scholarly journals Rapid predictive simulations with complex musculoskeletal models suggest that diverse healthy and pathological human gaits can emerge from similar control strategies

2019 ◽  
Vol 16 (157) ◽  
pp. 20190402 ◽  
Author(s):  
Antoine Falisse ◽  
Gil Serrancolí ◽  
Christopher L. Dembia ◽  
Joris Gillis ◽  
Ilse Jonkers ◽  
...  
Keyword(s):  
Control Strategies ◽  
Three Dimensional ◽  
Human Movement ◽  
Performance Criterion ◽  
Computationally Efficient ◽  
Algorithmic Differentiation ◽  
Direct Collocation ◽  
Musculoskeletal Models ◽  
Model Control ◽  
Predictive Simulations

Physics-based predictive simulations of human movement have the potential to support personalized medicine, but large computational costs and difficulties to model control strategies have limited their use. We have developed a computationally efficient optimal control framework to predict human gaits based on optimization of a performance criterion without relying on experimental data. The framework generates three-dimensional muscle-driven simulations in 36 min on average—more than 20 times faster than existing simulations—by using direct collocation, implicit differential equations and algorithmic differentiation. Using this framework, we identified a multi-objective performance criterion combining energy and effort considerations that produces physiologically realistic walking gaits. The same criterion also predicted the walk-to-run transition and clinical gait deficiencies caused by muscle weakness and prosthesis use, suggesting that diverse healthy and pathological gaits can emerge from the same control strategy. The ability to predict the mechanics and energetics of a broad range of gaits with complex three-dimensional musculoskeletal models will allow testing novel hypotheses about gait control and hasten the development of optimal treatments for neuro-musculoskeletal disorders.

scholarly journals Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 124
Author(s):  
Taha Elmokadem ◽  
Andrey V. Savkin
Keyword(s):  
Obstacle Avoidance ◽  
Precision Agriculture ◽  
Control Strategies ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Problem Formulation ◽  
Computationally Efficient ◽  
Control Approach ◽  
Computational Performance ◽  
Coverage Problems

This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

scholarly journals Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait

2021 ◽  
Vol 288 (1946) ◽  
pp. 20202432
Author(s):  
Friedl De Groote ◽  
Antoine Falisse
Keyword(s):  
Musculoskeletal System ◽  
Degrees Of Freedom ◽  
Human Movement ◽  
Human Gait ◽  
Complex Interactions ◽  
Musculoskeletal Models ◽  
The Central Nervous System ◽  
Fundamental Insight ◽  
Insight Into ◽  
Predictive Simulations

Locomotion results from complex interactions between the central nervous system and the musculoskeletal system with its many degrees of freedom and muscles. Gaining insight into how the properties of each subsystem shape human gait is challenging as experimental methods to manipulate and assess isolated subsystems are limited. Simulations that predict movement patterns based on a mathematical model of the neuro-musculoskeletal system without relying on experimental data can reveal principles of locomotion by elucidating cause–effect relationships. New computational approaches have enabled the use of such predictive simulations with complex neuro-musculoskeletal models. Here, we review recent advances in predictive simulations of human movement and how those simulations have been used to deepen our knowledge about the neuromechanics of gait. In addition, we give a perspective on challenges towards using predictive simulations to gain new fundamental insight into motor control of gait, and to help design personalized treatments in patients with neurological disorders and assistive devices that improve gait performance. Such applications will require more detailed neuro-musculoskeletal models and simulation approaches that take uncertainty into account, tools to efficiently personalize those models, and validation studies to demonstrate the ability of simulations to predict gait in novel circumstances.

