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

An Exploratory Analysis of Postural Control in People with Type 2 Diabetes Mellitus Using a Smartphone IMU Sensor.

Sensors (Basel, Switzerland)·2026
Same author

The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices.

Sensors (Basel, Switzerland)·2026
Same author

Gait asymmetry in Parkinson's disease - a systematic review and meta-analysis (AsymmGait-Parkinson study).

Scientific reports·2026
Same author

Cognitive load alters cortical dynamics during gait in Parkinson's disease but not in neurologically healthy individuals.

Cognitive neurodynamics·2026
Same author

Age-related differences in force steadiness and motor unit behavior during dynamic ankle dorsiflexions.

Journal of neurophysiology·2026
Same author

Beyond Quiet Stance: The Role of Levodopa in Prolonged Standing in Parkinson's Disease.

Motor control·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
09:37

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

9.1K

Accuracy and Inter-Subject Variability of Gait Event Detection Methods Based on Optical and Inertial Motion Capture.

Vinicius Cavassano Zampier1, Morten Bilde Simonsen2, Fabio Augusto Barbieri1

  • 1Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, School of Sciences, São Paulo State University (UNESP), Bauru 17033-360, Brazil.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

Inertial measurement units (IMUs) show lower accuracy and higher variability for detecting gait events compared to optical motion capture. Researchers should consider these limitations when using IMUs for biological signal segmentation.

Keywords:
IMUgait analysisremote monitoringvalidationwalking

More Related Videos

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

14.2K
Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents
04:37

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

Published on: July 6, 2022

2.8K

Related Experiment Videos

Last Updated: Jan 7, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
09:37

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

9.1K
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

14.2K
Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents
04:37

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

Published on: July 6, 2022

2.8K

Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Wearable Sensor Technology

Background:

  • Gait events, including heel strikes and toe-offs, are crucial for analyzing spatiotemporal gait parameters and segmenting biological signals like electromyography (EMG) and electroencephalography (EEG).
  • While force platforms and optical motion capture (OMC) are accurate, inertial measurement units (IMUs) offer greater applicability for gait event detection.

Purpose of the Study:

  • To compare the accuracy and variability of IMU-based gait event detection algorithms against gold-standard ground reaction force (GRF) detection.
  • To evaluate the performance of two OMC-based and two IMU-based algorithms for identifying gait events.

Main Methods:

  • Seventeen healthy adults walked across a 10m walkway equipped with force plates.
  • Foot kinematics were captured using retro-reflective markers and a sacrum-mounted IMU.
  • Gait events were identified using two OMC algorithms (OMC1, OMC2) and two IMU algorithms (IMU1, IMU2); accuracy was assessed via root-mean-square error (RMSE) and variability via coefficient of variation (CoV) relative to GRF.

Main Results:

  • OMC1 demonstrated significantly lower RMSE for heel strikes (14 ms) and toe-offs (17 ms) compared to IMU algorithms (IMU1: 50 ms, 54 ms; IMU2: 61 ms, 74 ms).
  • IMU2 exhibited the highest variability (CoV = 24 ms) for gait events, whereas OMC1 showed the lowest (7 ms).

Conclusions:

  • Sacrum-mounted IMUs provide less accurate and more variable gait event detection compared to OMC methods.
  • Researchers should carefully consider the limitations of IMUs for gait event detection, particularly for segmenting EMG/EEG data.
  • Future validation studies should incorporate variability metrics alongside accuracy measures.