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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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 instrumental in...
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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...

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Related Experiment Video

Updated: Jun 7, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

An adaptive gyroscope-based algorithm for temporal gait analysis.

Barry R Greene1, Denise McGrath, Ross O'Neill

  • 1Intel Digital Health Group, Leixlip, Co, Kildare, Ireland. barry.r.greene@intel.com

Medical & Biological Engineering & Computing
|November 3, 2010
PubMed
Summary
This summary is machine-generated.

This study validates a gyroscope-based algorithm for gait analysis. The adaptive algorithm shows promise for accurate, low-cost ambulatory gait monitoring in healthy and pathological cases.

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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Related Experiment Videos

Last Updated: Jun 7, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Gait Analysis

Background:

  • Body-worn kinematic sensors offer a portable and cost-effective approach for ambulatory gait monitoring.
  • Automated temporal gait analysis is crucial for understanding gait dynamics and detecting abnormalities.

Purpose of the Study:

  • To evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis.
  • To assess the accuracy of wireless gyroscopes for gait monitoring in healthy and pathological conditions.

Main Methods:

  • An adaptive gyroscope-based algorithm was developed and tested.
  • Gyroscope data were collected from nine healthy adults at various speeds and from a poliomyelitis patient.
  • Data were validated against simultaneous measurements from force plates and an optical motion capture system.

Main Results:

  • The adaptive gyroscope algorithm demonstrated mean errors of -4.5 ± 14.4 ms (IC) and 43.4 ± 6.0 ms (TC) compared to force plates in healthy subjects.
  • In a poliomyelitis patient, errors were -75.61 ± 27.53 ms (IC) and 99.20 ± 46.00 ms (TC) versus force plates.
  • Good agreement was observed between the gyroscope algorithm and optical motion analysis in healthy subjects.

Conclusions:

  • The adaptive gyroscope algorithm provides a robust basis for portable, low-cost ambulatory gait monitoring.
  • This technology has potential applications in clinical settings for assessing gait in various populations.
  • Further validation may enhance the clinical utility of gyroscope-based gait analysis systems.