difference between actual damages and those driven by the traditional approach was not statistically significant (p = 0.219). However, damages based on a constant Gain factor were significantly greater than those based on the actual (p = 0.012), fatigue-modified (p = 0.017), and traditional (p = 0.007) approaches.Practitioner summary: In this study, we managed to include the effect of muscular fatigue on cumulative lumbar damage calculations. Including the effect of muscular fatigue leads to an accurate estimation of cumulative damages while eliminating computational complexity. However, using the traditional approach also appears to provide acceptable estimates for ergonomic assessments.

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads.

Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To estimate muscle forces and joint loads, these models require body posture during the activity under consideration. Posture is usually measured via video-camera motion tracking approaches that are time-consuming, costly, and/or limited to laboratories. Alternatively, posture-prediction tools based on artificial intelligence can be trained using measured postures of several subjects performing many activities. We aimed to use our previous posture-prediction artificial neural network (ANN), developed based on many measured static postures, to predict posture during dynamic lifting activities. Moreover, effects of the ANN posture-prediction errors on dynamic spinal loads were investigated using subject-specific musculoskeletal models. Seven individuals each performed twenty-five lifting tasks while their full-body three-dimensional posture was measured by a 10-camera Vicon system and also predicted by the ANN as functions of the hand-load positions during the lifting activities. The measured and predicted postures (i.e., coordinates of 39 skin markers) and their model-estimated L5-S1 loads were compared. The overall root-mean-squared-error (RMSE) and normalized (by the range of measured values) RMSE (nRMSE) between the predicted and measured postures for all markers/tasks/subjects was equal to 7.4 cm and 4.1 %, respectively (R2 = 0.98 and p < 0.05). The model-estimated L5-S1 loads based on the predicted and measured postures were generally in close agreements as also confirmed by the Bland-Altman analyses; the nRMSE for all subjects/tasks was < 10 % (R2 > 0.7 and p > 0.05). In conclusion, the easy-to-use ANN can accurately predict posture in dynamic lifting activities and its predicted posture can drive musculoskeletal models.

Multi-task artificial neural networks and their extrapolation capabilities to predict full-body 3D human posture during one- and two-handed load-handling activities.

Machine-learning based human posture-prediction tools can potentially be robust alternatives to motion capture measurements. Existing posture-prediction approaches are confined to two-handed load-handling activities performed at heights below 120 cm from the floor and to predicting a limited number of body-joint coordinates/angles. Moreover, the extrapolating power of these tools beyond the range of the input dataset they were trained for (e.g., for underweight, overweight, or left-handed individuals) has not been investigated. In this study, we trained/validated/tested two posture-prediction (for full-body joint coordinates and angles) artificial neural networks (ANNs) using both 70%/15%/15% random-hold-out and leave-one-subject-out methods, based on a comprehensive kinematic dataset of forty-one full-body skin markers collected from twenty right-handed normal-weight (BMI = 18-26 kg/m2) subjects. Subjects performed 204 one- and two-handed unloaded activities at different vertical (0 to 180 cm from the floor) and horizontal (up to 60 cm lateral and/or anterior) destinations. Subsequently, the extrapolation capability of the trained/validated/tested ANNs was evaluated using data collected from fifteen additional subjects (unseen by the ANNs); three individuals in five groups: underweight, overweight, obese, left-handed individuals, and subjects with a hand-load. Results indicated that the ANNs predicted body joint coordinates and angles during various activities with errors of âˆ¼ 25 mm and âˆ¼ 10°, respectively; considerable improvements when compared to previous posture-prediction ANNs. Extrapolation errors of our ANNs generally remained within the error range of existing ANNs with obesity and being left-handed having, respectively, the most and least compromising effects on their accuracy. These easy-to-use ANNs appear, therefore, to be robust alternatives to common posture-measurement approaches.

Prediction of Post-operative Clinical Indices in Scoliosis Correction Surgery Using an Adaptive Neuro-fuzzy Interface System.