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 Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking

2021 ◽  
Author(s):  
Antoine Falisse ◽  
Maarten Afschrift ◽  
Friedl De Groote
Keyword(s):  
Achilles Tendon ◽  
Three Dimensional ◽  
Knee Extensor ◽  
Knee Extension ◽  
Performance Criterion ◽  
Knee Mechanics ◽  
Walking Pattern ◽  
Ankle Kinematics ◽  
Complex Models ◽  
Predictive Simulations

Physics-based predictive simulations have been shown to capture many salient features of human walking. Yet they often fail to produce realistic stance knee mechanics and terminal stance ankle plantarflexion. While the influence of the performance criterion on the predicted walking pattern has been previously studied, the influence of the mechanics has been less explored. Here, we investigated the influence of two mechanical assumptions on the predicted walking pattern: the complexity of the foot segment and the stiffness of the Achilles tendon. We found, through three-dimensional muscle-driven predictive simulations of walking, that modeling the toes and metatarsophalangeal joints, and thus using two-segment instead of single-segment foot models, contributed to robustly eliciting physiological stance knee flexion angles, knee extension torques, and knee extensor activity. Yet modeling toe joints did not improve ankle kinematics, nor did decreasing the Achilles tendon stiffness. The lack of predicted terminal stance ankle plantarflexion thereby remains an open question. Overall, this simulation study shows that not only the performance criterion but also mechanical assumptions affect predictive simulations of walking. Improving the realism of predictive simulations is required for their application in clinical contexts. Here, we suggest that using complex models is needed to yield such realism.

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).

scholarly journals Algorithmic differentiation improves the computational efficiency of OpenSim-based optimal control simulations of movement

2019 ◽  
Author(s):  
Antoine Falisse ◽  
Gil Serrancolí ◽  
Christopher L. Dembia ◽  
Joris Gillis ◽  
Friedl De Groote
Keyword(s):  
Optimal Control ◽  
Differential Equations ◽  
Three Dimensional ◽  
Second Order ◽  
Computational Time ◽  
Code Transformation ◽  
Algorithmic Differentiation ◽  
Implicit Differential Equations ◽  
Direct Collocation ◽  
Source Code Transformation

AbstractAlgorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primarily aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based optimal control simulations. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to perform optimal control simulations. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of optimal control simulations as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of optimal control simulations, which will facilitate their use for biomechanical applications.

scholarly journals Predictive simulations of musculoskeletal function and jumping performance in a generalized bird

2021 ◽  
Author(s):  
P J Bishop ◽  
A Falisse ◽  
F De Groote ◽  
J R Hutchinson
Keyword(s):  
Optimal Control ◽  
Three Dimensional ◽  
Muscle Performance ◽  
Whole Body ◽  
Distal Limb ◽  
Jump Performance ◽  
Everyday Activity ◽  
Direct Collocation ◽  
Musculoskeletal Function ◽  
Predictive Simulations

Abstract Jumping is a common, but demanding, behaviour that many animals employ during everyday activity. In contrast to jump-specialists such as anurans and some primates, jumping biomechanics and the factors that influence performance remains little studied for generalized species that lack marked adaptations for jumping. Computational biomechanical modelling approaches offer a way of addressing this in a rigorous, mechanistic fashion. Here, optimal control theory and musculoskeletal modelling are integrated to generate predictive simulations of maximal height jumping in a small ground-dwelling bird, a tinamou. A three-dimensional musculoskeletal model with 36 actuators per leg is used, and direct collocation is employed to formulate a rapidly solvable optimal control problem involving both liftoff and landing phases. The resulting simulation raises the whole-body centre of mass to over double its standing height, and key aspects of the simulated behaviour qualitatively replicate empirical observations for other jumping birds. However, quantitative performance is lower, with reduced ground forces, jump heights and muscle–tendon power. A pronounced countermovement manoeuvre is used during launch. The use of a countermovement is demonstrated to be critical to the achievement of greater jump heights, and this phenomenon may only need to exploit physical principles alone to be successful; amplification of muscle performance may not necessarily be a proximate reason for the use of this manoeuvre. Increasing muscle strength or contractile velocity above nominal values greatly improves jump performance, and interestingly has the greatest effect at more distal limb extensor muscles (i.e., those of the ankle), suggesting that the distal limb may be a critical link for jumping behaviour. These results warrant a re-evaluation of previous inferences of jumping ability in some extinct species with foreshortened distal limb segments, such as dromaeosaurid dinosaurs.

