Physics-based simulations to predict the differential effects of motor control and musculoskeletal deficits on gait dysfunction in cerebral palsy : a retrospective case study

Physics-based simulations of walking have the theoretical potential to support clinical decision-making by predicting the functional outcome of treatments in terms of walking performance. Yet before using such simulations in clinical practice, their ability to identify the main treatment targets in specific patients needs to be demonstrated. In this study, we generated predictive simulations of walking with a medical imaging based neuro-musculoskeletal model of a child with cerebral palsy presenting crouch gait. We explored the influence of altered muscle-tendon properties, reduced neuromuscular control complexity, and spasticity on gait dysfunction in terms of joint kinematics, kinetics, muscle activity, and metabolic cost of transport. We modeled altered muscle-tendon properties by personalizing Hill-type muscle-tendon parameters based on data collected during functional movements, simpler neuromuscular control by reducing the number of independent muscle synergies, and spasticity through delayed muscle activity feedback from muscle force and force rate. Our simulations revealed that, in the presence of aberrant musculoskeletal geometries, altered muscle-tendon properties rather than reduced neuromuscular control complexity and spasticity were the primary cause of the crouch gait pattern observed for this child, which is in agreement with the clinical examination. These results suggest that muscle-tendon properties should be the primary target of interventions aiming to restore an upright gait pattern for this child. This suggestion is in line with the gait analysis following muscle-tendon property and bone deformity corrections. Future work should extend this single case analysis to more patients in order to validate the ability of our physics-based simulations to capture the gait patterns of individual patients pre- and post-treatment. Such validation would open the door for identifying targeted treatment strategies with the aim of designing optimized interventions for neuro-musculoskeletal disorders

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

ESTIMATING LOWER LIMB JOINT MOMENTS IN GAIT USING COMMON MACHINE LEARNING APPROACHES

The aim of this study was to investigate the efficacy of common machine learning algorithmic approaches to estimate lower limb joint moments during fast walking gait. Kinematic and ground reaction force data on 19 participants were captured with a force-plate and motion caption capture system. Inverse dynamics was used to calculate the right lower limb joint moments and common machine learning algorithmic approaches, such as Random Forest (RF), Linear Regression (LR), Neural Network (NN), AdaBoost (AB) and Gradient Boosting, were used to predict the corresponding joint moments using only the kinematic data. High coefficient of determination values (R2\u3e0.9) for predicting moments using random forest, neural network and AdaBoost are observed in for the ankle, knee and hip joints in frontal, sagittal and transverse planes. The other approaches had R2 values between ranged 0.71 and 0.97. This suggests that common machine learning algorithms may be a feasible approach to estimate joint moments during fast walking in a clinical setting for monitoring sport injury prevention and management

Evaluation of the accuracy of musculoskeletal simulation during squats by means of instrumented knee prostheses

Standard musculoskeletal simulation tools now offer widespread access to internal loading conditions for use in improving rehabilitation concepts or training programmes. However, despite broad reliance on their outcome, the accuracy of such loading estimations, specifically in deep knee flexion, remains generally unknown. The aim of this study was to evaluate the error of tibio-femoral joint contact force (JCF) calculations using musculoskeletal simulation compared to in vivo measured JCFs in subjects with instrumented total knee endoprostheses during squat exercises. Using the early but common “Gait2392_simbody” (OpenSim) scaled musculoskeletal models, tibio-femoral JCFs were calculated in 6 subjects for 5 repetitions of squats. Tibio-femoral JCFs of 0.8–3.2 times bodyweight (BW) were measured. While the musculoskeletal simulations underestimated the measured knee JCFs at low flexion angles, an average error of less than 20% was achieved between approximately 25°–60° knee flexion. With an average error that behaved almost linearly with knee flexion angle, an overestimation of approximately 60% was observed at deep flexion (ca. 80°), with an absolute maximum error of ca. 1.9BW. Our data indicate that loading estimations from early musculoskeletal gait models at both high and low knee joint flexion angles should be interpreted carefully

