The levitation tricompartment offloader (TCO) brace is designed to unload all three knee compartments by reducing compressive forces caused by muscle contraction. This study aimed to determine the ...effect of the TCO on knee contact forces and quadriceps muscle activity in individuals with knee osteoarthritis. Lower limb kinematics, kinetics, and electromyography data were collected during a chair rise‐and‐lower task. A three‐dimensional inverse dynamics model of the lower leg and foot was used with a sagittal plane knee model to compute knee joint forces. TCO brace use significantly decreased forces in the tibiofemoral p = 0.001; mean difference, MD (97.5% confidence interval, CI) −0.62 (−0.91, −0.33) body weight (BW) and patellofemoral p = 0.001; MD (97.5% CI) −0.88 (−1.36, −0.39) BW compartments in high‐power mode. Significant reductions in quadriceps tendon force p = 0.002; MD (97.5% CI) −0.53 (−0.83, −0.23) BW and electromyography intensity of the vastus medialis p = 0.018, MD (97.5% CI) −30.7 (−59.1, −2.3) and vastus lateralis p = 0.012, MD (97.5% CI) −26.2 (−48.5, −3.9) were also observed. The TCO significantly reduced tibiofemoral and patellofemoral contact forces throughout chair lower, and when knee flexion was greater than 50° during chair rise in high power. These results demonstrate that the TCO reduces contact forces in the tibiofemoral and patellofemoral joint compartments and confirms that the TCO unloads the joint by reducing compressive forces caused by the quadriceps. Clinical significance: The magnitude of knee joint unloading provided by the TCO is similar to that achieved by clinically recommended levels of bodyweight loss and is therefore expected to result in clinical benefits for knee osteoarthritis patients.
•Accuracy of stair GRFs using depth sensor-driven musculoskeletal model was assessed.•Study subjects were ACL patients following ACL reconstruction surgery.•The estimation of GRFs was highly ...dependent on the evaluated force component.•This method has the potential as a cost-effective tool in the clinical setting.
Although stair ambulation should be included in the rehabilitation of the long-term effects of ACL injury on knee function, the assessment of kinetic parameter in the situation where stair gait can only be established using costly and cumbersome force platforms via conventional inverse dynamic analysis. Therefore, there is a need to develop a practical laboratory setup as an assessment tool of the stair gait abnormalities in lower extremity that arise from an ACL deficiency.
Can the use of a single depth sensor-driven full-body musculoskeletal gait model be considered an accurate assessment tool of the ground reaction forces (GRFs) during stair climbing for patients following ACL reconstruction (ACLR) surgery?
A total of 15 patients who underwent ACLR participated in this study. GRFs data during stair climbing was collected using a custom-built 3-step staircase with two embedded force platforms. A single depth sensor, commercially available and cost effective, was used to obtain participants’ depth map information to extract the full-body skeleton information. The AnyBody TM GaitFullBody model was utilized to estimate GRFs attained by 25 artificial muscle-like actuators placed under each foot. Mean differences between the measured and estimated GRFs were compared using paired samples t-tests. The ensemble curves of the GRFs were compared between both approaches during stance phase of the gait cycle.
The findings of this study showed that the estimation of the GRFs produced during staircase gait using a depth sensor-driven musculoskeletal model can produce acceptable results when compared to the traditional inverse dynamics modelling approach as an alternative tool in clinical settings for individuals who had undergone ACLR.
The introduced approach of full-body musculoskeletal modelling driven by a single depth sensor has the potential to be a cost-effective stair gait analysis tool for patients with ACL injury.
Variations observed in biomechanical studies might be attributed to errors made by operators during the construction of musculoskeletal models, rather than being solely attributed to patient-specific ...geometry.
What is the impact of operator errors on the construction of musculoskeletal models, and how does it affect the estimation of muscle moment arms and hip joint reaction forces?
