scholarly journals Principal components of hand kinematics and neurophysiological signals in motor cortex during reach to grasp movements

2014 ◽  
Vol 112 (8) ◽  
pp. 1857-1870 ◽  
Author(s):  
Mohsen Mollazadeh ◽  
Vikram Aggarwal ◽  
Nitish V. Thakor ◽  
Marc H. Schieber
Keyword(s):  
Motor Cortex ◽  
Joint Angle ◽  
Principal Component ◽  
Field Potentials ◽  
Joint Angles ◽  
Reach To Grasp ◽  
Lower Accuracy ◽  
Neurophysiological Data ◽  
Kinematic Synergies ◽  
Cross Correlations

A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects.

Joint trajectory generator for powered orthosis based on gait modelling using PCA and FFT

Robotica ◽  
2017 ◽  
Vol 36 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Nicholas B. Melo ◽  
Carlos E. T. Dórea ◽  
Pablo J. Alsina ◽  
Márcio V. Araújo
Keyword(s):  
Principal Component Analysis ◽  
Fourier Series ◽  
Energy Consumption ◽  
Lower Limb ◽  
Joint Angle ◽  
Principal Component ◽  
Orthotic Devices ◽  
Joint Angles ◽  
Anthropometric Features ◽  
Trajectory Generator

SUMMARYIn this work, we propose a method able to find user-oriented gait trajectories that can be used in powered lower limb orthosis applications. Most research related to active orthotic devices focuses on solving hardware issues. However, the problem of generating a set of joint trajectories that are user-oriented still persists. The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.

scholarly journals Posture similarity index: A method to compare hand postures in synergy space

2018 ◽  
Author(s):  
Nayan Bhatt ◽  
Varadhan SKM
Keyword(s):  
Experimental Data ◽  
Synthetic Data ◽  
Similarity Index ◽  
Principal Component ◽  
Synthetic Dataset ◽  
Human Hand ◽  
Joint Angles ◽  
Task Requirements ◽  
Kinematic Synergies ◽  
Real Experimental Data

Background The Human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer. Central Nervous System (CNS) uses different strategies in different manipulation tasks based on task requirements. Several attempts to compare postures of the hand have been made. Some of these have been developed for use in Robotics and animation industries. In this study, we develop an index to quantify the similarity between two human hand postures, the posture similarity index. Methods Twelve right-handed volunteers performed 70 postures and lifted and held 30 objects (total of 100 different postures, each performed 5 times). Kinematics of individual finger phalanges (segments) were captured by using a 16-sensor electromagnetic tracking sensor system. The hand was modelled as a 21-DoF system and the corresponding joint angles were computed. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We developed a posture similarity index (PSI), that represents similarity between posture in the synergy (Principal component) space. First, performance of this index was tested using a synthetic dataset. After confirming that it performs well with synthetic dataset, we used it to analyse experimental data. Further, we used PSI to identify postures that are representative in the sense that they have a greater overlap (in synergy space) with a large number of postures. Results Using synthetic data and real experimental data, it was found that PSI was a relatively accurate index of similarity in synergy space. Also, it was found that more special postures than common postures were found among “representative” postures. Conclusion An index for comparing posture similarity in synergy space has been developed and its use has been demonstrated using synthetic dataset and experimental dataset. In addition, we found that special postures are actually special in the sense that there are more of them in the “representative” postures as identified by our posture similarity index.

scholarly journals Posture similarity index: A method to compare hand postures in synergy space

2018 ◽  
Author(s):  
Nayan Bhatt ◽  
Varadhan SKM
Keyword(s):  
Experimental Data ◽  
Synthetic Data ◽  
Similarity Index ◽  
Principal Component ◽  
Synthetic Dataset ◽  
Human Hand ◽  
Joint Angles ◽  
Task Requirements ◽  
Kinematic Synergies ◽  
Real Experimental Data

Background The Human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer. Central Nervous System (CNS) uses different strategies in different manipulation tasks based on task requirements. Several attempts to compare postures of the hand have been made. Some of these have been developed for use in Robotics and animation industries. In this study, we develop an index to quantify the similarity between two human hand postures, the posture similarity index. Methods Twelve right-handed volunteers performed 70 postures and lifted and held 30 objects (total of 100 different postures, each performed 5 times). Kinematics of individual finger phalanges (segments) were captured by using a 16-sensor electromagnetic tracking sensor system. The hand was modelled as a 21-DoF system and the corresponding joint angles were computed. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We developed a posture similarity index (PSI), that represents similarity between posture in the synergy (Principal component) space. First, performance of this index was tested using a synthetic dataset. After confirming that it performs well with synthetic dataset, we used it to analyse experimental data. Further, we used PSI to identify postures that are representative in the sense that they have a greater overlap (in synergy space) with a large number of postures. Results Using synthetic data and real experimental data, it was found that PSI was a relatively accurate index of similarity in synergy space. Also, it was found that more special postures than common postures were found among “representative” postures. Conclusion An index for comparing posture similarity in synergy space has been developed and its use has been demonstrated using synthetic dataset and experimental dataset. In addition, we found that special postures are actually special in the sense that there are more of them in the “representative” postures as identified by our posture similarity index.