Objectives: Accurate estimation of post-operative clinical parameters in scoliosis correction surgery is crucial. Different studies have been carried out to investigate scoliosis surgery results, which were costly, time-consuming, and with limited application. This study aims to estimate post-operative main thoracic cobb and thoracic kyphosis angles in adolescent idiopathic scoliosis patients using an adaptive neuro-fuzzy interface system. Methods: Distinct pre-operative clinical indices of fifty-five patients (e.g., thoracic cobb, kyphosis, lordosis, and pelvic incidence) were taken as the inputs of the adaptive neuro-fuzzy interface system in four categorized groups, and post-operative thoracic cobb and kyphosis angles were taken as the outputs. To evaluate the robustness of this adaptive system, the predicted values of post-operative angles were compared with the measured indices after the surgery by calculating the root mean square errors and clinical corrective deviation indices, including the relative deviation of post-operative angle prediction from the actual angle after the surgery. Results: The group with inputs for main thoracic cobb, pelvic incidence, thoracic kyphosis, and T1 spinopelvic inclination angles had the lowest root mean square error among the four groups. The error values were 3.0° and 6.3° for the post-operative cobb and thoracic kyphosis angles, respectively. Moreover, the values of clinical corrective deviation indices were calculated for four sample cases, including 0.0086 and 0.0641 for the cobb angles of two cases and 0.0534 and 0.2879 for thoracic kyphosis of the other two cases. Conclusion: In all scoliotic cases, the post-operative cobb angles were lesser than the pre-operative ones; however, the post-operative thoracic kyphosis might be lesser or higher than the pre-operative ones. Therefore, the cobb angle correction is in a more regular pattern and is more straightforward to predict cobb angles. Consequently, their root-mean-squared errors become lesser values than thoracic kyphosis.

The National Institute for Occupational Safety and Health (NIOSH) Recommended Weight Generates Different Spine Loads in Load-Handling Activity Performed Using Stoop, Semi-squat and Full-Squat Techniques; a Full-Body Musculoskeletal Model Study.

OBJECTIVE: Adequacy of the Revised NIOSH Lifting Equation (RNLE) in maintaining lumbosacral (L5-S1) loads below their recommended action limits in stoop, full-squat, and semi-squat load-handling activities was investigated using a full-body musculoskeletal model. BACKGROUND: The NIOSH committee did not consider the lifting technique adapted by workers when estimating the recommended weight limit (RWL). It is currently unknown whether the lifting technique adapted by workers would affect the competence of the RNLE in keeping spine loads below their recommended limits. METHOD: A full-body subject-specific musculoskeletal model (Anybody Modeling System, AMS) driven by a 10-camera Vicon motion capture system (Vicon Motion Systems Inc., Oxford, UK) was used to simulate different static stoop, semi-squat, and full-squat load-handling activities of ten normal-weight volunteers (mean of ∼70 kg corresponding to the 15th percentile of adult American males) with the task-specific NIOSH RWL held in hands. RESULTS: Two-way repeated measures ANOVA revealed a significant effect of lifting technique on both the L5-S1 compression (p = 0.003) and shear (p = 0.004) loads with semi-squat technique resulting in significantly larger loads than both stoop and full-squat techniques (p < 0.05). While mean of L5-S1 loads remained smaller than their recommended limits, it is much expected that they pass these limits for heavier individuals, that is, for the 50th percentile of adult American males. CONCLUSION: Spinal loads are expected to pass their recommended limits for heavier individuals especially during semi-squat lifting as the most frequently adapted technique by workers. APPLICATION: Caution is required for the assessment of semi-squat lifting activities by the RNLE.

The Validity and Inter-Rater Reliability of a Video-Based Posture-Matching Tool to Estimate Cumulative Loads on the Lower Back.

