Complete Solution Sets for Neuromuscular Models Reveal How Mechanical Constraints Limit Neural Control Options

2010 ◽  
Author(s):  
Jason J. Kutch ◽  
Francisco J. Valero-Cuevas
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Muscle Activation ◽  
Complete Solution ◽  
The Body ◽  
Joint Torques ◽  
Mechanical Constraints ◽  
Geometric Methods ◽  
Muscle Activations ◽  
Fingertip Forces

One of the main goals of neuromuscular modeling is to establish the range of feasible muscle activations for a given mechanical output of the body. This is not a trivial problem because there are typically infinitely many combinations of muscle activations that will generate the same joint torques, as most joints are actuated by more muscles than rotational degrees of freedom. Here we show that well-established geometric methods easily provide a complete description of the set of muscle activations that generate a desired set of joint torques or endpoint forces. In contrast to iterative linear programming optimizations, geometric methods provide a set of solutions in muscle activation space simply by converting between the geometric representations of neural and mechanical constraints. As an example, we use geometric methods to find the feasible set of activations that produce fingertip forces in a set of directions. These results show that for a given set of fingertip forces, the range of feasible activation for each muscle can differ with the choice of mechanical constraints. Thus, the mechanical constraints of the task play an important role governing the options the nervous system has when controlling redundant muscles.

Neuromuscular Adaptation During Skill Acquisition on a Two Degree-of-Freedom Target-Acquisition Task: Isometric Torque Production

2005 ◽  
Vol 94 (5) ◽  
pp. 3046-3057 ◽  
Author(s):  
Jonathan Shemmell ◽  
Matthew Forner ◽  
James R. Tresilian ◽  
Stephan Riek ◽  
Benjamin K. Barry ◽  
...  
Keyword(s):  
Skill Acquisition ◽  
Neural Control ◽  
Muscle Activation ◽  
Joint Torque ◽  
Acquisition Time ◽  
Neutral Position ◽  
Target Acquisition ◽  
Joint Torques ◽  
Flexion Extension ◽  
Torque Development

In this study we attempted to identify the principles that govern the changes in neural control that occur during repeated performance of a multiarticular coordination task. Eight participants produced isometric flexion/extension and pronation/supination torques at the radiohumeral joint, either in isolation (e.g., flexion) or in combination (e.g., flexion–supination), to acquire targets presented by a visual display. A cursor superimposed on the display provided feedback of the applied torques. During pre- and postpractice tests, the participants acquired targets in eight directions located either 3.6 cm (20% maximal voluntary contraction [MVC]) or 7.2 cm (40% MVC) from a neutral cursor position. On each of five consecutive days of practice the participants acquired targets located 5.4 cm (30% MVC) from the neutral position. EMG was recorded from eight muscles contributing to torque production about the radiohumeral joint during the pre- and posttests. Target-acquisition time decreased significantly with practice in most target directions and at both target torque levels. These performance improvements were primarily associated with increases in the peak rate of torque development after practice. At a muscular level, these changes were brought about by increases in the rates of recruitment of all agonist muscles. The spatiotemporal organization of muscle synergies was not significantly altered after practice. The observed adaptations appear to lead to performances that are generalizable to actions that require both greater and smaller joint torques than that practiced, and may be successfully recalled after a substantial period without practice. These results suggest that tasks in which performance is improved by increasing the rate of muscle activation, and thus the rate of joint torque development, may benefit in terms of the extent to which acquired levels of performance are maintained over time.

Estimation of the 2-DOF Time-Varying Impedance of the Human Ankle

2017 ◽  
Author(s):  
Evandro Ficanha ◽  
Guilherme Aramizo Ribeiro ◽  
Lauren Knop ◽  
Mohammad Rastgaar Aagaah
Keyword(s):  
Degrees Of Freedom ◽  
Muscle Activation ◽  
Stance Phase ◽  
Impedance Control ◽  
The Body ◽  
Time Varying ◽  
Ankle Impedance ◽  
Impedance Modulation ◽  
Human Ankle ◽  
Vibrating Platform

The human ankle plays a major role in locomotion as it the first major joint to transfer the ground reaction torques to the rest of the body while providing power for locomotion and stability. One of the main causes of the ankle impedance modulation is muscle activation [1, 2], which can tune the ankle’s stiffness and damping during the stance phase of gait. The ankle’s time-varying impedance is also task dependent, meaning that different activities such as walking at different speeds, turning, and climbing/descending stairs would impose different profiles of time-varying impedance modulation. The impedance control is commonly used in the control of powered ankle-foot prostheses; however, the information on time-varying impedance of the ankle during the stance phase is limited in the literature. The only previous study during the stance phase, to the best of the authors knowledge, reported the human ankle impedance at four points of the stance phase in dorsiflexion-plantarflexion (DP) [1] during walking. To expand previous work and estimate the impedance in inversion-eversion (IE), a vibrating platform was fabricated (Fig. 1) [3]. The platform allows the ankle impedance to be estimated at 250 Hz in both DP and IE, including combined rotations in both degrees of freedom (DOF) simultaneously. The results can be used in a 2-DOF powered ankle-foot prosthesis developed by the authors, which is capable of mimicking the ankle kinetics and kinematics in the frontal and sagittal planes [4]. The vibrating platform can also be used to tune the prosthesis to assure it properly mimics the human ankle dynamics. This paper describes the results of the preliminary experiments using the vibrating platform on 4 male subjects. For the first time, the time-varying impedance of the human ankle in both DP and IE during walking in a straight line are reported.