Two Kinematic Synergies in Voluntary Whole-Body Movements During Standing

2006 ◽  
Vol 95 (2) ◽  
pp. 636-645 ◽  
Author(s):  
Sandra M.S.F. Freitas ◽  
Marcos Duarte ◽  
Mark L. Latash
Keyword(s):  
Joint Angle ◽  
Center Of Pressure ◽  
Uncontrolled Manifold ◽  
Whole Body ◽  
Joint Angles ◽  
Body Movements ◽  
Uncontrolled Manifold Hypothesis ◽  
Kinematic Synergies ◽  
Trunk Orientation ◽  
Speed Accuracy

We used a particular computational approach, the uncontrolled manifold hypothesis, to investigate joint angle covariation patterns during whole-body actions performed by standing persons. We hypothesized that two kinematic synergies accounted for the leg/trunk joint covariation across cycles during a rhythmic whole-body motion to stabilize two performance variables, the trunk orientation in the external space and the horizontal position of the center of mass (COM). Subjects stood on a force plate and performed whole-body rhythmic movements for 45 s under visual feedback on one of the four variables, the position of the center of pressure or the angle in one of the three joints (ankle, knee, or hip). The Fitts-like paradigm was used with two target amplitudes and six indices of difficulty (ID) for each of the four variables. This was done to explore the robustness of kinematic postural synergies. A speed-accuracy trade-off was observed in all feedback conditions such that the movement time scaled with ID and the scaling differed between the two movement amplitudes. Principal-component (PC) analysis showed the existence of a single PC in the joint space that accounted for over 95% of the joint angle variance. Analysis within the uncontrolled manifold hypothesis has shown that data distributions in the joint angle space were compatible with stabilization of both trunk orientation and COM location. We conclude that trunk orientation and the COM location are stabilized by co-varied changes of the major joint angles during whole-body movements. Despite the strong effects of movement amplitude and ID on performance, the structure of the joint variance showed only minor dependence on these task parameters. The two kinematic synergies (co-varied changes in the joint angles that stabilized the COM location and trunk orientation) have proven to be robust over a variety of tasks.

scholarly journals Genome-Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 370 ◽  
Author(s):  
Annik Imogen Gmel ◽  
Thomas Druml ◽  
Rudolf von Niederhäusern ◽  
Tosso Leeb ◽  
Markus Neuditschko
Keyword(s):  
Joint Angle ◽  
Association Studies ◽  
Genome Wide Association ◽  
Cranial Morphology ◽  
Genome Wide Association Studies ◽  
Joint Angles ◽  
Mineral Density ◽  
Mixed Effect ◽  
Stifle Joint ◽  
Genome Wide

The evaluation of conformation traits is an important part of selection for breeding stallions and mares. Some of these judged conformation traits involve joint angles that are associated with performance, health, and longevity. To improve our understanding of the genetic background of joint angles in horses, we have objectively measured the angles of the poll, elbow, carpal, fetlock (front and hind), hip, stifle, and hock joints based on one photograph of each of the 300 Franches-Montagnes (FM) and 224 Lipizzan (LIP) horses. After quality control, genome-wide association studies (GWASs) for these traits were performed on 495 horses, using 374,070 genome-wide single nucleotide polymorphisms (SNPs) in a mixed-effect model. We identified two significant quantitative trait loci (QTL) for the poll angle on ECA28 (p = 1.36 × 10−7), 50 kb downstream of the ALX1 gene, involved in cranial morphology, and for the elbow joint on ECA29 (p = 1.69 × 10−7), 49 kb downstream of the RSU1 gene, and 75 kb upstream of the PTER gene. Both genes are associated with bone mineral density in humans. Furthermore, we identified other suggestive QTL associated with the stifle joint on ECA8 (p = 3.10 × 10−7); the poll on ECA1 (p = 6.83 × 10−7); the fetlock joint of the hind limb on ECA27 (p = 5.42 × 10−7); and the carpal joint angle on ECA3 (p = 6.24 × 10−7), ECA4 (p = 6.07 × 10−7), and ECA7 (p = 8.83 × 10−7). The application of angular measurements in genetic studies may increase our understanding of the underlying genetic effects of important traits in equine breeding.

