Exercise Fatigue Prediction in Healthy Individuals
Effect of Exercise on Human Fatigue and Performance in Healthy Individuals
National Taipei University
30 participants
Mar 1, 2025
INTERVENTIONAL
Conditions
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.
Eligibility
Inclusion Criteria4
- Individuals between 18 and 30 years old
- Healthy college students who regularly exercise
- Participants who meet the World Health Organization (WHO) guidelines for physical activity: at least 150-300 minutes of aerobic activity per week or muscle-strengthening exercises for major muscle groups on 2 or more days per week
- Participants who provide written informed consent
Exclusion Criteria4
- Individuals younger than 18 or older than 30
- History of any metabolic, systemic, or musculoskeletal disorder
- Recent injury or surgery
- Failure to pass the pre-exercise fitness screening questionnaire (PAR-Q)
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Interventions
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)
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NCT07066462