Optimality and Modularity in Human Movement: From Optimal Control to Muscle Synergies

2018 ◽  
pp. 105-133 ◽  
Author(s):  
Bastien Berret ◽  
Ioannis Delis ◽  
Jérémie Gaveau ◽  
Frédéric Jean
Keyword(s):  
Optimal Control ◽  
Human Movement ◽  
Muscle Synergies

scholarly journals Optimal control approaches for combining medicines and mosquito control in tackling dengue

2020 ◽  
Vol 7 (4) ◽  
pp. 181843 ◽  
Author(s):  
Thomas Rawson ◽  
Kym E. Wilkins ◽  
Michael B. Bonsall
Keyword(s):  
Optimal Control ◽  
Disease Control ◽  
Mosquito Control ◽  
Large Population ◽  
Human Movement ◽  
Mitigation Strategies ◽  
Disease Dynamics ◽  
Vector Ecology ◽  
Dengue Disease ◽  
The Impact

Dengue is a debilitating and devastating viral infection spread by mosquito vectors, and over half the world’s population currently live at risk of dengue (and other flavivirus) infections. Here, we use an integrated epidemiological and vector ecology framework to predict optimal approaches for tackling dengue. Our aim is to investigate how vector control and/or vaccination strategies can be best combined and implemented for dengue disease control on small networks, and whether these optimal strategies differ under different circumstances. We show that a combination of vaccination programmes and the release of genetically modified self-limiting mosquitoes (comparable to sterile insect approaches) is always considered the most beneficial strategy for reducing the number of infected individuals, owing to both methods having differing impacts on the underlying disease dynamics. Additionally, depending on the impact of human movement on the disease dynamics, the optimal way to combat the spread of dengue is to focus prevention efforts on large population centres. Using mathematical frameworks, such as optimal control, are essential in developing predictive management and mitigation strategies for dengue disease control.

scholarly journals Muscle Synergies Reliability in the Power Clean Exercise

2020 ◽  
Vol 5 (4) ◽  
pp. 75
Author(s):  
Paulo D. G. Santos ◽  
João R. Vaz ◽  
Paulo F. Correia ◽  
Maria J. Valamatos ◽  
António P. Veloso ◽  
...  
Keyword(s):  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Human Movement ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Complex Task ◽  
Muscle Coordination ◽  
High Similarity ◽  
Strength Tests ◽  
Power Clean

Muscle synergy extraction has been utilized to investigate muscle coordination in human movement, namely in sports. The reliability of the method has been proposed, although it has not been assessed previously during a complex sportive task. Therefore, the aim of the study was to evaluate intra- and inter-day reliability of a strength training complex task, the power clean, assessing participants’ variability in the task across sets and days. Twelve unexperienced participants performed four sets of power cleans in two test days after strength tests, and muscle synergies were extracted from electromyography (EMG) data of 16 muscles. Three muscle synergies accounted for almost 90% of variance accounted for (VAF) across sets and days. Intra-day VAF, muscle synergy vectors, synergy activation coefficients and individual EMG profiles showed high similarity values. Inter-day muscle synergy vectors had moderate similarity, while the variables regarding temporal activation were still strongly related. The present findings revealed that the muscle synergies extracted during the power clean remained stable across sets and days in unexperienced participants. Thus, the mathematical procedure for the extraction of muscle synergies through nonnegative matrix factorization (NMF) may be considered a reliable method to study muscle coordination adaptations from muscle strength programs.

scholarly journals Muscle synergies are associated with intermuscular coherence in an isometric upper limb task

2019 ◽  
Author(s):  
Pablo Ortega-Auriol ◽  
Winston D Byblow ◽  
Angus JC McMorland
Keyword(s):  
Functional Connectivity ◽  
Upper Limb ◽  
Physiological Mechanism ◽  
Human Movement ◽  
Muscle Synergies ◽  
Alpha Band ◽  
Muscle Weight ◽  
Cortex Area ◽  
Higher Weights ◽  
Intermuscular Coherence

