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.

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.

scholarly journals Candidates for Synergies: Linear Discriminants versus Principal Components

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ramana Vinjamuri ◽  
Vrajeshri Patel ◽  
Michael Powell ◽  
Zhi-Hong Mao ◽  
Nathan Crone
Keyword(s):  
American Sign Language ◽  
Principal Component ◽  
Hand Movements ◽  
Human Hand ◽  
Linear Discriminant ◽  
Advantages And Disadvantages ◽  
Reduced Dimensions ◽  
Kinematic Synergies ◽  
Hand Synergies ◽  
Linear Discriminants

Movement primitives or synergies have been extracted from human hand movements using several matrix factorization, dimensionality reduction, and classification methods. Principal component analysis (PCA) is widely used to obtain the first few significant eigenvectors of covariance that explain most of the variance of the data. Linear discriminant analysis (LDA) is also used as a supervised learning method to classify the hand postures corresponding to the objects grasped. Synergies obtained using PCA are principal component vectors aligned with dominant variances. On the other hand, synergies obtained using LDA are linear discriminant vectors that separate the groups of variances. In this paper, time varying kinematic synergies in the human hand grasping movements were extracted using these two diametrically opposite methods and were evaluated in reconstructing natural and American sign language (ASL) postural movements. We used an unsupervised LDA (ULDA) to extract linear discriminants. The results suggest that PCA outperformed LDA. The uniqueness, advantages, and disadvantages of each of these methods in representing high-dimensional hand movements in reduced dimensions were discussed.

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.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals Low-temperature thermal hydrolysis of sludge prior to anaerobic digestion: principal component analysis (PCA) of experimental data

Data in Brief ◽  
2021 ◽  
pp. 107323
Author(s):  
Mohamed N.A. Meshref ◽  
Seyed Mohammad Mirsoleimani Azizi ◽  
Wafa Dastyar ◽  
Rasha Maal-Bared ◽  
Bipro Ranjan Dhar
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Anaerobic Digestion ◽  
Low Temperature ◽  
Principal Component ◽  
Component Analysis ◽  
Thermal Hydrolysis ◽  
Hydrolysis Of

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 Continuum Power CCA: A Unified Approach for Isolating Coupled Modes

2015 ◽  
Vol 28 (3) ◽  
pp. 1016-1030 ◽  
Author(s):  
Erik Swenson
Keyword(s):  
Signal To Noise Ratio ◽  
Full Range ◽  
Synthetic Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Accurate Estimate ◽  
Unified Approach ◽  
Coupled Modes ◽  
Multivariate Statistical ◽  
Sample Covariance

Abstract Various multivariate statistical methods exist for analyzing covariance and isolating linear relationships between datasets. The most popular linear methods are based on singular value decomposition (SVD) and include canonical correlation analysis (CCA), maximum covariance analysis (MCA), and redundancy analysis (RDA). In this study, continuum power CCA (CPCCA) is introduced as one extension of continuum power regression for isolating pairs of coupled patterns whose temporal variation maximizes the squared covariance between partially whitened variables. Similar to the whitening transformation, the partial whitening transformation acts to decorrelate individual variables but only to a partial degree with the added benefit of preconditioning sample covariance matrices prior to inversion, providing a more accurate estimate of the population covariance. CPCCA is a unified approach in the sense that the full range of solutions bridges CCA, MCA, RDA, and principal component regression (PCR). Recommended CPCCA solutions include a regularization for CCA, a variance bias correction for MCA, and a regularization for RDA. Applied to synthetic data samples, such solutions yield relatively higher skill in isolating known coupled modes embedded in noise. Provided with some crude prior expectation of the signal-to-noise ratio, the use of asymmetric CPCCA solutions may be justifiable and beneficial. An objective parameter choice is offered for regularization with CPCCA based on the covariance estimate of O. Ledoit and M. Wolf, and the results are quite robust. CPCCA is encouraged for a range of applications.

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