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Updated: Oct 8, 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

Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification.

Jaerock Kwon¹, Yunju Lee², Jehyung Lee³

¹Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

A Siamese Long Short Term Memory (LSTM) network effectively identifies individuals using 3D gait patterns, outperforming traditional feature-based methods. This advancement aids in applications like forensic gait analysis and neurological disorder detection.

Keywords:

gait gait analysis machine learning markerless motion capture person re-identification siamese neural networks vision-based

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Area of Science:

Computer Vision
Biomechanical Engineering
Machine Learning

Background:

Model-based gait analysis utilizes human kinematic characteristics for individual identification.
Extracting gait features from 3D anatomical landmarks is crucial, but 2D images present challenges like viewpoint variance and occlusion.
Estimating 3D joint positions from 2D sequences is essential for real-world gait analysis.

Purpose of the Study:

To compare feature-based and spatiotemporal-based viewpoint-invariant person re-identification using gait patterns.
To evaluate the efficacy of a Siamese LSTM network against traditional gait feature extraction methods.
To assess the potential of gait analysis for applications in rehabilitation, forensics, and medical diagnostics.

Main Methods:

A comparative study was conducted on two approaches: gait features from 3D joint positions and a Siamese LSTM network using 3D spatiotemporal changes.
The Siamese LSTM network classifies individuals directly from gait patterns without explicit feature extraction.
Experiments were performed on the MARS and CASIA-A open datasets to validate and compare the methods.

Main Results:

The Siamese LSTM network demonstrated superior performance, outperforming gait feature-based approaches by 20% on the MARS dataset and 55% on the CASIA-A dataset.
Feature-based gait analysis using 2D and 3D pose estimators was found to be premature.
The study highlights the potential of deep learning models for robust gait recognition.

Conclusions:

The Siamese LSTM network offers a more effective approach to person re-identification using gait patterns compared to traditional feature extraction.
Further research is needed to develop large-scale gait datasets and improve 2D/3D joint position estimators for gait analysis.
Advancements in gait analysis hold promise for diverse applications, including forensic science and early disease detection.