scholarly journals Low-Dimensional Motor Control Representations in Throwing Motions

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Ana Lucia Cruz Ruiz ◽  
Charles Pontonnier ◽  
Georges Dumont
Keyword(s):  
Degrees Of Freedom ◽  
Muscle Activation ◽  
Extraction Methods ◽  
Muscle Synergies ◽  
Control Mechanisms ◽  
Dimensional Representation ◽  
Physical Conditions ◽  
Activation Level ◽  
Task Features ◽  
Low Dimensional

In this study, we identified a low-dimensional representation of control mechanisms in throwing motions from a variety of subjects and target distances. The control representation was identified at the kinematic level in task and joint spaces, respectively, and at the muscle activation level using the theory of muscle synergies. Representative features of throwing motions in all of these spaces were chosen to be investigated. Features were extracted using factorization and clustering techniques from the muscle data of unexperienced subjects (with different morphologies and physical conditions) during a series of throwing tasks. Two synergy extraction methods were tested to assess their consistency. For the task features, the degrees of freedom (DoF), and the muscles under study, the results can be summarized as (1) a control representation across subjects consisting of only two synergies at the activation level and of representative features in the task and joint spaces, (2) a reduction of control redundancy (since the number of synergies are less than the number of actions to be controlled), (3) links between the synergies triggering intensity and the throwing distance, and finally (4) consistency of the extraction methods. Such results are useful to better represent mechanisms hidden behind such dynamical motions and could offer a promising control representation for synthesizing motions with muscle-driven characters.

scholarly journals Lower complexity of motor primitives ensures robust control of high-speed human locomotion

2020 ◽  
Author(s):  
Alessandro Santuz ◽  
Antonis Ekizos ◽  
Yoko Kunimasa ◽  
Kota Kijima ◽  
Masaki Ishikawa ◽  
...  
Keyword(s):  
High Speed ◽  
Muscle Activation ◽  
Human Locomotion ◽  
Muscle Synergies ◽  
Activation Patterns ◽  
Motor Primitives ◽  
Muscle Activation Patterns ◽  
Relative Contribution ◽  
Muscle Activations ◽  
Low Dimensional

AbstractWalking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.

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.

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.

Muscle Synergies Based on a Biomechanical Biaxial Isometric Shoulder Model Minimizing Fatigue

2010 ◽  
Author(s):  
Mohamadreza Nassajian Moghadam ◽  
Kamiar Aminian ◽  
Mohsen Asghari ◽  
Mohammad Parnianpour
Keyword(s):  
Shoulder Joint ◽  
Degrees Of Freedom ◽  
Muscle Activation ◽  
Glenohumeral Joint ◽  
Nonnegative Matrix ◽  
Factorization Method ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Specific Direction ◽  
Activation Data

In this study we utilize the concept of synergy formation as a simplifying control strategy to manage the high number of degrees of freedom presented in the maintenance of the posture of the shoulder joint. We address how to find the muscle synergy recruitment map to the biomechanical demands (biaxial external torque) during an isometric shoulder task. We use a numerical optimization based shoulder model to obtain muscle activation levels when a biaxial external isometric torque is exposed at the shoulder glenohumeral joint. In the numerical simulations, different shoulder torque vectors parallel to the horizontal plane are considered. For each selected direction for the torque, the resulting muscle activation data are calculated and then used for grouping muscles in some fixed element synergies by nonnegative matrix factorization method Next, the muscle synergies are converted from activation level to the torque space to see how muscle synergy recruitment addresses the torque production in a specific direction at the shoulder joint. The results confirmed our expectation that the few dominant synergies are sufficient to address the torque vectors in directions which coincide to the basic vectors of torque space, such that each muscle contributed to more than one synergy.

scholarly journals A chiral switchable photovoltaic ferroelectric 1D perovskite

2020 ◽  
Vol 6 (9) ◽  
pp. eaay4213 ◽  
Author(s):  
Yang Hu ◽  
Fred Florio ◽  
Zhizhong Chen ◽  
W. Adam Phelan ◽  
Maxime A. Siegler ◽  
...  
Keyword(s):  
Electric Fields ◽  
Mirror Symmetry ◽  
Degrees Of Freedom ◽  
Electrical Measurements ◽  
Temperature Dependent ◽  
Paraelectric Phase Transition ◽  
Strongly Coupled ◽  
Yield Phenomena ◽  
Hybrid Perovskite ◽  
Low Dimensional

Spin and valley degrees of freedom in materials without inversion symmetry promise previously unknown device functionalities, such as spin-valleytronics. Control of material symmetry with electric fields (ferroelectricity), while breaking additional symmetries, including mirror symmetry, could yield phenomena where chirality, spin, valley, and crystal potential are strongly coupled. Here we report the synthesis of a halide perovskite semiconductor that is simultaneously photoferroelectricity switchable and chiral. Spectroscopic and structural analysis, and first-principles calculations, determine the material to be a previously unknown low-dimensional hybrid perovskite (R)-(−)-1-cyclohexylethylammonium/(S)-(+)-1 cyclohexylethylammonium) PbI3. Optical and electrical measurements characterize its semiconducting, ferroelectric, switchable pyroelectricity and switchable photoferroelectric properties. Temperature dependent structural, dielectric and transport measurements reveal a ferroelectric-paraelectric phase transition. Circular dichroism spectroscopy confirms its chirality. The development of a material with such a combination of these properties will facilitate the exploration of phenomena such as electric field and chiral enantiomer–dependent Rashba-Dresselhaus splitting and circular photogalvanic effects.

scholarly journals Optimizing the structure and movement of a robotic bat with biological kinematic synergies

2018 ◽  
Vol 37 (10) ◽  
pp. 1233-1252 ◽  
Author(s):  
Jonathan Hoff ◽  
Alireza Ramezani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Principal Component Analysis ◽  
Topological Structure ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Biologically Inspired ◽  
Wing Kinematics ◽  
Wing Motion ◽  
Kinematic Synergies ◽  
Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

scholarly journals Integrating Multiple Interaction Networks for Gene Function Inference

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 30 ◽  
Author(s):  
Jingpu Zhang ◽  
Lei Deng
Keyword(s):  
State Of The Art ◽  
Interaction Networks ◽  
Dimensional Representation ◽  
Functional Interaction ◽  
High Throughput Data ◽  
Multiple Networks ◽  
The Past ◽  
Proteomic Data ◽  
Multiple Interaction ◽  
Low Dimensional

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

scholarly journals A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

2021 ◽  
Vol 6 (22) ◽  
pp. 51-59
Author(s):  
Mustazzihim Suhaidi ◽  
Rabiah Abdul Kadir ◽  
Sabrina Tiun
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Input Data ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Machine Learning Algorithm ◽  
Learning Tasks ◽  
Low Dimensional

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

scholarly journals Neurobiological successor features for spatial navigation

2019 ◽  
Author(s):  
William de Cothi ◽  
Caswell Barry
Keyword(s):  
Grid Cell ◽  
Biological Data ◽  
Basis Set ◽  
Grid Cells ◽  
Dimensional Representation ◽  
Boundary Vector ◽  
Good Account ◽  
Cell Firing ◽  
Low Dimensional ◽  
Environmental Geometry

AbstractThe hippocampus has long been observed to encode a representation of an animal’s position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modelled by learning a successor representation (SR) between distinct positions in an environment. However, this discretisation of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features – boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low-dimensional representation of these successor features. We show that the place and grid cells generated using the BVC-SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.

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