scholarly journals Gravitational and dynamic components of muscle torque underlie tonic and phasic muscle activity during goal-directed reaching

2017 ◽  
Author(s):  
Erienne Olesh ◽  
Bradley Pollard ◽  
Valeriya Gritsenko
Keyword(s):  
Motion Capture ◽  
Muscle Activity ◽  
Neural Control ◽  
Muscle Activation ◽  
Kinetic Properties ◽  
Motion Capture Data ◽  
Active Muscle ◽  
Muscle Torque ◽  
Dynamic Components ◽  
Muscle Torques

AbstractHuman reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing question in neuroscience. To address this question, muscle activity is often subdivided into static and phasic components. The former is thought to be related to posture maintenance and transitions between postures. The latter represents the remainder of muscle activity and is thought to be related to active movement production and the compensation for the kinetic properties of the limb. In the present study, we directly addressed how this subdivision of muscle activity into static and phasic components is related to the corresponding components of active muscle torques. Eight healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate gravitational and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the gravitational and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to identify static and phasic components of muscle activity using muscle torques. The contribution of both components to the overall muscle activity was largely equal, unlike their relative contribution to active muscle torques, which may reflect a neural control strategy.

scholarly journals Estimating muscle activation from EMG using deep learning-based dynamical systems models

2021 ◽  
Author(s):  
Lahiru N. Wimalasena ◽  
Jonas F. Braun ◽  
Mohammad Reza Keshtkaran ◽  
David Hofmann ◽  
Juan Álvaro Gallego ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dynamical Systems ◽  
Muscle Activity ◽  
Neural Control ◽  
Large Scale ◽  
Muscle Activation ◽  
Bayesian Filtering ◽  
Muscle Coordination ◽  
Low Pass

AbstractObjectiveTo study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded electromyographic (EMG) signals. Common approaches estimate muscle activation independently for each channel or require manual tuning of model hyperparameters to optimally preserve behaviorally-relevant features.ApproachHere, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks (RNNs) to model the spatial and temporal regularities that underlie multi-muscle activation.Main ResultsWe first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion, and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also tested the generality of the approach by applying AutoLFADS to monkey forearm muscle activity from an isometric task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force compared to low-pass or Bayesian filtering. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than other tested approaches.SignificanceUltimately, this method leverages both dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles that can be used for further studies of multi-muscle coordination and its control by upstream brain areas.

scholarly journals Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses

2015 ◽  
Vol 113 (7) ◽  
pp. 2102-2113 ◽  
Author(s):  
Katherine M. Steele ◽  
Matthew C. Tresch ◽  
Eric J. Perreault
Keyword(s):  
Matrix Factorization ◽  
Muscle Activity ◽  
Neural Control ◽  
Muscle Activation ◽  
Neuromuscular Control ◽  
Task Constraints ◽  
Factorization Algorithms ◽  
Low Dimensional ◽  
Or Task ◽  
And Task

Matrix factorization algorithms are commonly used to analyze muscle activity and provide insight into neuromuscular control. These algorithms identify low-dimensional subspaces, commonly referred to as synergies, which can describe variation in muscle activity during a task. Synergies are often interpreted as reflecting underlying neural control; however, it is unclear how these analyses are influenced by biomechanical and task constraints, which can also lead to low-dimensional patterns of muscle activation. The aim of this study was to evaluate whether commonly used algorithms and experimental methods can accurately identify synergy-based control strategies. This was accomplished by evaluating synergies from five common matrix factorization algorithms using muscle activations calculated from 1) a biomechanically constrained task using a musculoskeletal model and 2) without task constraints using random synergy activations. Algorithm performance was assessed by calculating the similarity between estimated synergies and those imposed during the simulations; similarities ranged from 0 (random chance) to 1 (perfect similarity). Although some of the algorithms could accurately estimate specified synergies without biomechanical or task constraints (similarity >0.7), with these constraints the similarity of estimated synergies decreased significantly (0.3–0.4). The ability of these algorithms to accurately identify synergies was negatively impacted by correlation of synergy activations, which are increased when substantial biomechanical or task constraints are present. Increased variability in synergy activations, which can be captured using robust experimental paradigms that include natural variability in motor activation patterns, improved identification accuracy but did not completely overcome effects of biomechanical and task constraints. These results demonstrate that a biomechanically constrained task can reduce the accuracy of estimated synergies and highlight the importance of using experimental protocols with physiological variability to improve synergy analyses.

scholarly journals Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching

2017 ◽  
Vol 11 ◽  
Author(s):  
Erienne V. Olesh ◽  
Bradley S. Pollard ◽  
Valeriya Gritsenko
Keyword(s):  
Muscle Activity ◽  
Muscle Torque ◽  
Dynamic Components

scholarly journals Muscle forces comprise a more sensitive measure of post-stroke movement deficits than joint angles

2019 ◽  
Author(s):  
Ariel B. Thomas ◽  
Erienne V. Olesh ◽  
Amelia Adcock ◽  
Valeriya Gritsenko
Keyword(s):  
Nervous System ◽  
Motion Capture ◽  
Neural Control ◽  
Motor Deficits ◽  
Muscle Forces ◽  
Joint Angles ◽  
Sensitive Measure ◽  
Post Stroke ◽  
Age Related ◽  
Muscle Torques