Background: Low back pain (LBP) is known as one of the most common work-related musculoskeletal disorders. Spinal cumulative loads (CLs) during manual material handling (MMH) tasks are the main risk factors for LBP. However, there is no valid and reliable quantitative lifting analysis tool available for quantifying CLs among Iranian workers performing MMH tasks. Objective: This study aimed to investigate the validity and inter-rater reliability of a posture-matching load assessment tool (PLAT) for estimating the L5-S1 static cumulative compression (CC) and shear (CS) loads based on predictive regression equations. Material and Methods: This experimental study was conducted among six participants performing four lifting tasks, each comprised of five trials during which their posture was recorded via a motion capture (Vicon) and simultaneously a three-camera system located at three different angles (0°, 45°, and 90°) to the sagittal plane. Results: There were no significant differences between the two CLs estimated by PLAT from the three-camera system and the gold-standard Vicon. In addition, ten raters estimated CLs of the tasks using PLAT in three sessions. The calculated intra-class correlation coefficients for the estimated CLs within each task revealed excellent inter-rater reliability (> 0.75), except for CS in the first and third tasks, which were good (0.6 to 0.75). Conclusion: The proposed posture-matching approach provides a valid and reliable ergonomic assessment tool suitable for assessing spinal CLs during various lifting activities.

Submaximal electromyography-driven musculoskeletal modeling of the human trunk during static tasks: Equilibrium and stability analyses.

Conventional electromyography-driven (EMG) musculoskeletal models are calibrated during maximum voluntary contraction (MVC) tasks, but individuals with low back pain cannot perform unbiased MVCs. To address this issue, EMG-driven models can be calibrated in submaximal tasks. However, the effects of maximal (when data points include the maximum contraction) and submaximal calibration techniques on model outputs (e.g., muscle forces, spinal loads) remain yet unknown. We calibrated a subject-specific EMG-driven model, using maximal/submaximal isometric contractions, and simulated different independent tasks. Both approaches satisfactorily predicted external moments (Pearson's correlation ∼ 0.75; relative error = 44%), and removing calibration tasks under axial torques markedly improved the model performance (Pearson's correlation âˆ¼ 0.92; relative error âˆ¼ 28%). Unlike individual muscle forces, gross (aggregate) model outputs (i.e., spinal loads, stability index, and sum of abdominal/back muscle forces) estimated from maximal and submaximal calibration techniques were highly correlated (r > 0.78). Submaximal calibration method overestimated spinal loads (6% in average) and abdominal muscle forces (11% in average). Individual muscle forces estimated from maximal and submaximal approaches were substantially different; however, gross model outputs (especially internal loads and stability index) remained highly correlated with small to moderate relative differences; therefore, the submaximal calibration technique can be considered as an alternative to the conventional maximal calibration approach.

Adjacent segments biomechanics following lumbar fusion surgery: a musculoskeletal finite element model study.

PURPOSE: This study exploits a novel musculoskeletal finite element (MS-FE) spine model to evaluate the post-fusion (L4-L5) alterations in adjacent segment kinetics. METHODS: Unlike the existing MS models with idealized representation of spinal joints, this model predicts stress/strain distributions in all passive tissues while organically coupled to a MS model. This generic (in terms of musculature and material properties) model uses population-based in vivo vertebral sagittal rotations, gravity loads, and an optimization algorithm to calculate muscle forces. Simulations represent individuals with an intact L4-L5, a preoperative severely degenerated L4-L5 (by reducing the disc height by ~ 60% and removing the nucleus incompressibility), and a postoperative fused L4-L5 segment with either a fixed or an altered lumbopelvic rhythm with respect to the intact condition (based on clinical observations). Changes in spine kinematics and back muscle cross-sectional areas (due to intraoperative injuries) are considered based on in vivo data while simulating three activities in upright/flexed postures. RESULTS: Postoperative changes in some adjacent segment kinetics were found considerable (i.e., larger than 25%) that depended on the postoperative lumbopelvic kinematics and preoperative L4-L5 disc condition. Postoperative alterations in adjacent disc shear, facet/ligament forces, and annulus stresses/strains were greater (> 25%) than those found in intradiscal pressure and compression (< 25%). Kinetics of the lower (L5-S1) and upper (L3-L4) adjacent segments were altered to different degrees. CONCLUSION: Alterations in segmental rotations mainly affected adjacent disc shear forces, facet/ligament forces, and annulus/collagen fibers stresses/strains. An altered lumbopelvic rhythm (increased pelvis rotation) tends to mitigate some of these surgically induced changes.