Muscle Synergies Modify Optimization Estimates of Joint Stiffness During Walking

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Mohammad S. Shourijeh ◽  
Benjamin J. Fregly
Keyword(s):  
Degrees Of Freedom ◽  
Computational Models ◽  
Joint Stiffness ◽  
The Body ◽  
Grand Challenge ◽  
Lower Body ◽  
Inverse Dynamic ◽  
Co Activation ◽  
Muscle Activations ◽  
The Cost

Abstract Because of its simplicity, static optimization (SO) is frequently used to resolve the muscle redundancy problem (i.e., more muscles than degrees-of-freedom (DOF) in the human musculoskeletal system). However, SO minimizes antagonistic co-activation and likely joint stiffness as well, which may not be physiologically realistic since the body modulates joint stiffness during movements such as walking. Knowledge of joint stiffness is limited due to the difficulty of measuring it experimentally, leading researchers to estimate it using computational models. This study explores how imposing a synergy structure on the muscle activations estimated by optimization (termed “synergy optimization,” or SynO) affects calculated lower body joint stiffnesses during walking. By limiting the achievable muscle activations and coupling all time frames together, a synergy structure provides a potential mechanism for reducing indeterminacy and improving physiological co-activation but at the cost of a larger optimization problem. To compare joint stiffnesses produced by SynO (2–6 synergies) and SO, we used both approaches to estimate lower body muscle activations and forces for sample experimental overground walking data obtained from the first knee grand challenge competition. Both optimizations used a custom Hill-type muscle model that permitted analytic calculation of individual muscle contributions to the stiffness of spanned joints. Both approaches reproduced inverse dynamic joint moments well over the entire gait cycle, though SynO with only two synergies exhibited the largest errors. Maximum and mean joint stiffnesses for hip and knee flexion in particular decreased as the number of synergies increased from 2 to 6, with SO producing the lowest joint stiffness values. Our results suggest that SynO increases joint stiffness by increasing muscle co-activation, and furthermore, that walking with a reduced number of synergies may result in increased joint stiffness and perhaps stability.

Kinematic and Kinetic Constraints on Arm, Trunk, and Leg Segments in Target-Reaching Movements

2005 ◽  
Vol 93 (1) ◽  
pp. 352-364 ◽  
Author(s):  
James S. Thomas ◽  
Daniel M. Corcos ◽  
Ziaul Hasan
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Time Course ◽  
Sagittal Plane ◽  
Principal Component ◽  
Joint Torque ◽  
Trunk Flexion ◽  
Joint Torques ◽  
Joint Task ◽  
Task Conditions

We studied target reaching tasks involving not only the arms but also the trunk and legs, which necessitated some trunk flexion. Such tasks can be successfully completed using an infinite number of combinations of segment motions due to the inherent kinematic redundancy with the excessive degrees of freedom (DOFs). Sagittal plane motions of six segments (shank, thigh, pelvis, trunk, humerus, and forearm) and dynamic torques of six joints (ankle, knee, hip, lumbar, shoulder, and elbow) were analyzed separately by principal component (PC) analyses to determine if there was a commonality among the shapes of the respective waveforms. Additionally, PC analyses were used to probe for constraining relationships among the 1) relative magnitudes of segment excursions and 2) the peak-to-peak dynamic joint torques. In summary, at the kinematic level, the tasks are simplified by the use of a single common waveform for all segment excursions with 89.9% variance accounted for (VAF), but with less fixed relationships among the relative scaling of the magnitude of segment excursions (62.2% VAF). However, at the kinetic level, the time course of the dynamic joint torques are not well captured by a single waveform (72.7% VAF), but the tasks are simplified by relatively fixed relationships among the scaling of dynamic joint torque magnitudes across task conditions (94.7% VAF). Taken together, these results indicate that, while the effective DOFs in a multi-joint task are reduced differently at the kinematic and kinetic levels, they both contribute to simplifying the neural control of these tasks.