Thirteen independent operators participated in defining the muscle model, while a single operator performed 13 repetitions to define the muscle model based on 3D bone geometry. For each model, the muscle moment arms relative to the hip joint center of rotation was evaluated. Additionally, the hip joint reaction force during one-legged stance was assessed using static inverse optimization.
The results indicated high levels of consistency, as evidenced by the intra- rater and inter-rater agreement measured by the Intraclass Correlation Coefficient (ICC), which yielded values of 0.95 and 0.99, respectively. However, the estimated muscle moment arms exhibited an error of up to 16 mm compared to the reference musculoskeletal model. It was found that muscles attached to prominent anatomical landmarks were specified with greater accuracy than those attached over larger areas. Furthermore, the variability in estimated moment arms contributed to variations of up to 12% in the hip joint reaction forces.
Both moment arm and muscle force demonstrated significantly lower variability when assessed by a single operator, suggesting the preference for employing a single operator in the creation of musculoskeletal models for clinical biomechanical studies.
Objective: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but ...provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. Methods: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. Results: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. Conclusion: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. Significance: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation.
Abstract The ability of a biomechanical simulation to produce results that can translate to real-life situations is largely dependent on the physiological accuracy of the musculoskeletal model. There ...are a limited number of freely-available, full-body models that exist in OpenSim, and those that do exist are very limited in terms of trunk musculature and degrees of freedom in the spine. Properly modeling the motion and musculature of the trunk is necessary to most accurately estimate lower extremity and spinal loading. The objective of this study was to develop and validate a more physiologically accurate OpenSim full-body model. By building upon three previously developed OpenSim models, the Full-Body Lumbar Spine (FBLS) model, comprised of 21 segments, 30 degrees-of-freedom, and 324 musculotendon actuators, was developed. The five lumbar vertebrae were modeled as individual bodies, and coupled constraints were implemented to describe the net motion of the spine. The eight major muscle groups of the lumbar spine were modeled (rectus abdominis, external and internal obliques, erector spinae, multifidus, quadratus lumborum, psoas major, and latissimus dorsi), and many of these muscle groups were modeled as multiple fascicles allowing the large muscles to act in multiple directions. The resulting FBLS model’s trunk muscle geometry, maximal isometric joint moments, and simulated muscle activations compare well to experimental data. The FBLS model will be made freely available (https://simtk.org/home/fullbodylumbar) for others to perform additional analyses and develop simulations investigating full-body dynamics and contributions of the trunk muscles to dynamic tasks.
Abstract Inverse dynamics based simulations on musculoskeletal models is a commonly used method for the analysis of human movement. Due to inaccuracies in the kinematic and force plate data, and a ...mismatch between the model and the subject, the equations of motion are violated when solving the inverse dynamics problem. As a result, dynamic inconsistency will exist and lead to residual forces and moments. In this study, we present and evaluate a computational method to perform inverse dynamics-based simulations without force plates, which both improves the dynamic consistency as well as removes the model׳s dependency on measured external forces. Using the equations of motion and a scaled musculoskeletal model, the ground reaction forces and moments (GRF&Ms) are derived from three-dimensional full-body motion. The method entails a dynamic contact model and optimization techniques to solve the indeterminacy problem during a double contact phase and, in contrast to previously proposed techniques, does not require training or empirical data. The method was applied to nine healthy subjects performing several Activities of Daily Living (ADLs) and evaluated with simultaneously measured force plate data. Except for the transverse ground reaction moment, no significant differences ( P >0.05) were found between the mean predicted and measured GRF&Ms for almost all ADLs. The mean residual forces and moments, however, were significantly reduced ( P >0.05) in almost all ADLs using our method compared to conventional inverse dynamic simulations. Hence, the proposed method may be used instead of raw force plate data in human movement analysis using inverse dynamics.
