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Fatigue

Fatigue

Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...

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Related Experiment Video

Updated: Jul 15, 2025

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse

Published on: May 31, 2016

A Data-Driven Approach for Fatigue Detection during Running Using Pedobarographic Measurements.

Zixiang Gao^1,2,3, Liangliang Xiang^1,4, Gusztáv Fekete³

¹Department of Radiology, Ningbo No. 2 Hospital, Ningbo 315010, China.

Applied Bionics and Biomechanics

|October 5, 2023

Summary

Detecting running fatigue early can prevent overuse injuries. This study found significant changes in foot plantar force distribution after fatigue, with a deep learning model accurately identifying fatigued gaits.

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Area of Science:

Biomechanics and Sports Science
Machine Learning in Sports
Gait Analysis

Background:

Early detection of running fatigue is crucial for preventing overuse injuries.
Investigating the impact of fatigue on plantar force distribution in amateur runners' feet.

Purpose of the Study:

To analyze how running fatigue affects plantar force distribution in dominant and nondominant limbs.
To develop and evaluate deep learning models for automatic fatigue gait recognition.

Main Methods:

Thirty amateur runners underwent a fatigue protocol.
Bilateral time-series plantar forces were measured using a FootScan plate before and after fatigue.
Convolutional Neural Network (CNN) and CNN-based Long Short-Term Memory (ConvLSTM) models were trained using Python.

Main Results:

Fatigue altered plantar force distribution, increasing mid-forefoot and heel force in the dominant limb.
Peak force timing shifted in both midfoot/sum (nondominant) and hallux (dominant) regions.
The ConvLSTM model achieved higher accuracy (0.867) in fatigue gait detection compared to CNN (0.800).

Conclusions:

Findings provide data for assessing overuse injury risk factors in single limbs.
Early detection of fatigued gait is feasible using plantar force analysis and deep learning.