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Related Experiment Video

Updated: Oct 10, 2025

A Rat Model of Central Fatigue Using a Modified Multiple Platform Method

A Rat Model of Central Fatigue Using a Modified Multiple Platform Method

Published on: August 14, 2018

Classification Model for Discriminating Trunk Fatigue During Running.

Yannis Halkiadakis, Helia Mahzoun Alzakerin, Kristin D Morgan

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 11, 2021

Summary

This summary is machine-generated.

This study used machine learning to identify key running gait changes associated with trunk fatigue. The developed model accurately detects fatigue, aiding in injury prevention for runners.

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Last Updated: Oct 10, 2025

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Area of Science:

Biomechanics
Sports Science
Machine Learning in Healthcare

Background:

Fatigue increases injury risk in runners.
Research on lower extremity fatigue is common, but trunk fatigue's impact on running gait is less understood.

Purpose of the Study:

To identify gait parameters most indicative of trunk fatigue using machine learning.
To develop a machine learning algorithm for classifying trunk fatigue in runners.

Main Methods:

Seventy-two participants underwent a trunk fatigue protocol.
Running biomechanics were captured pre- and post-fatigue using motion capture and an instrumented treadmill.
Gait variables were extracted to train machine learning models.

Main Results:

Stance time, maximum propulsive ground reaction force (GRF), and maximum braking GRF were the best discriminators of fatigue.
A Support Vector Machine (SVM) with Bagging achieved 82% accuracy in classifying fatigued vs. non-fatigued running.

Conclusions:

Machine learning effectively classifies trunk fatigue using specific GRF-derived gait parameters.
This model can be integrated into wearable technology and clinical settings for fatigue detection and injury prevention.