Predicting kinetics using musculoskeletal modeling and inertial motion capture

Inverse dynamic analysis using musculoskeletal modeling is a powerful tool, which is utilized in a range of applications to estimate forces in ligaments, muscles, and joints, non-invasively. To date, the conventional input used in this analysis is derived from optical motion capture (OMC) and force plate (FP) systems, which restrict the application of musculoskeletal models to gait laboratories. To address this problem, we propose a musculoskeletal model, capable of estimating the internal forces based solely on inertial motion capture (IMC) input and a ground reaction force and moment (GRF&M) prediction method. We validated the joint angle and kinetic estimates of the lower limbs against an equally constructed musculoskeletal model driven by OMC and FP system. The sagittal plane joint angles of ankle, knee, and hip presented excellent Pearson correlations (\rho = 0.95, 0.99, and 0.99, respectively) and root-mean-squared differences (RMSD) of 4.1 $\pm$ 1.3$\circ$, 4.4 $\pm$ 2.0$\circ$, and 5.7 $\pm$ 2.1$\circ$, respectively. The GRF&M predicted using IMC input were found to have excellent correlations for three components (vertical:\rho = 0.97, RMSD=9.3 $\pm$ 3.0 %BW, anteroposterior: \rho = 0.91, RMSD=5.5 $\pm$ 1.2 %BW, sagittal: \rho = 0.91, RMSD=1.6 $\pm$ 0.6 %BW*BH), and strong correlations for mediolateral (\rho = 0.80, RMSD=2.1 $\pm$ 0.6%BW ) and transverse (\rho = 0.82, RMSD=0.2 $\pm$ 0.1 %BW*BH). The proposed IMC-based method removes the complexity and space-restrictions of OMC and FP systems and could enable applications of musculoskeletal models in either monitoring patients during their daily lives or in wider clinical practice.Comment: 19 pages, 4 figures, 3 table

Influence of musculoskeletal model parameter values on prediction of accurate knee contact forces during walking

Treatment design for musculoskeletal disorders using in silico patient-specific dynamic simulations is becoming a clinical possibility. However, these simulations are sensitive to model parameter values that are difficult to measure experimentally, and the influence of uncertainties in these parameter values on the accuracy of estimated knee contact forces remains unknown. This study evaluates which musculoskeletal model parameters have the greatest influence on estimating accurate knee contact forces during walking. We performed the evaluation using a two-level optimization algorithm where musculoskeletal model parameter values were adjusted in the outer level and muscle activations were estimated in the inner level. We tested the algorithm with different sets of design variables (combinations of optimal muscle fiber lengths, tendon slack lengths, and muscle moment arm offsets) resulting in nine different optimization problems. The most accurate lateral knee contact force predictions were obtained when tendon slack lengths and moment arm offsets were adjusted simultaneously, and the most accurate medial knee contact force estimations were obtained when all three types of parameters were adjusted together. Inclusion of moment arm offsets as design variables was more important than including either tendon slack lengths or optimal muscle fiber lengths alone to obtain accurate medial and lateral knee contact force predictions. These results provide guidance on which musculoskeletal model parameter values should be calibrated when seeking to predict in vivo knee contact forces accurately.Postprint (updated version

Musculoskeletal Loads during Stationary Cycling and the Effects of Pedal Modifications for Knee Osteoarthritis

Knee OA is a deteriorating joint disease affecting 27 million people in the US and current exercise prescriptions may be improved with new knowledge of their effects on muscle forces and joint contact loads. Cycling rather than other exercise modalities is generally considered an alternative for people with knee OA. If these research objectives were achieved, clinicians would have additional tools related to joint contact loads for treating people with OA with an cycling exercise while controlling progression of OA. The long-term goal of this research is to provide a scientific basis for planning, evaluation and improvement of subject-specific rehabilitation for subjects with knee osteoarthritis (OA). The principles governing relationships between muscle forces, joint contact loads and movements in people with knee OA, have not been discovered. Determining how to adjust movements to optimize joint contact loads is difficult because experiments do not account for these loads. In combination with experimental approaches, muscle-actuated inverse dynamic simulations provide a scientific framework to estimate important variables and identify cause-and- effect relationships. These activities challenge existing paradigms for exercise prescriptions by including movements specifically designed for decreasing knee joint contact loads. The research objective is to investigate muscle forces and joint contact loads that are experienced by the knee during cycling. The overall hypothesis is a combination of biomechanical cycling modifications that contribute to altered muscle forces and a reduction in knee joint contact loads in subjects with and without knee OA during cycling; this may be mitigated with a novel pedal design. The overall purpose of this research was to discover relationships between muscle forces, joint contact loads, cycling and OA-friendly cycling modifications for improving exercise prescriptions. The following objectives were addressed: 1) determine the effects of lateral pedal wedges and toe-in on joint biomechanics during cycling and 2) examine the potential of optimization to design subject-specific cycling modifications for decreasing knee joint contact loads