Improved artificial neural networks for 3D body posture and lumbosacral moment predictions during manual material handling activities.

Body posture measurement approaches, required in biomechanical models to assess risk of musculoskeletal injuries, are usually costly and/or impractical for use in real workplaces. Therefore, we recently developed three artificial neural networks (ANNs), based on measured posture data on several individuals, to predict whole body 3D posture (coordinates of 15 markers located on body's main joints), segmental orientations (Euler angles of 14 body segments), and lumbosacral (L5-S1) moments during static manual material handling (MMH) activities (ANNPosture, ANNAngle, and ANNMoment, respectively). These ANNs require worker's body height, body weight (only for ANNMoment), hand-load 3D position, and its mass as inputs to accurately predict 3D marker coordinates (RMSE = 7.0 cm), segmental orientations (RMSE = 29.9°) and L5-S1 moments (RMSE = 16.5 N.m) for various static MMH activities. The current work aims to further improve the accuracy of these ANNs by performing outlier elimination and data normalization (as effective tools to improve the accuracy of ANNs) as well as by introducing participant's knee flexion angle (i.e., lifting technique: stoop, semi-squat, and full-squat) and body weight as new inputs into these ANNs. Results indicate that the RMSE of the new ANNPosture, ANNAngle, and ANNMoment reduced by, respectively, ∼43%, 10%, and 29% (from 7.0 cm, 29.9°, and 16.5 Nm in the original ANNs to, respectively, 4.0 cm, 27.0°, and 11.8 Nm). Such significant improvements in the predictive power of our ANNs further confirm their effectiveness as alternative posture-prediction approaches requiring minimal in vivo data collection in real workplaces.

A Comprehensive Evaluation of Spine Kinematics, Kinetics, and Trunk Muscle Activities During Fatigue-Induced Repetitive Lifting.

OBJECTIVE: Spine kinematics, kinetics, and trunk muscle activities were evaluated during different stages of a fatigue-induced symmetric lifting task over time. BACKGROUND: Due to neuromuscular adaptations, postural behaviors of workers during lifting tasks are affected by fatigue. Comprehensive aspects of these adaptations remain to be investigated. METHOD: Eighteen volunteers repeatedly lifted a box until perceived exhaustion. Body center of mass (CoM), trunk and box kinematics, and feet center of pressure (CoP) were estimated by a motion capture system and force-plate. Electromyographic (EMG) signals of trunk/abdominal muscles were assessed using linear and nonlinear approaches. The L5-S1 compressive force (Fc) was predicted via a biomechanical model. A two-way multivariate analysis of variance (MANOVA) was performed to examine the effects of five blocks of lifting cycle (C1 to C5) and lifting trial (T1 to T5), as independent variables, on kinematic, kinetic, and EMG-related measures. RESULTS: Significant effects of lifting trial blocks were found for CoM and CoP shift in the anterior-posterior direction (respectively p < .001 and p = .014), trunk angle (p = .004), vertical box displacement (p < .001), and Fc (p = .005). EMG parameters indicated muscular fatigue with the extent of changes being muscle-specific. CONCLUSION: Results emphasized variations in most kinematics/kinetics, and EMG-based indices, which further provided insight into the lifting behavior adaptations under dynamic fatiguing conditions. APPLICATION: Movement and muscle-related variables, to a large extent, determine the magnitude of spinal loading, which is associated with low back pain.

Sex-Dependent Estimation of Spinal Loads During Static Manual Material Handling Activities-Combined in vivo and in silico Analyses.

