An EMG-Assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities : Towards a Rapid Assessment Toolbox

Publisher Copyright: © 1964-2012 IEEE.Joint tissue mechanics (e.g., stress and strain) are believed to have a major involvement in the onset and progression of musculoskeletal disorders, e.g., knee osteoarthritis (KOA). Accordingly, considerable efforts have been made to develop musculoskeletal finite element (MS-FE) models to estimate highly detailed tissue mechanics that predict cartilage degeneration. However, creating such models is time-consuming and requires advanced expertise. This limits these complex, yet promising, MS-FE models to research applications with few participants and makes the models impractical for clinical assessments. Also, these previously developed MS-FE models have not been used to assess activities other than gait. This study introduces and verifies a semi-automated rapid state-of-the-art MS-FE modeling and simulation toolbox incorporating an electromyography- (EMG) assisted MS model and a muscle-force driven FE model of the knee with fibril-reinforced poro(visco)elastic cartilages and menisci. To showcase the usability of the pipeline, we estimated joint- and tissue-level knee mechanics in 15 KOA individuals performing different daily activities. The pipeline was verified by comparing the estimated muscle activations and joint mechanics to existing experimental data. To determine the importance of the EMG-assisted MS analysis approach, results were compared to those from the same FE models but driven by static-optimization-based MS models. The EMG-assisted MS-FE pipeline bore a closer resemblance to experiments compared to the static-optimization-based MS-FE pipeline. Importantly, the developed pipeline showed great potential as a rapid MS-FE analysis toolbox to investigate multiscale knee mechanics during different activities of individuals with KOA.Peer reviewe

SPADA: A Toolbox of Designing Soft Pneumatic Actuators for Shape Matching based on Surrogate Modeling

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This paper addresses these challenges, offering an end-to-end shape-matching design framework for bellow-SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a GUI for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, Finite Element Method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An Artificial Neural Network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging 3D PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match 2D shapes with root-mean-square errors of 4.16, 2.70, and 2.51mm, and demonstrating its potential by designing a 3D deformable actuator

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Computational Design Optimization of Arc Welding Process for Reduced Distortion in Welded Structures

An effective approach to determine optimum welding process parameters is implementation of advanced computer aided engineering (CAE) tool that integrates efficient optimization techniques and numerical welding simulation. In this thesis, an automated computational methodology to determine optimum arc welding process parameters is proposed. It is a coupled Genetic Algorithms (GA) and Finite Element (FE) based optimization method where GA directly utilizes output responses of FE based welding simulations for iterative optimization. Effectiveness of the method has been demonstrated by predicting optimum parameters of a lap joint specimen of two thin steel plates and automotive structure of nonlinear welding path for minimum distortion. Three dimensional FE models have been developed to simulate the arc welding process and subsequently, the models have been used by GA as the evaluation model for optimization. The optimization results show that such a CAE based methodology can contribute to facilitate the product design and development

Numerical simulation of a single floating point absorber wave energy converter using OpenFOAM

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Synthesis of Subject-Specific Human Balance Responses using a Task-Level Neuromuscular Control Platform

Many activities of daily living require a high level of neuromuscular coordination and balance control to avoid falls. Complex musculoskeletal models paired with detailed neuromuscular simulations complement experimental studies and uncover principles of coordinated and uncoordinated movements. Here, we created a closed-loop forward dynamic simulation framework that utilizes a detailed musculoskeletal model (19 degrees of freedom, and 92 Muscles) to synthesize human balance responses after support-surface perturbation. In addition, surrogate response models of task-level experimental kinematics from two healthy subjects were provided as inputs to our closedloop simulations to inform the design of the task-level controller. The predicted muscle EMGs and the resulting synthesized subject joint angles showed good conformity with the average of experimental trials. The simulated whole-body center of mass displacements, generated from a single kinematics trial per perturbation direction, were on average, within 7 mm (anterior perturbations) and 13 mm (posterior perturbations) of experimental displacements. Our results confirmed how a complex subject-specific movement can be reconstructed by sequencing and prioritizing multiple task-level commands to achieve desired movements. By combining the multidisciplinary approaches of robotics and biomechanics, the platform demonstrated here offers great potential for studying human movement control and subject-specific outcome prediction

Thermomechanical modeling and optimization of friction stir welding

This thesis research implemented an existing thermomechanical model of friction stir welding process, and studied the surrogate model-based optimization approach to obtain optimal process parameters for the modeled friction stir welding process. As an initial step, the thermomechanical model developed by Zhu and Chao for friction stir welding of 304L stainless steel was replicated using ANSYS. The developed model was then used to conduct parametric studies to understand the effect of various input parameters like total rate of heat input, welding speed and clamping location on temperature distribution and residual stress in the workpiece. With the data from the simulated model, linear and nonlinear surrogate models were constructed using regression analysis to relate the selected input process parameters with response variables. Constrained optimization models were formulated using surrogate models and optimization of process parameters for minimizing cost and maximizing throughput was carried out using improved harmony search algorithm. To handle the constraints, Deb’s parameter-less penalty method was used and implemented in the algorithm. It is learned from this research that: (1) heat input is mainly constrained by the lower bound of the temperature for making good welds; (2) the optimal welding speed must balance the loss of heat input and the gain in productivity; (3) clamping closer to the weld is better than away from the weld in terms of lowering the peak residual stresses. Moreover, the nonlinear surrogate models resulted in a slightly better optimal solution than the linear models when wide temperature range was used. However, for tight temperature constraints, optimization on linear surrogate models produced better results. The implemented improved harmony search algorithm seems not able to converge to the best solution in every run. Nevertheless, the non-converged solution it found was very close to the best

Experimental Modeling of NOx and PM Generation from Combustion of Various Biodiesel Blends for Urban Transport Buses

Biodiesel has diverse sources of feedstock and the amount and composition of its emissions vary significantly depending on combustion conditions. Results of laboratory and field tests reveal that nitrogen oxides (NOx) and particulate matter (PM) emissions from biodiesel are influenced more by combustion conditions than emissions from regular diesel. Therefore, NOx and PM emissions documented through experiments and modeling studies are the primary focus of this investigation. In addition, a comprehensive analysis of the feedstock-related combustion characteristics and pollutants are investigated. Research findings verify that the oxygen contents, the degree of unsaturation, and the size of the fatty acids in biodiesel are the most important factors that determine the amounts and compositions of NOx and PM emissions