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Exercise Fatigue Prediction in Healthy Individuals
Sponsor: National Taipei University
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
Conditions
Interventions
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