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

Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

806
The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the...
806
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

856
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
856
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

851
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
851
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

716
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
716
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

897
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...
897
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

707
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...
707

You might also read

Related Articles

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

Sort by
Same author

A Deep Learning Approach to Automatically Classify Ice Hockey Shooting Actions Using Acceleration Signals.

Sensors (Basel, Switzerland)·2026
Same author

The association between pain sensitivity and hip and lumbar joint excursion during a lifting task in people with chronic low back pain.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2026
Same author

Fear of movement, obesity and physical activity in knee osteoarthritis.

Clinical biomechanics (Bristol, Avon)·2026
Same author

Machine learning-based classification of ice hockey skating tasks using kinematic data.

Sports biomechanics·2025
Same author

Breaking the ice: exploring sex-based variations in the mechanics of ice hockey slap shots.

Sports biomechanics·2025
Same author

Non-Linear Gait Dynamics Are Affected by Commonly Occurring Outdoor Surfaces and Sex in Healthy Adults.

Sensors (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jan 25, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K

The Effects of Data Padding Techniques on Continuous Relative-Phase Analysis Using the Hilbert Transform.

Patrick Ippersiel1,2, Richard Preuss1,2, Shawn M Robbins1,2

  • 11 McGill University.

Journal of Applied Biomechanics
|April 30, 2019
PubMed
Summary
This summary is machine-generated.

Padding techniques do not impact continuous relative phase (CRP) analysis for sinusoidal data. For kinematic data, spline extrapolation minimizes errors when extraneous data are unavailable.

Keywords:
end effectsjoint coordinationsignal processingsit-to-stand-to-sit

More Related Videos

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K
Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
09:42

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Published on: November 8, 2013

13.9K

Related Experiment Videos

Last Updated: Jan 25, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K
Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
09:42

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Published on: November 8, 2013

13.9K

Area of Science:

  • Biomechanics
  • Signal Processing

Background:

  • Continuous relative phase (CRP) analysis is crucial for biomechanical studies.
  • The Hilbert transform, commonly used for CRP, is susceptible to end effects.
  • Data padding techniques are often employed to mitigate these end effects.

Purpose of the Study:

  • To evaluate the impact of different padding techniques on end effects in Hilbert-transformed CRP calculations.
  • To compare the efficacy of unpadded, reflection, spline extrapolation, and extraneous data padding methods.
  • To analyze these effects on sinusoidal, nonsinusoidal, and real-world kinematic data.

Main Methods:

  • Calculated CRP angles using the Hilbert transform on sinusoidal, nonsinusoidal, and kinematic (sit-to-stand-to-sit task) data.
  • Compared various padding techniques (unpadded, reflection, spline extrapolation, extraneous data).
  • Quantified errors using root mean square difference and true error against a gold standard.

Main Results:

  • Unpadded methods showed negligible error for sinusoidal data across all periods.
  • No single padding method was consistently superior for nonsinusoidal data.
  • Spline extrapolation significantly reduced root mean square difference for kinematic data compared to reflection and unpadded methods.

Conclusions:

  • Padding is unnecessary for sinusoidal data in CRP analysis.
  • For kinematic data, spline extrapolation is recommended when extraneous data are not available to minimize distortion.
  • Findings guide optimal data processing for accurate biomechanical analysis.