Manual material handling (MMH) is considered as one of the main contributors to low back pain. While males traditionally perform MMH tasks, recently the number of females who undertake these physically-demanding activities is also increasing. To evaluate the risk of mechanical injuries, the majority of previous studies have estimated spinal forces using different modeling approaches that mostly focus on male individuals. Notable sex-dependent differences have, however, been reported in torso muscle strength and anatomy, segmental mass distribution, as well as lifting strategy during MMH. Therefore, this study aimed to use sex-specific models to estimate lumbar spinal and muscle forces during static MHH tasks in 10 healthy males and 10 females. Motion-capture, surface electromyographic from select trunk muscles, and ground reaction force data were simultaneously collected while subjects performed twelve symmetric and asymmetric static lifting (10 kg) tasks. AnyBody Modeling System was used to develop base-models (subject-specific segmental length, muscle architecture, and kinematics data) for both sexes. For females, female-specific models were also developed by taking into account for the female's muscle physiological cross-sectional areas, segmental mass distributions, and body fat percentage. Males showed higher absolute L5-S1 compressive and shear loads as compared to both female base-models (25.3% compressive and 14% shear) and female-specific models (41% compressive and 23.6% shear). When the predicted spine loads were normalized to subjects' body weight, however, female base-models showed larger loads (9% compressive and 16.2% shear on average), and female-specific models showed 2.4% smaller and 9.4% larger loads than males. Females showed larger forces in oblique abdominal muscles during both symmetric and asymmetric lifting tasks, while males had larger back extensor muscle forces during symmetric lifting tasks. A stronger correlation between measured and predicted muscle activities was found in females than males. Results indicate that female-specific characteristics affect the predicted spinal loads and must be considered in musculoskeletal models. Neglecting sex-specific parameters in these models could lead to the overestimation of spinal loads in females.

Biomechanical effects of lumbar fusion surgery on adjacent segments using musculoskeletal models of the intact, degenerated and fused spine.

Adjacent segment disorders are prevalent in patients following a spinal fusion surgery. Postoperative alterations in the adjacent segment biomechanics play a role in the etiology of these conditions. While experimental approaches fail to directly quantify spinal loads, previous modeling studies have numerous shortcomings when simulating the complex structures of the spine and the pre/postoperative mechanobiology of the patient. The biomechanical effects of the L4-L5 fusion surgery on muscle forces and adjacent segment kinetics (compression, shear, and moment) were investigated using a validated musculoskeletal model. The model was driven by in vivo kinematics for both preoperative (intact or severely degenerated L4-L5) and postoperative conditions while accounting for muscle atrophies. Results indicated marked changes in the kinetics of adjacent L3-L4 and L5-S1 segments (e.g., by up to 115% and 73% in shear loads and passive moments, respectively) that depended on the preoperative L4-L5 disc condition, postoperative lumbopelvic kinematics and, to a lesser extent, postoperative changes in the L4-L5 segmental lordosis and muscle injuries. Upper adjacent segment was more affected post-fusion than the lower one. While these findings identify risk factors for adjacent segment disorders, they indicate that surgical and postoperative rehabilitation interventions should focus on the preservation/restoration of patient's normal segmental kinematics.

Design, Fabrication, and Accuracy of a Novel Noncovering Lock-Mechanism Bilateral Patient-Specific Drill Guide Template for Nondeformed and Deformed Thoracic Spines.

Background: Pedicle screw (PS) placement has been widely used in fusion surgeries on the thoracic spine. Achieving cost-effective yet accurate placements through nonradiation techniques remains challenging. Questions/Purposes: Novel noncovering lock-mechanism bilateral vertebra-specific drill guides for PS placement were designed/fabricated, and their accuracy for both nondeformed and deformed thoracic spines was tested. Methods: One nondeformed and 1 severe scoliosis human thoracic spine underwent computed tomographic (CT) scanning, and 2 identical proportions of each were 3-dimensional (3D) printed. Pedicle-specific optimal (no perforation) drilling trajectories were determined on the CT images based on the entry point/orientation/diameter/length of each PS. Vertebra-specific templates were designed and 3D printed, assuring minimal yet firm contacts with the vertebrae through a noncovering lock mechanism. One model of each patient was drilled using the freehand and one using the template guides (96 pedicle drillings). Postoperative CT scans from the models with the inserted PSs were obtained and superimposed on the preoperative planned models to evaluate deviations of the PSs. Results: All templates fitted their corresponding vertebra during the simulated operations. As compared with the freehand approach, PS placement deviations from their preplanned positions were significantly reduced: for the nonscoliosis model, from 2.4 to 0.9 mm for the entry point, 5.0° to 3.3° for the transverse plane angle, 7.1° to 2.2° for the sagittal plane angle, and 8.5° to 4.1° for the 3D angle, improving the success rate from 71.7% to 93.5%. Conclusions: These guides are valuable, as the accurate PS trajectory could be customized preoperatively to match the patients' unique anatomy. In vivo studies will be required to validate this approach.

