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RECRUITING
NCT07066462
NA

Exercise Fatigue Prediction in Healthy Individuals

Sponsor: National Taipei University

View on ClinicalTrials.gov

Summary

The goal of this research study is to develop an AI-based model to detect physical fatigue in healthy young adults. The main questions it aims to answer are: 1. Can muscle, heart, and brain signals be used to predict physical fatigue in real time? 2. How accurately can an AI model detect fatigue based on these signals? Participants will: * Perform moderate to high intensity physical exercises, including static bicycling and dumbbell squats, while wearing non-invasive sensors that measure muscle activity (sEMG), heart rate (HR), and brain activity (EEG). * Before starting the exercises, participants will complete a brief warm-up session that includes stretching and mobility movements. * Each participant undergoes two training sessions, with pre- and post-evaluations of their physical fitness status and static muscle strength.

Official title: Effect of Exercise on Human Fatigue and Performance in Healthy Individuals

Key Details

Gender

All

Age Range

18 Years - 30 Years

Study Type

INTERVENTIONAL

Enrollment

30

Start Date

2025-03-01

Completion Date

2025-11

Last Updated

2025-07-15

Healthy Volunteers

Yes

Interventions

OTHER

Fatigue Exercise Protocol with Biosignal Monitoring

Participants will complete two fatiguing exercises, including static bicycling and dumbbell squats. During each exercise, surface electromyography (sEMG), electroencephalography (EEG), and heart rate (HR) will be recorded to analyze fatigue levels.

Locations (1)

National Taipei University, Master Program in Smart Healthcare Management

New Taipei City, Taiwan