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Related Experiment Video

Updated: Dec 30, 2025

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

Gait Estimation and Analysis from Noisy Observations.

Hafsa Ismail, Ibrahim Radwan, Hanna Suominen

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

Summary

This summary is machine-generated.

This study introduces an affordable gait analysis method using video recordings. It accurately estimates walking movements, enabling early detection of health changes through simple gait parameter measurements.

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Last Updated: Dec 30, 2025

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

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Published on: November 7, 2014

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

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Area of Science:

Biomechanics
Medical Technology
Computer Science

Background:

Human gait is a key health indicator, influenced by pain, illness, aging, and weakness.
Traditional gait analysis methods are often costly and impractical for frequent use.
There is a need for accessible and cost-effective gait analysis solutions.

Purpose of the Study:

To develop an inexpensive gait analysis system using recorded videos.
To validate the accuracy of video-based gait movement estimation against gold-standard 3D motion capture.
To enable early detection of health changes through gait analysis.

Main Methods:

Estimating 2D joint locations from video footage to represent body part movements.
Utilizing a long-short-term memory (LSTM) regression model to predict 3D motion capture data.
Calculating spatial gait parameters such as step length, cadence, and walk base from estimated movements.

Main Results:

Video-based estimated foot movements showed high correlation with Vicon 3D motion capture data.
The method successfully enabled gait analysis by measuring key spatial gait parameters.
The proposed approach offers a reliable and inexpensive alternative for gait assessment.

Conclusions:

Inexpensive video-based gait analysis is feasible and effective.
Early detection of gait changes using this method can facilitate timely medical intervention.
This technology has the potential to improve healthcare monitoring and patient outcomes.