Comparison of knee loading during walking via musculoskeletal modelling using marker-based and IMU-based approaches

openThe current thesis is the result of the candidate's work over a six-month period with the assistance of the supervisor and co-supervisors, thanks to the collaboration between the Human Movement Bioengineering Laboratory Research group at the University of Padova (Italy) and the Human Movement Biomechanics Research group at KU Leuven (Belgium). Gait analysis, at a clinical level, is a diagnostic test with multiple potentials, in particular in identifying functional limitations related to a pathological path. Three-dimensional motion capture is now consolidated as an approach for human movement research studies and consists of a set of very precise measurements, the latter are processed by biomechanical models, and curves relating to the kinematics and indirect dynamics, i.e., the joint angles and the relative forces and moments, can be obtained. These results are considered fully reliable and based on these curves it is decided how to intervene on the specific subject to make the path as less pathological as possible. However, the use of wearable sensors (IMUs) consisting of accelerometers, gyroscopes, and magnetic sensors for gait analysis, has increased in the last decade due to the low production costs, portability, and small size that have allowed for studies in everyday life conditions. Inertial capture (InCap) systems have become an appealing alternative to 3D Motion Capture (MoCap) systems due to the ability of inertial measurement units (IMUs) to estimate the orientation of 3D sensors and segments. Musculoskeletal modelling and simulation provide the ideal framework to examine quantities in silico that cannot be measured in vivo, such as musculoskeletal loading, muscle forces and joint contact forces. The specific software used in this study is Opensim: an open-source software that allows modelling, analysis, and simulation of the musculoskeletal system. The aim of this thesis is to compare a marker-based musculoskeletal modelling approach with an IMUs-based one, in terms of kinematics, dynamics, and muscle activations. In particular, the project will focus on knee loading, using an existing musculoskeletal model of the lower limb. The current project was organized as follows: first, the results for the MoCap approach were obtained, following a specific workflow that used the COMAK IK tool and the COMAK algorithm to get the secondary knee kinematics, muscle activations, and knee contact forces. Where COMAK is a modified static optimization algorithm that solves for muscle activations and secondary kinematics to obtain measured primary DOF accelerations while minimizing muscle activation. Then these results were used to make a comparison with those obtained by the inertial-based approach, with the attempt to use as little information as possible from markers while estimating kinematics from IMU data using an OpenSim toolbox called OpenSense. Afterward, in order to promote an approach more independent from the constraints of a laboratory, the Zero Moment Point (ZMP) method was used to estimate the center of pressure position of the measured ground reaction forces (GRFs), and a specific Matlab code was implemented to improve this estimation. Using the measured GRFs with the new CoPs, the results of Inverse Dynamics, muscle activations, and finally knee loading were calculated and compared to the MoCap results. The final step was to conduct a statistical analysis to compare the two approaches and emphasize the importance of using IMUs for gait analysis, particularly to study knee mechanics

Gait Modeling Using Genetic Algorithm Optimized Four-Bar Linkages

Important in diagnosing gait abnormalities and pathologies is knowing the position of the leg at various points throughout the gait cycle. This is currently done with motion capture technology but the demand for Inertial Measurement Unit (IMU) based navigation and position tracking has been on the rise. A required component of this alternative is a gait model that can accurately predict the position of points of interest. In this thesis, a Kalman Filter is constructed using a contrived model to test if, given an accurate gait model, the filter can converge to an accurate and true position solution. Also presented is a Genetic Algorithm approach to dynamic system modeling. The dynamic system is made up of a four-bar linkage and has the ability to adapt to different gaits, both healthy and pathological. Results for the Kalman Filter are illustrated through convergence plots, and final position solutions and results for the Genetic Algorithm are given by position solutions of the four-bar linkage. These results show that a genetic approach is robust and has application in gait analysi