scholarly journals Filtering Biomechanical Signals in Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4580
Author(s):  
Francesco Crenna ◽  
Giovanni Battista Rossi ◽  
Marta Berardengo
Keyword(s):  
Measurement Uncertainty ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Movement Analysis ◽  
Analytical Signal ◽  
Human Movement ◽  
Reference Points ◽  
Biomechanical Analysis ◽  
Method Performance ◽  
Acceleration Signal

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.

scholarly journals A 3D hydrodynamic flow-focusing device for cell sorting

2021 ◽  
Vol 25 (3) ◽  
Author(s):  
Xiaofei Yuan ◽  
Andrew Glidle ◽  
Hitoshi Furusho ◽  
Huabing Yin
Keyword(s):  
Cell Sorting ◽  
Control Strategies ◽  
Three Dimensional ◽  
Hydrodynamic Flow ◽  
Proof Of Concept ◽  
Flow Focusing ◽  
Hydrodynamic Focusing ◽  
Cell Handling ◽  
Fluid Flow Control ◽  
High Degree

AbstractOptical-based microfluidic cell sorting has become increasingly attractive for applications in life and environmental sciences due to its ability of sophisticated cell handling in flow. The majority of these microfluidic cell sorting devices employ two-dimensional fluid flow control strategies, which lack the ability to manipulate the position of cells arbitrarily for precise optical detection, therefore resulting in reduced sorting accuracy and purity. Although three-dimensional (3D) hydrodynamic devices have better flow-focusing characteristics, most lack the flexibility to arbitrarily position the sample flow in each direction. Thus, there have been very few studies using 3D hydrodynamic flow focusing for sorting. Herein, we designed a 3D hydrodynamic focusing sorting platform based on independent sheath flow-focusing and pressure-actuated switching. This design offers many advantages in terms of reliable acquisition of weak Raman signals due to the ability to precisely control the speed and position of samples in 3D. With a proof-of-concept demonstration, we show this 3D hydrodynamic focusing-based sorting device has the potential to reach a high degree of accuracy for Raman activated sorting.

Direct simulation of evolution and control of three-dimensional instabilities in attachment-line boundary layers

1995 ◽  
Vol 291 ◽  
pp. 369-392 ◽  
Author(s):  
Ronald D. Joslin
Keyword(s):  
Boundary Layer ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Plate Boundary ◽  
Navier Stokes ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Simulation Results ◽  
Dimensional Simulation ◽  
Attachment Line

The spatial evolution of three-dimensional disturbances in an attachment-line boundary layer is computed by direct numerical simulation of the unsteady, incompressible Navier–Stokes equations. Disturbances are introduced into the boundary layer by harmonic sources that involve unsteady suction and blowing through the wall. Various harmonic-source generators are implemented on or near the attachment line, and the disturbance evolutions are compared. Previous two-dimensional simulation results and nonparallel theory are compared with the present results. The three-dimensional simulation results for disturbances with quasi-two-dimensional features indicate growth rates of only a few percent larger than pure two-dimensional results; however, the results are close enough to enable the use of the more computationally efficient, two-dimensional approach. However, true three-dimensional disturbances are more likely in practice and are more stable than two-dimensional disturbances. Disturbances generated off (but near) the attachment line spread both away from and toward the attachment line as they evolve. The evolution pattern is comparable to wave packets in flat-plate boundary-layer flows. Suction stabilizes the quasi-two-dimensional attachment-line instabilities, and blowing destabilizes these instabilities; these results qualitatively agree with the theory. Furthermore, suction stabilizes the disturbances that develop off the attachment line. Clearly, disturbances that are generated near the attachment line can supply energy to attachment-line instabilities, but suction can be used to stabilize these instabilities.

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