AbstractTo elucidate the underlying physiological mechanism of muscle synergies, we investigated the functional corticomuscular and intermuscular binding during an isometric upper limb task in 14 healthy participants. Cortical activity was recorded using 32-channel encephalography (EEG) and muscle activity using 16-channel electromyography (EMG). Using non-negative matrix factorization (NMF), we calculated muscle synergies from two different tasks. A preliminary multidirectional task was used to identify synergy preferred directions. A subsequent coherence task, consisting of generating forces isometrically in the synergy PDs, was used to assess the functional connectivity properties of synergies. Functional connectivity was estimated using corticomuscular coherence (CMC) and intermuscular coherence (IMC). Overall, we were able to extract four different synergies from the multidirectional task. A significant alpha band IMC was present consistently in all extracted synergies. Moreover, alpha band IMC was higher between muscles with higher weights within a synergy. In contrast, no significant CMC was found between the motor cortex area and synergy muscles. In addition, there is a relationship between a synergy muscle weight and the level of IMC. Our findings suggest the existence of a consistent shared input between muscles of each synergy. Finally, the existence of a shared input onto synergistic muscles within a synergy supports the idea of neurally-derived muscle synergies that build human movement.

Muscle Contracture Modeling and Optimal Control for Crouch Gait Prediction

2020 ◽  
Author(s):  
Matilde Tomasi ◽  
Alessio Artoni
Keyword(s):  
Optimal Control ◽  
Human Movement ◽  
Gait Pattern ◽  
Computational Framework ◽  
Control Framework ◽  
Crouch Gait ◽  
Pathological Gait ◽  
Muscle Groups ◽  
Muscle Activations ◽  
Research Challenge

Abstract Prediction of human movement, and especially of pathological gait, is nowadays an important and mostly unsolved research challenge. In this work, a recently developed computational framework based on optimal control was adopted and explored to assess its potential for predicting a pathological gait pattern, in particular the crouch gait typical of subjects affected by cerebral palsy. To this end, the generic musculoskeletal model on which this optimal control framework is based was made representative of such pathological case by modeling contracture of relevant muscle groups commonly associated with crouch gait, namely knee and hip flexors. All the conducted simulations succeeded in inducing the model into a crouch gait pattern, despite their diversity in cost functions. Moreover, the obtained joint angle trajectories correlated well with the experimental ones obtained from a CP child walking in crouch. These kinematic results suggest that optimal control techniques and proper tuning of musculotendon parameters are an important pairing for predictive simulations of human walking. On the other hand, the obtained results confirm that estimation of muscle activations is strongly dependent on the selected objective function and still requires deeper investigations.

scholarly journals Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Nicholas A. Bianco ◽  
Carolynn Patten ◽  
Benjamin J. Fregly
Keyword(s):  
Muscle Force ◽  
Human Movement ◽  
Optimization Methods ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Large Set ◽  
Dimensional Representation ◽  
Joint Contact ◽  
Low Dimensional

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called “muscle excitations”), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called “included” muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called “excluded” muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called “synergy extrapolation”). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

Optimal Control Modeling of Human Movement

2018 ◽  
pp. 327-348 ◽  
Author(s):  
Brian R. Umberger ◽  
Ross H. Miller
Keyword(s):  
Optimal Control ◽  
Human Movement ◽  
Control Modeling

scholarly journals Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1638 ◽  
Author(s):  
Leng-Feng Lee ◽  
Brian R. Umberger
Keyword(s):  
Optimal Control ◽  
Open Source ◽  
Human Mobility ◽  
Human Movement ◽  
Great Promise ◽  
Shooting Methods ◽  
Simulation And Optimization ◽  
Direct Collocation ◽  
Data Tracking ◽  
Predictive Simulations

Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1–2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This should allow researchers to more readily use predictive simulation as a tool to address clinical conditions that limit human mobility.

scholarly journals Bioptim, a Python framework for Musculoskeletal Optimal Control in Biomechanics

2021 ◽  
Author(s):  
Benjamin Michaud ◽  
François Bailly ◽  
Eve Charbonneau ◽  
Amedeo Ceglia ◽  
Léa Sanchez ◽  
...  
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Motion Tracking ◽  
Human Movement ◽  
Parameters Optimization ◽  
Multiple Shooting ◽  
Maximum Height ◽  
Moving Horizon Estimation ◽  
Computationally Efficient ◽  
Algorithmic Differentiation

Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal models. Relying on algorithmic differentiation and the multiple shooting formulation, bioptim interfaces nonlinear solvers to quickly provide dynamically consistent optimal solutions. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control. Six contrasting examples are presented, comprising various models, dynamics, objective functions and constraints. They include data-driven simulations (i.e., a multiphase muscle driven gait cycle and an upper-limb real-time moving horizon estimation of muscle forces) and predictive simulations (i.e., a muscle-driven pointing task, a twisting somersault with a quaternion-based model, a position controller using external forces, and a multiphase torque-driven maximum-height jump motion).

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