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.

scholarly journals Similar muscles contribute to horizontal and vertical acceleration of center of mass in forward and backward walking: implications for neural control

2012 ◽  
Vol 107 (12) ◽  
pp. 3385-3396 ◽  
Author(s):  
Karen Jansen ◽  
Friedl De Groote ◽  
Firas Massaad ◽  
Pieter Meyns ◽  
Jacques Duysens ◽  
...  
Keyword(s):  
Time Reversal ◽  
Neural Control ◽  
Muscle Activation ◽  
Center Of Mass ◽  
The Body ◽  
Vertical Acceleration ◽  
Horizontal Movement ◽  
Backward Walking ◽  
Body Center ◽  
Leg Kinematics

Leg kinematics during backward walking (BW) are very similar to the time-reversed kinematics during forward walking (FW). This suggests that the underlying muscle activation pattern could originate from a simple time reversal, as well. Experimental electromyography studies have confirmed that this is the case for some muscles. Furthermore, it has been hypothesized that muscles showing a time reversal should also exhibit a reversal in function [from accelerating the body center of mass (COM) to decelerating]. However, this has not yet been verified in simulation studies. In the present study, forward simulations were used to study the effects of muscles on the acceleration of COM in FW and BW. We found that a reversal in function was indeed present in the muscle control of the horizontal movement of COM (e.g., tibialis anterior and gastrocnemius). In contrast, muscles' antigravity contributions maintained their function for both directions of movement. An important outcome of the present study is therefore that similar muscles can be used to achieve opposite functional demands at the level of control of the COM when walking direction is reversed. However, some muscles showed direction-specific contributions (i.e., dorsiflexors). We concluded that the changes in muscle contributions imply that a simple time reversal would be insufficient to produce BW from FW. We therefore propose that BW utilizes extra elements, presumably supraspinal, in addition to a common spinal drive. These additions are needed for propulsion and require a partial reconfiguration of lower level common networks.

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.

A Limited Set of Muscle Synergies for Force Control During a Postural Task

2005 ◽  
Vol 93 (1) ◽  
pp. 609-613 ◽  
Author(s):  
Lena H. Ting ◽  
Jane M. Macpherson
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Muscle Activation ◽  
Balance Control ◽  
Computational Techniques ◽  
Muscle Synergies ◽  
Support Surface ◽  
Muscle Activation Patterns ◽  
Automatic Postural Response ◽  
Postural Responses

Recently developed computational techniques have been used to reduce muscle activation patterns of high complexity to a simple synergy organization and to bring new insights to the long-standing degrees of freedom problem in motor control. We used a nonnegative factorization approach to identify muscle synergies during postural responses in the cat and to examine the functional significance of such synergies for natural behaviors. We hypothesized that the simplification of neural control afforded by muscle synergies must be matched by a similar reduction in degrees of freedom at the biomechanical level. Electromyographic data were recorded from 8–15 hindlimb muscles of cats exposed to 16 directions of support surface translation. Results showed that as few as four synergies could account for >95% of the automatic postural response across all muscles and all directions. Each synergy was activated for a specific set of perturbation directions, and moreover, each was correlated with a unique vector of endpoint force under the limb. We suggest that, within the context of active balance control, postural synergies reflect a neural command signal that specifies endpoint force of a limb.

scholarly journals A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans

2015 ◽  
Vol 12 (102) ◽  
pp. 20140963 ◽  
Author(s):  
Victoria J. Butler ◽  
Robyn Branicky ◽  
Eviatar Yemini ◽  
Jana F. Liewald ◽  
Alexander Gottschalk ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Neural Control ◽  
Muscle Activation ◽  
Movement Patterns ◽  
Large Change ◽  
High Viscosity ◽  
The Body ◽  
Theoretical Treatment ◽  
Low Viscosity ◽  
Undulatory Swimming

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force–posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

scholarly journals Identification of the Plant for Upright Stance in Humans: Multiple Movement Patterns From a Single Neural Strategy

2008 ◽  
Vol 100 (6) ◽  
pp. 3394-3406 ◽  
Author(s):  
Tim Kiemel ◽  
Alexander J. Elahi ◽  
John J. Jeka
Keyword(s):  
Muscle Activation ◽  
The Body ◽  
Visual Scene ◽  
Trunk Muscles ◽  
Emg Activity ◽  
Upright Stance ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Joint Torques ◽  
Intrinsic Stiffness

We determined properties of the plant during human upright stance using a closed-loop system identification method originally applied to human postural control by another group. To identify the plant, which was operationally defined as the mapping from muscle activation (rectified EMG signals) to body segment angles, we rotated the visual scene about the axis through the subject's ankles using a sum-of-sines stimulus signal. Because EMG signals from ankle muscles and from hip and lower trunk muscles showed similar responses to the visual perturbation across frequency, we combined EMG signals from all recorded muscles into a single plant input. Body kinematics were described by the trunk and leg angles in the sagittal plane. The phase responses of both angles to visual scene angle were similar at low frequencies and approached a difference of ∼150° at higher frequencies. Therefore we considered leg and trunk angles as separate plant outputs. We modeled the plant with a two-joint (ankle and hip) model of the body, a second-order low-pass filter from EMG activity to active joint torques, and intrinsic stiffness and damping at both joints. The results indicated that the in-phase (ankle) pattern was neurally generated, whereas the out-of-phase pattern was caused by plant dynamics. Thus a single neural strategy leads to multiple kinematic patterns. Moreover, estimated intrinsic stiffness in the model was insufficient to stabilize the plant.

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