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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: August 12, 2019

Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model.

Jungi Kim¹, Haneol Seo², Muhammad Tahir Naseem²

¹Department of Automotive Lighting Convergence Engineering, Yeungnam University, Gyeongsan 38541, Korea.

Sensors (Basel, Switzerland)

|July 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel spatiotemporal graph convolutional network (ST-GCN) with attention mechanisms for accurate pathological gait classification, aiding in sarcopenia diagnosis.

Keywords:

gait classification global average pooling (GAP)graph convolutional networks (GCN)multiple-input branches (MIB)spatiotemporal graph convolutional networks (ST-GCN)temporal convolutional network (TCN)

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Area of Science:

Biomedical Engineering
Computer Science
Kinesiology

Background:

Gait analysis is crucial for understanding body condition and has applications in human identification, sports science, and medicine.
Pathological gait classification is essential for diagnosing conditions like sarcopenia.

Purpose of the Study:

To develop a spatiotemporal graph convolutional network (ST-GCN) model with attention mechanisms for pathological gait classification.
To extract spatiotemporal features from skeletal data for improved gait analysis.
To enhance the focus on critical joints within gait sequences.

Main Methods:

Utilized skeletal information and joint connections to extract spatiotemporal features.
Applied graph convolutional neural networks (GCNs) for feature extraction.
Developed an attention mechanism to prioritize important joints in gait analysis.

Main Results:

The proposed ST-GCN model with attention demonstrated superior performance in pathological gait classification.
Experiments conducted on three diverse datasets (NTU RGB+D, GIST, MMGS) validated the model's effectiveness.
The model successfully established a system for pathological gait classification for sarcopenia diagnosis.

Conclusions:

The developed attention-based ST-GCN model offers a robust approach for pathological gait classification.
This model shows significant potential for the early diagnosis of sarcopenia and other gait-related conditions.
The findings highlight the efficacy of integrating attention mechanisms into GCNs for complex human movement analysis.