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

You might also read

Related Articles

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

Sort by
Same author

Reducing Video Verification Burden: Machine Learning Classification of Head Acceleration Events in Youth Football.

Research square·2026
Same author

Reducing Physical Restraint in the ICU: Multicenter Phase II Randomized Trial of a Novel Device Versus Traditional Wrist Restraints.

Critical care medicine·2026
Same author

Learning Beyond the Clinic: Can Point of Choice Visual Feedback Prompts Elicit Motor Behavior Changes in Persons with Multiple Sclerosis?

Research square·2026
Same author

Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy.

Digital biomarkers·2026
Same author

Exploration of wearable sensor measures associated with panic attacks differs across mental health conditions.

Frontiers in digital health·2026
Same author

Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study.

JMIR research protocols·2026

Related Experiment Video

Updated: Dec 18, 2025

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.1K

Gait event detection using a thigh-worn accelerometer.

Reed D Gurchiek1, Cole P Garabed1, Ryan S McGinnis1

  • 1M-Sense Research Group, University of Vermont, Burlington, VT, USA.

Gait & Posture
|June 15, 2020
PubMed
Summary
This summary is machine-generated.

This study quantifies errors in gait event detection using thigh-worn accelerometers. While spatiotemporal gait variables are accurately estimated, foot contact and foot off events show some bias, impacting biomechanical analysis.

Keywords:
AccelerometerEvent detectionGait analysisWearable sensor

More Related Videos

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

14.5K
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

9.4K

Related Experiment Videos

Last Updated: Dec 18, 2025

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.1K
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

14.5K
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

9.4K

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Gait Analysis

Background:

  • Remote gait analysis relies on accurate gait event detection.
  • Thigh-worn accelerometers estimate spatiotemporal gait variables but may have biased foot contact and foot off event estimates.
  • Previous studies have not quantified these gait event estimation errors.

Purpose of the Study:

  • To quantify the error in estimating foot contact and foot off events using a thigh-worn accelerometer.
  • To evaluate the accuracy of spatiotemporal gait variable estimation.
  • To assess the clinical utility for biomechanical analysis requiring precise gait phase segmentation.

Main Methods:

  • 32 healthy subjects walked at various speeds (0.56-1.78 m/s).
  • Gait events and spatiotemporal variables were estimated using a thigh-worn accelerometer.
  • Ground truth was established using vertical ground reaction forces from a pressure treadmill.

Main Results:

  • Absolute errors for foot contact and foot off were 39 ± 28 ms and 28 ± 28 ms, respectively.
  • Stride time, stance time, and swing time showed strong correlations (1.00, 0.92, 0.80) with reference data.
  • Estimation errors for foot contact and foot off were within approximately three samples at 31.25 Hz.

Conclusions:

  • The algorithm supports accurate spatiotemporal gait variable estimation.
  • Error distributions for gait events provide bounds for biomechanical analysis.
  • This method offers valuable insights for remote gait monitoring and analysis.