Multibody Dynamics Integrated With Muscle Models and Space-Time Constraints for Optimization of Lifting Movements

2005 ◽  
Author(s):  
Cunjun Huang ◽  
Pradip N. Sheth ◽  
Kevin P. Granata
Keyword(s):  
Multibody Dynamics ◽  
Dynamic Optimization ◽  
Muscle Activation ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Activation Parameters ◽  
Space Time ◽  
Time Constraints ◽  
Joint Torques ◽  
Muscle Models

A multibody dynamics model integrated with space-time constraints based optimization is presented in this paper for generating optimal trajectories of human lifting movements. “Space-time constraints” is a two-point boundary value dynamic optimization technique developed for animation of computer graphics characters and has a significant potential for biomechanics and other mechanical movement based dynamic optimization problems. Optimization results demonstrate the ability to consider different preferences for minimizing the loading of specific joints such as an ankle, or a knee, or a shoulder during the lifting motion and the resulting lifting trajectories are shown to be different. Lumped muscle models to generate the joint torques are incorporated at five joints to model the actuation effects of the muscular system during the dynamic movement. The dynamic optimization is then based on the muscle activation parameters instead of the traditionally used joint torques. The muscle activation model optimization is shown to correlate better with the actual motion tests conducted by the VICON video capture and test data analysis system.

A phase-reduced neuro-mechanical model for insect locomotion: feed-forward stability and proprioceptive feedback

2010 ◽  
Vol 368 (1930) ◽  
pp. 5087-5104 ◽  
Author(s):  
J. Proctor ◽  
R. P. Kukillaya ◽  
P. Holmes
Keyword(s):  
Degrees Of Freedom ◽  
Muscle Activation ◽  
Phase Oscillators ◽  
Joint Torques ◽  
Insect Locomotion ◽  
Phase Reduction ◽  
Activation Dynamics ◽  
Order Of Magnitude ◽  
Sensory Pathway ◽  
Muscle Models

In earlier work, we have developed an integrated model for insect locomotion that includes a central pattern generator (CPG), nonlinear muscles, hexapedal geometry and a representative proprioceptive sensory pathway. Here, we employ phase reduction and averaging theory to replace 264 ordinary differential equations (ODEs), describing bursting neurons in the CPG, their synaptic connections to motoneurons, muscle activation dynamics and sensory neurons, with 24 one-dimensional phase oscillators that describe motoneuronal activation of agonist–antagonist muscle pairs driving the jointed legs. Reflexive feedback is represented by stereotypical spike trains with rates proportional to joint torques, which change phase relationships among the motoneuronal oscillators. Restriction to the horizontal plane, neglect of leg mass and use of Hill-type muscle models yield a biomechanical body–limb system with only three degrees of freedom, and the resulting hybrid dynamical system involves 30 ODEs: reduction by an order of magnitude. We show that this reduced model captures the dynamics of unperturbed gaits and the effects of an impulsive perturbation as accurately as the original one. Moreover, the phase response and coupling functions provide an improved understanding of reflexive feedback mechanisms.

Effects of Optimization Technique on Simulated Muscle Activations and Forces

2020 ◽  
Vol 36 (4) ◽  
pp. 259-278
Author(s):  
Sarah A. Roelker ◽  
Elena J. Caruthers ◽  
Rachel K. Hall ◽  
Nicholas C. Pelz ◽  
Ajit M.W. Chaudhari ◽  
...  
Keyword(s):  
Muscle Function ◽  
Muscle Activation ◽  
Optimization Technique ◽  
Knee Extension ◽  
Optimization Techniques ◽  
Model Choice ◽  
Muscle Forces ◽  
Joint Torques ◽  
Muscle Activations ◽  
Proportional Derivative Controller

Two optimization techniques, static optimization (SO) and computed muscle control (CMC), are often used in OpenSim to estimate the muscle activations and forces responsible for movement. Although differences between SO and CMC muscle function have been reported, the accuracy of each technique and the combined effect of optimization and model choice on simulated muscle function is unclear. The purpose of this study was to quantitatively compare the SO and CMC estimates of muscle activations and forces during gait with the experimental data in the Gait2392 and Full Body Running models. In OpenSim (version 3.1), muscle function during gait was estimated using SO and CMC in 6 subjects in each model and validated against experimental muscle activations and joint torques. Experimental and simulated activation agreement was sensitive to optimization technique for the soleus and tibialis anterior. Knee extension torque error was greater with CMC than SO. Muscle forces, activations, and co-contraction indices tended to be higher with CMC and more sensitive to model choice. CMC’s inclusion of passive muscle forces, muscle activation-contraction dynamics, and a proportional-derivative controller to track kinematics contributes to these differences. Model and optimization technique choices should be validated using experimental activations collected simultaneously with the data used to generate the simulation.

An algorithm comparison for dynamic optimization problems

2012 ◽  
Vol 12 (10) ◽  
pp. 3176-3192 ◽  
Author(s):  
Ignacio G. del Amo ◽  
David A. Pelta ◽  
Juan R. González ◽  
Antonio D. Masegosa
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Algorithm Comparison

Socioeconomic variations and disparity in space–time accessibility in suburban China: A case study of Guangzhou

Urban Studies ◽  
2020 ◽  
pp. 004209802091641
Author(s):  
Zifeng Chen ◽  
Anthony Gar-On Yeh
Keyword(s):  
Structural Equation ◽  
Time Perspective ◽  
Space Time ◽  
Time Constraints ◽  
Service Access ◽  
Service Accessibility ◽  
Home Based ◽  
Household Heads ◽  
And Gender

The concept of conventional place-based accessibility, despite being well researched, tends to ignore people’s space–time constraints arising from mandatory activities (e.g. work and household duties), which confine people’s potential movement and delimit the accessible opportunities. As people with different socioeconomic statuses may have different space–time constraints even while living in similar locations, using the place-based measures could lead to an underestimation of accessibility inequality. This study applies a space–time measure to unravel the disparities in service accessibility in suburban China. Since the late 1970s, suburbanisation in Chinese cities has fostered income inequality and has elevated other dimensions (e.g. institutional status and gender) of disparity within each income class. Within this context, we conduct a case study of suburban neighbourhoods in Guangzhou, based on the activity diary data derived from a home-based questionnaire survey. Findings indicate that the use of a space–time measure effectively captures the disparities in service accessibility among residents in suburban Guangzhou. On the basis of structural equation modelling, we further identify that certain socioeconomic groups (e.g. high-income residents, public sector workers, local hukou holders, male household heads and welfare housing residents) tend to experience fewer space–time constraints from rigid activities, such as work, commuting and household duties, and are thus more advantaged in accessing service facilities. These findings imply that urban planning should address the space–time perspective to promote equal service access for the highly heterogeneous social groups in suburban China and to incorporate time-sensitive policies (e.g. flexitime policies).

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

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