NCT07066462

Exercise Fatigue Prediction in Healthy Individuals

Effect of Exercise on Human Fatigue and Performance in Healthy Individuals

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 17 (actual)
• Sponsor: National Taipei University · Academic / Other
• Sex: All
• Age: 18 Years – 30 Years
• Healthy volunteers: Accepted

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.

Conditions

• Healthy Young Adults

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2025-03-01
• Primary completion: 2025-08-31
• Completion: 2025-11-30
• First posted: 2025-07-15
• Last updated: 2026-04-13

Locations

1 site across 1 country: Taiwan