Comparison of different lifting analysis tools in estimating lower spinal loads - Evaluation of NIOSH criterion.

Excessive loads on the human spine is recognized as a risk factor for back injuries/pain. Various lifting analysis tools such as musculoskeletal models, regression equations and NIOSH (National Institute for Occupational Safety and Health) lifting equation (NLE) have been proposed to evaluate and mitigate associated risks during manual material handling activities. Present study aims to compare predicted spinal loads from 5 different lifting analysis tools as well as to critically evaluate the NIOSH recommended weight limit (RWL). Spinal loads were estimated under different symmetric/asymmetric lifting tasks in which hand-load mass at each task was set based on RWL from NLE. Estimated intradiscal pressures (IDPs) of various tools were also compared with in vivo measurements. We compared RWL by NLE versus our estimations of RWL calculated from our regression equations using biomechanical criteria (compression <3400 N with/without shear <1000, 1250 or 1500 N). Our regression equations followed by OpenSim, AnyBody, simple polynomial and 3DSSPP satisfactorily predicted L4-L5 IDPs. Lifting analysis tools estimated comparable spinal compression forces (mean Pearson's r = 0.80; standard deviation of relative difference = 26%) while in shear, differences were greater (mean Pearson's r = 0.68; standard deviation of relative difference = 56%). NLE estimations of RWL were conservative in comparison with our estimations for lean individuals (BMI < 25 kg/m2) when compression <3400 N and shear <1250 N were considered as the biomechanical criteria. For heavier individuals, however, NLE estimations of RWL generated spinal compression >3400 N (NIOSH biomechanical safety threshold) as well as shear >1000 N. Although RWLs estimated by NLE was body weight independent, body weight substantially altered RWLs estimated from our regression equations. For improved estimation of the risk of injury, more accurate failure criteria for spinal segments are essential.

Estimation of Trunk Muscle Forces Using a Bio-Inspired Control Strategy Implemented in a Neuro-Osteo-Ligamentous Finite Element Model of the Lumbar Spine.

Low back pain (LBP), the leading cause of disability worldwide, remains one of the most common and challenging problems in occupational musculoskeletal disorders. The effective assessment of LBP injury risk, and the design of appropriate treatment modalities and rehabilitation protocols, require accurate estimation of the mechanical spinal loads during different activities. This study aimed to: (1) develop a novel 2D beam-column finite element control-based model of the lumbar spine and compare its predictions for muscle forces and spinal loads to those resulting from a geometrically matched equilibrium-based model; (2) test, using the foregoing control-based finite element model, the validity of the follower load (FL) concept suggested in the geometrically matched model; and (3) investigate the effect of change in the magnitude of the external load on trunk muscle activation patterns. A simple 2D continuous beam-column model of the human lumbar spine, incorporating five pairs of Hill's muscle models, was developed in the frontal plane. Bio-inspired fuzzy neuro-controllers were used to maintain a laterally bent posture under five different external loading conditions. Muscle forces were assigned based on minimizing the kinematic error between target and actual postures, while imposing a penalty on muscular activation levels. As compared to the geometrically matched model, our control-based model predicted similar patterns for muscle forces, but at considerably lower values. Moreover, irrespective of the external loading conditions, a near (<3°) optimal FL on the spine was generated by the control-based predicted muscle forces. The variation of the muscle forces with the magnitude of the external load within the simulated range at the L1 level was found linear. This work presents a novel methodology, based on a bio-inspired control strategy, that can be used to estimate trunk muscle forces for various clinical and occupational applications toward shedding light on the ever-elusive LBP etiology.

