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 Experiment Video

Updated: Dec 30, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Deep Learning Techniques for Improving Digital Gait Segmentation.

Matteo Gadaleta, Giulia Cisotto, Michele Rossi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    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

    Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study.

    NPJ Parkinson's disease·2026
    Same author

    Laboratory-based turning performance during walking in people with mild cognitive impairment and dementia.

    Journal of Alzheimer's disease : JAD·2026
    Same author

    Turning as a single and dual task-associations with Alzheimer's and vascular pathologies in cognitively healthy older people.

    The journals of gerontology. Series A, Biological sciences and medical sciences·2026
    Same author

    The construct validity of real-world digital mobility outcomes in people with COPD.

    ERJ open research·2026
    Same author

    IDEA-FAST clinical study protocol: Identifying digital end-points of fatigue, sleep quality and daytime sleepiness in N = 2000.

    Digital health·2026
    Same author

    Privacy and personalisation: predicting Parkinson's disease severity from real-world gait with federated learning.

    Frontiers in aging neuroscience·2026
    Same journal

    Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same journal

    AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same journal

    Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same journal

    Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same journal

    Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same journal

    A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    See all related articles

    Deep learning accurately detects gait events using wearable sensors, outperforming traditional methods. This technology offers reliable gait analysis for Parkinson

    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Gait Analysis

    Background:

    • Wearable technology enables advanced gait analysis outside clinical settings.
    • Accurate detection of gait events (foot contacts) is crucial for gait quantification.
    • Previous methods like wavelet transforms have limitations in precision.

    Purpose of the Study:

    • To apply deep learning (DL) for gait event detection using wearable inertial sensors.
    • To validate DL gait analysis against gold standards and wavelet transforms (WT).
    • To assess DL performance in quantifying high-level gait characteristics.

    Main Methods:

    • A novel method using dilated convolutions for gait event detection from wearable inertial sensors.
    • Data from 71 people with Parkinson's disease (PD) and 67 healthy controls.

    More Related Videos

    Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
    08:04

    Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

    Published on: April 23, 2020

    7.1K

    Related Experiment Videos

    Last Updated: Dec 30, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K
    Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
    08:04

    Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

    Published on: April 23, 2020

    7.1K
  • Sensors placed on the lumbar vertebrae and ankles.

    • Main Results:

    • The DL approach demonstrated high reliability in gait event detection.
    • Achieved significantly smaller temporal errors compared to WT, especially for final foot contacts.
    • Inter-quartile range for final contact detection was below 70 ms in the worst case.

    Conclusions:

    • Deep learning offers a reliable and accurate method for gait event detection using wearable sensors.
    • This approach surpasses traditional methods like WT in precision for gait analysis.
    • Paves the way for data-driven gait assessment systems in clinical and real-world settings.