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

Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions.

Scientific reports·2026
Same author

An Approach Toward Radioiodination and Radiopharmacological Evaluation of a Carborane-Containing Analog of Indomethacin.

Molecules (Basel, Switzerland)·2026
Same author

Wearable Sensor-Derived Gait Parameters Across Self-Reported Physical Activity Levels in Individuals With Knee Osteoarthritis and Healthy Controls: Pilot Cross-Sectional Validation Study.

JMIR formative research·2026
Same author

Automatic Delineation of Tumor Spheroids in Microscopic Images Using Deep-Learning.

ACS measurement science au·2026
Same author

Diagnostic Twins: Exploring the Radiohybrid Concept with Iodine-123 and Lanthanum-133 for PSMA-Targeted SPECT and PET Imaging.

Journal of medicinal chemistry·2026
Same author

Contactless Sleep Staging With Radar: A Transfer Learning Approach.

IEEE open journal of engineering in medicine and biology·2026

Related Experiment Video

Updated: Nov 21, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K

Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies.

Matthias Tietsch1,2, Amir Muaremi1, Ieuan Clay3

  • 1Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland.

Digital Biomarkers
|January 14, 2021
PubMed
Summary

Inconsistent wearable sensor placement affects human gait analysis. A new autocorrelation method for step detection improves accuracy, allowing more flexible and comfortable sensor wear in clinical studies.

Keywords:
AutocorrelationGait monitoringInertial sensorStep detectionWaist-worn

More Related Videos

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.5K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.0K

Related Experiment Videos

Last Updated: Nov 21, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.5K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.0K

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Clinical Biomechanics

Background:

  • Inertial sensors offer valuable gait analysis for various health impairments.
  • Real-world gait monitoring requires long-term, consistent data collection.
  • Inconsistent sensor placement can compromise gait characterization and clinical study outcomes.

Purpose of the Study:

  • To analyze the impact of varying waist-worn inertial sensor positions on gait signals.
  • To develop a robust step detection method resilient to sensor placement variations.
  • To enhance flexibility and participant adherence in clinical gait studies.

Main Methods:

  • Investigated the effects of asymmetric sensor placement on frequency spectrum components.
  • Developed and validated a step detection algorithm utilizing autocorrelation.
  • Evaluated the performance of the proposed method in terms of sensitivity and precision.

Main Results:

  • Asymmetric sensor wear introduces additional odd-harmonic frequency components.
  • The autocorrelation-based step detection achieved high sensitivity (0.99) and precision (0.99).
  • The proposed method effectively mitigates the impact of sensor position variability.

Conclusions:

  • A robust, position-agnostic gait assessment is achievable with advanced signal processing.
  • The developed method allows for more flexible sensor placement, improving participant comfort and adherence.
  • This approach represents a significant step towards position-agnostic gait analysis in clinical settings.