scholarly journals Experimental and Numerical Study of Fracture Behavior of Rock-Like Material Specimens with Single Pre-Set Joint under Dynamic Loading

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2690
Author(s):  
Bo Pan ◽  
Xuguang Wang ◽  
Zhenyang Xu ◽  
Lianjun Guo ◽  
Xuesong Wang
Keyword(s):  
Crack Initiation ◽  
Crack Propagation ◽  
Joint Angle ◽  
Simulation Software ◽  
Dissipated Energy ◽  
Energy Structure ◽  
Joint Angles ◽  
Crack Penetration ◽  
Before And After ◽  
The Impact

The Split Hopkinson Pressure Bar (SHPB) is an apparatus for testing the dynamic stress-strain response of the cement mortar specimen with pre-set joints at different angles to explore the influence of joint attitudes of underground rock engineering on the failure characteristics of rock mass structure. The nuclear magnetic resonance (NMR) has also been used to measure the pore distribution and internal cracks of the specimen before and after the testing. In combination with numerical analysis, the paper systematically discusses the influence of joint angles on the failure mode of rock-like materials from three aspects of energy dissipation, microscopic damage, and stress field characteristics. The result indicates that the impact energy structure of the SHPB is greatly affected by the pre-set joint angle of the specimen. With the joint angle increasing, the proportion of reflected energy moves in fluctuation, while the ratio of transmitted energy to dissipated energy varies from one to the other. NMR analysis reveals the structural variation of the pores in those cement specimens before and after the impact. Crack propagation direction is correlated with pre-set joint angles of the specimens. With the increase of the pre-set joint angles, the crack initiation angle decreases gradually. When the joint angles are around 30°–75°, the specimens develop obvious cracks. The crushing process of the specimens is simulated by LS-DYNA software. It is concluded that the stresses at the crack initiation time are concentrated between 20 and 40 MPa. The instantaneous stress curve first increases and then decreases with crack propagation, peaking at different times under various joint angles; but most of them occur when the crack penetration ratio reaches 80–90%. With the increment of joint angles in specimens through the simulation software, the changing trend of peak stress is consistent with the test results.

Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex

2004 ◽  
Vol 98 (4-6) ◽  
pp. 498-506 ◽  
Author(s):  
Carsten Mehring ◽  
Martin Paul Nawrot ◽  
Simone Cardoso de Oliveira ◽  
Eilon Vaadia ◽  
Andreas Schulze-Bonhage ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Arm Movement ◽  
Movement Direction ◽  
Single Channels ◽  
Field Potentials ◽  
Human Motor Cortex ◽  
Human Motor

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 Posture similarity index: a method to compare hand postures in synergy space

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6078 ◽  
Author(s):  
Nayan Bhatt ◽  
Varadhan SKM
Keyword(s):  
Experimental Data ◽  
Tracking System ◽  
Synthetic Data ◽  
Similarity Index ◽  
Principal Component ◽  
Synthetic Dataset ◽  
Human Hand ◽  
Task Requirements ◽  
Kinematic Synergies ◽  
The Central Nervous System

Background The human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer. The central nervous system (CNS) uses different strategies in different manipulation tasks based on task requirements. Attempts to compare postures of the hand have been made for use in robotics and animation industries. In this study, we developed an index called the posture similarity index to quantify the similarity between two human hand postures. Methods Twelve right-handed volunteers performed 70 postures, and lifted and held 30 objects (total of 100 different postures, each performed five times). A 16-sensor electromagnetic tracking system captured the kinematics of individual finger phalanges (segments). We modeled the hand as a 21-DoF system and computed the corresponding joint angles. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We developed a posture similarity index (PSI), that represents the similarity between posture in the synergy (Principal component) space. First, we tested the performance of this index using a synthetic dataset. After confirming that it performs well with the synthetic dataset, we used it to analyze the experimental data. Further, we used PSI to identify postures that are “representative” in the sense that they have a greater overlap (in synergy space) with a large number of postures. Results Our results confirmed that PSI is a relatively accurate index of similarity in synergy space both with synthetic data and real experimental data. Also, more special postures than common postures were found among “representative” postures. Conclusion We developed an index for comparing posture similarity in synergy space and demonstrated its utility by using synthetic dataset and experimental dataset. Besides, we found that “special” postures are actually “special” in the sense that there are more of them in the “representative” postures as identified by our posture similarity index.

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