Knee osteoarthritis is a major cause of pain and disability in the elderly population with many daily living activities being difficult to perform as a result of this disease. The present study aimed ...to estimate the knee adduction moment and tibiofemoral joint contact force during daily living activities using a musculoskeletal model with inertial motion capture derived kinematics in an elderly population. Eight elderly participants were instrumented with 17 inertial measurement units, as well as 53 opto-reflective markers affixed to anatomical landmarks. Participants performed stair ascent, stair descent, and sit-to-stand movements while both motion capture methods were synchronously recorded. A musculoskeletal model containing 39 degrees-of-freedom was used to estimate the knee adduction moment and tibiofemoral joint contact force. Strong to excellent Pearson correlation coefficients were found for the IMC-derived kinematics across the daily living tasks with root mean square errors (RMSE) between 3° and 7°. Furthermore, moderate to strong Pearson correlation coefficients were found in the knee adduction moment and tibiofemoral joint contact forces with RMSE between 0.006⁻0.014 body weight × body height and 0.4 to 1 body weights, respectively. These findings demonstrate that inertial motion capture may be used to estimate knee adduction moments and tibiofemoral contact forces with comparable accuracy to optical motion capture.
This study evaluated the between-session reliability of creating subject-specific musculoskeletal models with optoelectronic motion capture data, and using them to estimate spine loading. Nineteen ...healthy participants aged 24–74 years underwent the same set of measurements on two separate occasions. Retroreflective markers were placed on anatomical regions, including C7, T1, T4, T5, T8, T9, T12 and L1 spinous processes, pelvis, upper and lower limbs, and head. We created full-body musculoskeletal models with detailed thoracolumbar spines, and scaled these to create subject-specific models for each individual and each session. Models were scaled from distances between markers, and spine curvature was adjusted according to marker-estimated measurements. Using these models, we estimated vertebral compressive loading for five different standardized postures: neutral standing, 45˚ trunk flexion, 15˚ trunk extension, 20˚ lateral bend to the right, and 45˚ axial rotation to the right. Intraclass correlation coefficients (ICCs) and standard error of measurement were calculated as measures of between-session reliability and measurement error, respectively. Spine curvature measures showed excellent reliability (ICC = 0.79–0.91) and body scaling segments showed fair to excellent reliability (ICC = 0.46–0.95). We found that musculoskeletal models showed mostly excellent between-session reliability to estimate spine loading, with 91% of ICC values > 0.75 for all activities. This information is a necessary precursor for using motion capture data to estimate spine loading from subject-specific musculoskeletal models, and suggests that marker data will deliver reproducible subject-specific models and estimates of spine loading.
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.
Objective: In this study, we aimed to develop a novel electromyography (EMG)-based neural machine interface (NMI), called the Neural Network-Musculoskeletal hybrid Model (N2M2), to decode continuous ...joint angles. Our approach combines the concepts of machine learning and musculoskeletal modeling. Methods: We compared our novel design with a musculoskeletal model (MM) and 2 continuous EMG decoders based on artificial neural networks (ANNs): multilayer perceptrons (MLPs) and nonlinear autoregressive neural networks with exogenous inputs (NARX networks). EMG and joint kinematics data were collected from 10 non-disabled and 1 transradial amputee subject. The offline performance tested across 3 different conditions (i.e., varied arm postures, shifted electrode locations, and noise-contaminated EMG signals) and online performance for a virtual postural matching task was quantified. Finally, we implemented the N2M2 to operate a prosthetic hand and tested functional task performance. Results: The N2M2 made more accurate predictions than the MLP in all postures and electrode locations (p < 0.003). For estimated MCP joint angles, the N2M2 was less sensitive to noisy EMG signals than the MM or NARX network with respect to error (p < 0.032) as well as the NARX network with respect to correlation (p = 0.007). Additionally, the N2M2 had better online task performance than the NARX network (p ≤ 0.030). Conclusion: Overall, we have found that combining the concepts of machine learning and musculoskeletal modeling has resulted in a more robust joint kinematics decoder than either concept individually. Significance: The outcome of this study may result in a novel, highly reliable controller for powered prosthetic hands.
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