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Updated: Jan 9, 2026

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

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

Published on: March 4, 2018

GSAHermNet: A GraphSAGE-Based Neural Network with Hermite Interpolation for Individualized Gait Pattern Generation.

Lin Meng, Shaochen Xu, Hongtao Dong

IEEE Journal of Biomedical and Health Informatics

|December 1, 2025

Summary

This summary is machine-generated.

GSAHermNet accurately generates robotic gait patterns by predicting key events and interpolating trajectories. This novel framework enhances generalizability for robotic gait rehabilitation applications.

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Last Updated: Jan 9, 2026

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

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Published on: March 4, 2018

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Area of Science:

Robotics
Biomechanics
Machine Learning

Background:

Accurate gait pattern generation is crucial for robotic gait rehabilitation.
Conventional methods often struggle with generalizability across different walking conditions.

Purpose of the Study:

To introduce GSAHermNet, a novel two-stage framework for accurate gait trajectory generation.
To improve generalizability and reduce overfitting in gait pattern prediction models.

Main Methods:

A GraphSAGE-based neural network predicts key gait events.
Hermite interpolation reconstructs full joint trajectories.
The model utilizes seven body and walking parameters for prediction.

Main Results:

GSAHermNet achieved high accuracy for hip and knee joints (MAD < 4.58°, r = 0.99).
Ankle joint accuracy was also strong (MAD < 3.69°, r = 0.85).
Outperformed conventional statistical and machine learning methods in accuracy and robustness.

Conclusions:

GSAHermNet offers a promising approach for robotic gait rehabilitation.
Potential applications include adaptive control and personalized motion planning.
Future work aims to establish an online framework for real-time generation.