Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Dimensional Analysis01:23

Dimensional Analysis

808
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
808
Variability: Analysis01:11

Variability: Analysis

115
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
115
Coordination Number and Geometry02:57

Coordination Number and Geometry

15.2K
For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
15.2K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

433
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
433
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

370
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
370
Correlation of Experimental Data01:23

Correlation of Experimental Data

100
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
100

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effects of delay and amplification of auditory feedback for walking: anticipation, variability, and frequency adaptation.

bioRxiv : the preprint server for biology·2026
Same author

Manifold properties in the macaque medial premotor cortex during switching from attending to tapping to a metronome.

bioRxiv : the preprint server for biology·2025
Same author

Concurrent Supra-Postural Auditory-Hand Coordination Task Affects Postural Control: Using Sonification to Explore Environmental Unpredictability in Factors Affecting Fall Risk.

Sensors (Basel, Switzerland)·2024
Same author

Influence of surface features on the perception of nonadjacent musical phrases.

Musicae scientiae : the journal of the European Society for the Cognitive Sciences of Music·2023
Same author

Collective dynamics support group drumming, reduce variability, and stabilize tempo drift.

eLife·2022
Same author

Creating a shared musical interpretation: Changes in coordination dynamics while learning unfamiliar music together.

Annals of the New York Academy of Sciences·2022

Related Experiment Video

Updated: May 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

Analysis of High-Dimensional Coordination in Human Movement Using Variance Spectrum Scaling and Intrinsic

Dobromir Dotov1, Jingxian Gu1, Philip Hotor2

  • 1Department of Biomechanics, University of Nebraska Omaha, Omaha, NE 68182, USA.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

New methods quantify full-body movement smoothness and dimensionality. Healthy individuals exhibit higher variance scaling and more complex kinematic manifolds compared to those with gait impairment.

Keywords:
Kuramoto modelbiomechanicscollective dynamicscoordination dynamicsdimensionality reductionmovement sciencemultivariate synchronization analysis

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Related Experiment Videos

Last Updated: May 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Area of Science:

  • Movement science
  • Biomechanics
  • Dynamical systems theory

Background:

  • Full-body movement analysis traditionally uses one-dimensional data, limiting understanding of complex coordination.
  • High-density recording and data sharing enable new approaches to study biomechanical degrees of freedom and variability.
  • Existing methods often up-dimensionalize, inferring higher-dimensional properties from limited data.

Purpose of the Study:

  • To develop a method for quantifying the smoothness and dimensionality of full-body movement kinematic manifolds.
  • To investigate the relationship between manifold properties and gait impairment.
  • To explore how manifold dimensionality and smoothness inform classic movement science problems.

Main Methods:

  • Developed an approach using principal component analysis of embedding dimensions to analyze variance spectra.
  • Calculated the power law scaling exponent of the variance spectrum to quantify manifold smoothness and dimensionality.
  • Validated the method with the Kuramoto model and applied it to a full-body motion capture dataset of gait trials.

Main Results:

  • The power law scaling exponent is a function of manifold smoothness and dimensionality, defining a non-differentiability threshold.
  • Variance scaling was highest in healthy individuals, followed by post-hip replacement osteoarthritis patients, and then pre-surgery patients.
  • Healthy individuals showed increased intrinsic dimensionality and decreased fractal dimension, indicating a more compact yet complex manifold.

Conclusions:

  • Manifold dimensionality and smoothness are quantifiable metrics for full-body movement.
  • These metrics reveal differences in kinematic organization related to gait impairment.
  • The approach offers insights into classic biomechanical problems and the exploration of full-body action.