Exercise Fatigue Prediction in Healthy Individuals

Effect of Exercise on Human Fatigue and Performance in Healthy Individuals

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.

Inclusion Criteria:

• 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 Criteria:

• 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)