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

Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

1.1K
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...
1.1K
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

1.0K
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...
1.0K
Measuring Acceleration Due to Gravity01:12

Measuring Acceleration Due to Gravity

1.4K
Consider a coffee mug hanging on a hook in a pantry. If the mug gets knocked, it oscillates back and forth like a pendulum until the oscillations die out.
A simple pendulum can be described as a point mass and a string. Meanwhile, a physical pendulum is any object whose oscillations are similar to a simple pendulum, but cannot be modeled as a point mass on a string because its mass is distributed over a larger area. The behavior of a physical pendulum can be modeled using the principles of...
1.4K

You might also read

Related Articles

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

Sort by
Same journalSame Topic

A novel method for EKG anomaly detection based on the double sliding window technique.

Computer methods in biomechanics and biomedical engineering·2026
Same journal

EEG emotion recognition using a channel-aware feature encoder and XGBoost classifier.

Computer methods in biomechanics and biomedical engineering·2026
Same journal

Prism-refraction K-means polar lights optimizer-driven feature selection for accurate diabetes diagnosis.

Computer methods in biomechanics and biomedical engineering·2026
Same journal

Parametric design of insole for different foot arch types by finite element method.

Computer methods in biomechanics and biomedical engineering·2026
Same journal

Finite element analysis of the biomechanics of fusion-fixed segments based on combined placement of new screws applied under TLIF.

Computer methods in biomechanics and biomedical engineering·2026
Same journal

Biomechanical modeling of elastoplastic behavior of spinal rod in spinal instrumentation.

Computer methods in biomechanics and biomedical engineering·2026

Related Experiment Video

Updated: Apr 14, 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

14.5K

Estimation of temporal gait parameters using Bayesian models on acceleration signals.

I H López-Nava1, A Muñoz-Meléndez1, A I Pérez Sanpablo2

  • 1a Computer Science Department , Instituto Nacional de Astrofísica, Óptica y Electrónica , Puebla , Mexico.

Computer Methods in Biomechanics and Biomedical Engineering
|April 16, 2015
PubMed
Summary

This study developed a system using wireless accelerometers and AI to calculate temporal gait parameters. The system achieved a 4.6% mean error, showing promise for diverse age groups.

Keywords:
Bayesian modelsacceleration signalsaccelerometer sensorgait analysisgait parameters

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.3K
Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
06:35

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

9.7K

Related Experiment Videos

Last Updated: Apr 14, 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

14.5K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.3K
Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
06:35

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

9.7K

Area of Science:

  • Biomechanics
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Human gait analysis is crucial for understanding locomotion and diagnosing disorders.
  • Accurate measurement of temporal gait parameters is essential for effective analysis.
  • Existing methods can be costly or cumbersome, necessitating simpler solutions.

Purpose of the Study:

  • To develop a cost-effective system for calculating temporal gait parameters using wireless accelerometers and AI.
  • To validate the system's accuracy across different age groups.
  • To contribute to advancements in human gait analysis research.

Main Methods:

  • Utilized two low-cost wireless accelerometers placed on subjects' ankles.
  • Processed raw acceleration signals to identify gait patterns and characteristic peaks.
  • Implemented a Bayesian model for classifying gait events (steps/nonsteps) and segmenting signals.
  • Calculated temporal gait parameters including cadence, step time, and phase durations.

Main Results:

  • Successfully estimated temporal gait parameters with a mean error of 4.6%.
  • Demonstrated the effectiveness of the Bayesian model in classifying gait data from subjects of different ages.
  • Validated the system's capability in segmenting acceleration signals based on gait events like heel strike and toe-off.

Conclusions:

  • The developed system provides accurate temporal gait parameter calculation using accessible technology.
  • Bayesian models are effective for classifying gait data across diverse age demographics.
  • This approach offers a promising, low-cost solution for human gait analysis.