scholarly journals Golf Learning: 3D Kinematics Approach to Long Drive Swing Shot

2021 ◽  
Vol 9 (6) ◽  
pp. 1390-1395
Author(s):  
Agus Rusdiana ◽  
Hadi Sartono ◽  
Angga M Syahid ◽  
Tian Kurniawan
Keyword(s):  
3D Kinematics

STRAIN ACCOMMODATION AND 3D KINEMATICS OF TRANSPRESSIONAL FLOW WITHIN THE LOWER CRUST OF A CRETACEOUS MAGMATIC ARC IN FIORDLAND NEW ZEALAND

2018 ◽  
Author(s):  
Griffin A. Moyer ◽  
◽  
Jesse Lee ◽  
Christopher Eddy ◽  
Elena A. Miranda ◽  
...  
Keyword(s):  
New Zealand ◽  
Lower Crust ◽  
3D Kinematics ◽  
Magmatic Arc

scholarly journals 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

2021 ◽  
Vol 11 (2) ◽  
pp. 834
Author(s):  
Marwa Mezghani ◽  
Nicola Hagemeister ◽  
Youssef Ouakrim ◽  
Alix Cagnin ◽  
Alexandre Fuentes ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Knee Osteoarthritis ◽  
Decision Trees ◽  
Exercise Program ◽  
Patient Specific ◽  
Pain Level ◽  
Knee Kinematic ◽  
Kinematic Data ◽  
3D Kinematics ◽  
The Impact

Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients’ response to a treatment consisting of a physical exercise program.

scholarly journals The Gaia-ESO Survey: membership probabilities for stars in 32 open clusters from 3D kinematics

2020 ◽  
Vol 496 (4) ◽  
pp. 4701-4716 ◽  
Author(s):  
R J Jackson ◽  
R D Jeffries ◽  
N J Wright ◽  
S Randich ◽  
G Sacco ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Proper Motion ◽  
Milky Way ◽  
Open Clusters ◽  
Evolutionary Models ◽  
Valuable Resource ◽  
3D Kinematics ◽  
Cluster Membership ◽  
Data Release ◽  
Average Probability

ABSTRACT The Gaia-ESO Survey (GES) observed many open clusters as part of its programme to spectroscopically characterize the various Milky Way populations. GES spectroscopy and Gaia astrometry from its second data release are used here to assign membership probabilities to targets towards 32 open clusters with ages from 1 to 3800 Myr, based on maximum likelihood modelling of the 3D kinematics of the cluster and field populations. From a parent catalogue of 14 398 individual targets, 5032 stars with uniformly determined 3D velocities, Teff, log g, and chemistry are assigned cluster membership with probability >0.9, and with an average probability of 0.991. The robustness of the membership probabilities is demonstrated using independent membership criteria (lithium and parallax) in two of the youngest clusters. The addition of radial velocities improves membership discrimination over proper motion selection alone, especially in more distant clusters. The kinematically selected nature of the membership lists, independent of photometry and chemistry, makes the catalogue a valuable resource for testing stellar evolutionary models and investigating the time evolution of various parameters.

scholarly journals 3D kinematics of a thick salt layer during gravity-driven deformation

2019 ◽  
Vol 110 ◽  
pp. 434-449
Author(s):  
Chris Kirkham ◽  
Joe Cartwright ◽  
Claudia Bertoni ◽  
Karyna Rodriguez ◽  
Neil Hodgson
Keyword(s):  
3D Kinematics ◽  
Salt Layer ◽  
Gravity Driven

In vitro 3D-kinematics of the upper cervical spine: helical axis and simulation for axial rotation and flexion extension

2009 ◽  
Vol 32 (2) ◽  
pp. 141-151 ◽  
Author(s):  
Pierre-Michel Dugailly ◽  
Stéphane Sobczak ◽  
Victor Sholukha ◽  
Serge Van Sint Jan ◽  
Patrick Salvia ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Axial Rotation ◽  
Helical Axis ◽  
Upper Cervical Spine ◽  
3D Kinematics ◽  
Flexion Extension ◽  
Upper Cervical

scholarly journals The challenge raised by Gaia

2009 ◽  
Vol 5 (H15) ◽  
pp. 174-175
Author(s):  
Annie C. Robin
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Milky Way ◽  
Dynamical Evolution ◽  
High Quality ◽  
3D Kinematics ◽  
Galactic Potential ◽  
The Galaxy ◽  
High Level ◽  
Data Analysis Methods

AbstractGaia will perform an unprecedented high quality survey of the Milky Way. Distances, 3D kinematics, ages and abundances will be obtained, giving access to the overall mass distribution and to the Galactic potential. Gaia data analysis will involve a high level of complexity requiring new and efficient multivariate data analysis methods, improved modelling of the stellar populations and dynamical approaches to the interpretation of the data in terms of the chemical and dynamical evolution of the Galaxy.

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