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

Effectiveness of non-pharmacological interventions for fatigue in adults with long-term conditions: a synopsis of the EIFFEL mixed-methods evidence synthesis.

Health technology assessment (Winchester, England)·2026
Same author

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Validity and Reliability of the Track-UL Algorithm Compared With Kinovea Software for Measuring Upper-Limb Functional Range of Motion in People After Stroke: Cross-Sectional Observational Study.

JMIR rehabilitation and assistive technologies·2026
Same author

Costs and cost effectiveness of the use of telerehabilitation training for upper limb function in people after stroke: A systematic review.

Clinical rehabilitation·2026
Same author

Appreciating the complexity of frailty and user context in digital health intervention design: A qualitative study with personas.

PloS one·2026
Same author

Effectiveness of non-pharmacological interventions for fatigue in long term conditions: systematic review and network meta-analysis.

BMJ medicine·2026
Same journal

Examination of participant sex bias in international society of biomechanics conference abstract submissions: patterns across cohorts, countries, and contexts.

Journal of biomechanics·2026
Same journal

Shear wave velocity of biceps femoris and medial gastrocnemius in different positions and intensities: a cross-sectional study in healthy young males.

Journal of biomechanics·2026
Same journal

Gait event detection using hybrid EMG/IMU systems: effect of SENIAM-constrained sensor placement on lower limb segments.

Journal of biomechanics·2026
Same journal

Relationship between knee adduction moment and knee contact forces during walking and running with modified foot progression angles.

Journal of biomechanics·2026
Same journal

Scaling contact force parameters across body size, limb count, and number of contact spheres.

Journal of biomechanics·2026
Same journal

The extrapolated body center of mass predicts subsequent foot placement choice during dynamic single-leg landings.

Journal of biomechanics·2026
See all related articles

Related Experiment Video

Updated: Feb 19, 2026

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

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.3K

A smart device inertial-sensing method for gait analysis.

Dax Steins1, Ian Sheret2, Helen Dawes3

  • 1Movement Science Group, Faculty of Healthy & Life Sciences, Oxford Brookes University, Oxford, United Kingdom.

Journal of Biomechanics
|October 13, 2014
PubMed
Summary
This summary is machine-generated.

Smart devices can reliably measure the center of mass (COM) during gait, offering a cost-effective solution for remote movement monitoring. This technology has significant potential for patient rehabilitation and therapy, improving healthcare accessibility.

Keywords:
AccelerometerGait analysisInertial measurement unitKalman filterSmartphonesmHealth

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

14.8K
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

9.2K

Related Experiment Videos

Last Updated: Feb 19, 2026

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

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.3K
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

14.8K
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

9.2K

Area of Science:

  • Biomechanics and Human Movement Analysis
  • Wearable Technology and Sensor Fusion
  • Rehabilitation Engineering

Background:

  • Accurate gait analysis is crucial for assessing movement disorders and guiding rehabilitation.
  • Traditional motion capture systems are expensive and not widely accessible.
  • Smart devices offer a potential low-cost alternative for movement monitoring.

Purpose of the Study:

  • To develop and validate a method for analyzing gait patterns using smart device inertial sensors.
  • To compare smart device-based center of mass (COM) measurements with established systems.
  • To assess the reliability and agreement of smart device gait analysis.

Main Methods:

  • Utilized an extended Kalman filter and quaternion rotations to process inertial sensor data.
  • Double integration of vertical acceleration to derive COM displacement.
  • Compared smart device COM data with motion capture and IMU systems.
  • Assessed inter-rater reliability using intraclass correlation coefficients (ICCs) and Bland-Altman plots.

Main Results:

  • Good-to-excellent inter-rater reliability for position (ICCs, .80-.95) and acceleration (ICCs, .54-.81) data.
  • Moderate agreement for position (LOA, 3.1-19.3%) and poor agreement for acceleration (LOA, 6.8%-17.8%) data.
  • Identified a small systematic underestimation of vertical COM position (2.2mm) requiring Kalman filter refinement.

Conclusions:

  • Smart devices provide reliable center of mass (COM) measurements for gait analysis.
  • This method presents a cost-effective, user-friendly approach for remote movement monitoring.
  • Potential to significantly impact patient rehabilitation, therapy, and healthcare expenditure.