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Related Experiment Video

Updated: Aug 29, 2025

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

14.0K

Transforming Gait: Video-Based Spatiotemporal Gait Analysis.

R James Cotton, Emoonah McClerklin, Anthony Cimorelli

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
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    This study developed a neural network for accurate human pose estimation from monocular video, providing clinically meaningful gait analysis metrics for rehabilitation and movement science.

    Area of Science:

    • Biomechanics
    • Rehabilitation Technology
    • Computer Vision

    Background:

    • Human pose estimation from monocular video is advancing rapidly, showing promise for human movement science and rehabilitation.
    • However, ensuring clinical meaningfulness and calibration of outputs remains a challenge.
    • Traditional gait analysis requires dedicated laboratory setups for precise kinematic and timing measurements.

    Purpose of the Study:

    • To develop a neural network capable of estimating clinically meaningful biomechanical outputs from monocular video.
    • To enable accurate gait cycle timing and spatiotemporal measurements using non-laboratory video data.
    • To bridge the gap between advanced pose estimation and practical clinical application in gait analysis.

    Main Methods:

    • Trained a neural network using over 7000 monocular videos from an instrumented gait analysis lab.

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    Related Experiment Videos

    Last Updated: Aug 29, 2025

    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

    14.0K
    Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
    07:51

    Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

    Published on: March 14, 2017

    16.9K
    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

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  • Mapped 3D joint trajectories and individual height to biomechanical outputs.
  • Incorporated a task-specific layer for accurate estimation of gait events and parameters.

    • Main Results:

    • The neural network accurately estimates foot contact and foot off events.
    • Cycle-by-cycle analysis of parsed kinematic outputs yields precise measurements.
    • Accurate estimation of cadence, step time, support times, walking speed, and step length was achieved.

    Conclusions:

    • Monocular video-based human pose estimation can provide clinically meaningful gait analysis.
    • The developed neural network offers a promising tool for accessible and accurate gait assessment.
    • This approach has the potential to enhance human movement science and rehabilitation practices.