AbstractBackground and PurposeThe whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The effect of damage to the nervous system such as stroke on the complex multi-joint motion is difficult to quantify in a meaningful way that informs about the underlying deficit in the neural control of movement. We tested the idea that the disruption in intersegmental coordination after stroke can be quantified with higher sensitivity using metrics based on forces rather than motion. Our study aim was to objectively quantify post-stroke motor deficits using motion capture of stereotypical reaching movements. Our hypothesis is that muscle forces estimated based on active joint torques are a more sensitive measure of post-stroke motor deficits than angular kinematics.MethodsThe motion of twenty-two participants was captured when reaching to virtual targets in a center-out task. We used inverse dynamic analysis to derive muscle torques, which were the result of the neural control signals to muscles to produce the recorded multi-joint movements. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to motion production. We used the kinematic and dynamic variables derived from motion capture to assess age-related and post-stroke motor deficits.ResultsWe found that reaching with the non-dominant arm was accomplished with shoulder and elbow torques that had larger amplitudes and inter-trial variability compared to reaching with the dominant arm. These dominance effects confounded the assessment of post-stroke motor deficits using amplitude and variability metrics. We then identified the metric based on waveform comparison that was insensitive to dominance effects. We used it to show that muscle torques with gravity-related components subtracted were much more sensitive to post-stroke motor deficits compared to measures based on joint angles. Using this metric, it was possible to quantify the extent of individual deficits caused by stroke independently from age-related deficits and dominance effects.ConclusionsFunctional deficits seen in task performance have biomechanical underpinnings, seen only through force-based analysis. Our study has shown that estimating muscle forces that drive motion can quantify with high sensitivity post-stroke deficits in intersegmental coordination. A force-based assessment developed based on this method could help quantify less “observable” deficits in mildly affected stroke patients, such as those classified as asymptomatic via traditional motion-based assessments, but who may still report difficulty moving, increased fatigue, and/or inactivity. Moreover, identifying deficits in the different components of muscle forces may be a way to personalize and standardize intervention and increase the effectiveness of robotic therapy.

Correlational Methods for Analysis of Dance Movements

2011 ◽  
Vol 29 (supplement) ◽  
pp. 283-304 ◽  
Author(s):  
Timothy R. Brick ◽  
Steven M. Boker
Keyword(s):  
Motion Capture ◽  
Mirror Symmetry ◽  
Cross Correlation ◽  
The Other ◽  
Free Form ◽  
Motion Capture Data ◽  
Complex Interactions ◽  
Dance Movements ◽  
Time Lagged ◽  
Insight Into

Among the qualities that distinguish dance from other types of human behavior and interaction are the creation and breaking of synchrony and symmetry. The combination of symmetry and synchrony can provide complex interactions. For example, two dancers might make very different movements, slowing each time the other sped up: a mirror symmetry of velocity. Examining patterns of synchrony and symmetry can provide insight into both the artistic nature of the dance, and the nature of the perceptions and responses of the dancers. However, such complex symmetries are often difficult to quantify. This paper presents three methods – Generalized Local Linear Approximation, Time-lagged Autocorrelation, and Windowed Cross-correlation – for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

Perceptually motivated LSPIHT for motion capture data compression

2015 ◽  
Vol 51 ◽  
pp. 1-7 ◽  
Author(s):  
Irene Cheng ◽  
Amirhossein Firouzmanesh ◽  
Anup Basu
Keyword(s):  
Data Compression ◽  
Motion Capture ◽  
Motion Capture Data

scholarly journals A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3035
Author(s):  
Néstor J. Jarque-Bou ◽  
Joaquín L. Sancho-Bru ◽  
Margarita Vergara
Keyword(s):  
Activities Of Daily Living ◽  
Musculoskeletal System ◽  
Muscle Activity ◽  
Daily Living ◽  
Muscle Activation ◽  
Human Hand ◽  
Hand Motion ◽  
Biomechanical Models ◽  
Rehabilitation Protocols ◽  
Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

scholarly journals Is the Occurrence of the Sticking Region in Maximum Smith Machine Squats the Result of Diminishing Potentiation and Co-Contraction of the Prime Movers among Recreationally Resistance Trained Males?

2021 ◽  
Vol 18 (3) ◽  
pp. 1366
Author(s):  
Roland van den Tillaar ◽  
Eirik Lindset Kristiansen ◽  
Stian Larsen
Keyword(s):  
Muscle Activity ◽  
Muscle Activation ◽  
Gluteus Maximus ◽  
Knee Extensors ◽  
Force Output ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Prime Movers ◽  
Almost All ◽  
Height Data

This study compared the kinetics, barbell, and joint kinematics and muscle activation patterns between a one-repetition maximum (1-RM) Smith machine squat and isometric squats performed at 10 different heights from the lowest barbell height. The aim was to investigate if force output is lowest in the sticking region, indicating that this is a poor biomechanical region. Twelve resistance trained males (age: 22 ± 5 years, mass: 83.5 ± 39 kg, height: 1.81 ± 0.20 m) were tested. A repeated two-way analysis of variance showed that Force output decreased in the sticking region for the 1-RM trial, while for the isometric trials, force output was lowest between 0–15 cm from the lowest barbell height, data that support the sticking region is a poor biomechanical region. Almost all muscles showed higher activity at 1-RM compared with isometric attempts (p < 0.05). The quadriceps activity decreased, and the gluteus maximus and shank muscle activity increased with increasing height (p ≤ 0.024). Moreover, the vastus muscles decreased only for the 1-RM trial while remaining stable at the same positions in the isometric trials (p = 0.04), indicating that potentiation occurs. Our findings suggest that a co-contraction between the hip and knee extensors, together with potentiation from the vastus muscles during ascent, creates a poor biomechanical region for force output, and thereby the sticking region among recreationally resistance trained males during 1-RM Smith machine squats.

Keypose synthesis from 3D motion capture data by using evolutionary clustering

2021 ◽  
Author(s):  
Mehmet Akif Günen ◽  
Pınar Çivicioğlu Beşdok ◽  
Erkan Beşdok
Keyword(s):  
Motion Capture ◽  
Motion Capture Data ◽  
3D Motion ◽  
Evolutionary Clustering ◽  
3D Motion Capture
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