Mechanical characterization of the ligaments in subject-specific models of the patellofemoral joint using in vivo laxity tests.

BACKGROUND: The purpose of this study was to propose a methodology for mechanical characterization of the ligaments in subject-specific models of the patellofemoral joint (PFJ) of living individuals. METHOD: PFJ laxity tests were performed on a healthy volunteer using a specially designed loading apparatus under biplane fluoroscopy. A three-dimensional (3D) parametric model of the PFJ was developed in the framework of the rigid body spring model using the geometrical data acquired from the subject's computed tomography and magnetic resonance images. The stiffness and pre-strains of the medial and lateral PFJ ligaments were characterized using a two-step optimization procedure which minimized the deviation between the model predictions and the calibration test results. Sensitivity analyses were performed to investigate the effect of mechanical properties of the non-characterized model components on the characterization procedure and its results. RESULTS: The overall findings indicate that the proposed methodology is applicable and can improve the model predictions effectively. For the subject under study, ligament characterization reduced the root mean square of the deviations between the patellar shift and tilt predicted by the model and obtained experimentally for the validation laxity test (from 6.2 mm to 0.5 mm, and from 8.4° to 1.5°, respectively) and passive knee flexion test (from 1.4 mm to 0.3 mm, and from 2.3° to 1.3°, respectively). The non-characterized mechanical properties were found to have a minimal effect on the characterization procedure and its results. CONCLUSION: The proposed methodology can help in developing truly patient-specific models of the PFJ, to be used for personalized preplanning of the clinical interventions.

Subject-specific loads on the lumbar spine in detailed finite element models scaled geometrically and kinematic-driven by radiography images.

Traditional load-control musculoskeletal and finite element (FE) models of the spine fail to accurately predict in vivo intervertebral joint loads due mainly to the simplifications and assumptions when estimating redundant trunk muscle forces. An alternative powerful protocol that bypasses the calculation of muscle forces is to drive the detailed FE models by image-based in vivo displacements. Development of subject-specific models, however, both involves the risk of extensive radiation exposures while imaging in supine and upright postures and is time consuming in terms of the reconstruction of the vertebrae, discs, ligaments, and facets geometries. This study therefore aimed to introduce a remedy for the development of subject-specific FE models by scaling the geometry of an existing detailed FE model of the T12-S1 lumbar spine. Five subject-specific scaled models were driven by their own radiography image-based displacements in order to predict joint loads, ligament forces, facet joint forces, and disc fiber strains during relaxed upright as well as moderate flexion and extension tasks. The predicted intradiscal pressures were found in adequate agreement with in vivo data for upright, flexion, and extension tasks. There were however large intersubject variations in the estimated joint loads and facet forces.

A stability-based model of a growing spine with adolescent idiopathic scoliosis: A combination of musculoskeletal and finite element approaches.

Using a combined musculoskeletal and finite element (FE) approach, this study aimed to evaluate stability-based muscle forces in a spine with adolescent idiopathic scoliosis (AIS) as compared to a normal spine; and subsequently, determine the effects of stress distribution on the growth plates (GPs) of the growing spine. For this purpose a nonlinear 3D FE model of one normal and one scoliotic thoracolumbar spine, consisting of GPs attached to rigid L1 to L4 vertebrae, were developed using computed tomography images coupled with a growth modulation using the Stokes' model. Corresponding well with recent in-vivo and in-vitro studies, results of the models predicted intradiscal pressures at the L3-L4 and L4-L5 levels of 0.32 and 0.38 MPa in the normal spine and 0.30 and 0.36 MPa in the scoliotic spine, respectively; and hydrostatic and octahedral shear stresses on the apical GP of 0.11 and 0.06 MPa, respectively. The reaction moments in the scoliotic model resulted in higher compression on the posteroconcave side of the GPs, which led to deformity progression as predicted by the Hueter-Volkmann theory. Moreover, the augmented baseline growth in the Stokes' model magnified both the scoliotic spine height and Cobb angle growth rates. The presented stability-based approach can be used to predict the performance of rehabilitation strategies in the clinical management of AIS.