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.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
EEG (Electroencephalography) Alpha, Beta, Delta, and Theta Band Frequency (Hz)
Timeframe: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)
sEMG (Surface Electromyography) amplitude (μV) and median frequency (MDF) (Hz)
Timeframe: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)
Heart rate (HR) and Heart rate variability (HRV)
Timeframe: Day 1 (Cycling session) and Day 2 (Squatting session)