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Completed

NCT Number: NCT07066462

Exercise Fatigue Prediction 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.

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Key information

Conditions

Healthy Young Adults

Age range

18 year–30 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

National Taipei University, Master Program in Smart Healthcare Management

New Taipei City, 237303, Taiwan

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

Fatigue Exercise Protocol with Biosignal Monitoring

Other

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.

Primary outcomes

  1. EEG (Electroencephalography) Alpha, Beta, Delta, and Theta Band Frequency (Hz)

    Time frame: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)

    Relative power in the alpha (8 to 12 Hz), beta (12 to 30 Hz), delta (2 to 4 Hz), and theta (4 to 8 Hz) bands extracted from EEG signals recorded during exercise. Alpha power is associated with the onset of physical fatigue and is computed using MATLAB.

  2. sEMG (Surface Electromyography) amplitude (μV) and median frequency (MDF) (Hz)

    Time frame: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)

    sEMG (microvolts) recorded from both sides of the quadriceps, hamstrings, tibialis anterior, and gastrocnemius muscles. Signal processing will be performed to compute amplitude and median frequency, assessing neuromuscular activation and fatigue during exercise.

  3. Heart rate (HR) and Heart rate variability (HRV)

    Time frame: Day 1 (Cycling session) and Day 2 (Squatting session)

    Heart rate (HR) and heart rate variability (HRV) are recorded in beats per minute (bpm) throughout cycling and squat sessions. Average and peak heart rates, as well as average heart rate variability (HRV), are used to evaluate physical fatigue and cardiovascular stress.

Secondary outcomes

  1. Body mass index (BMI)

    Time frame: Two times: before and after exercise sessions

    BMI is recorded by measuring body weight and height

  2. Static muscle strength (N)

    Time frame: Two times: before and after exercise sessions

    Static muscle strength in Newton of both sides of the quadriceps, hamstrings, tibialis anterior, and gastrocnemius is recorded using a dynamometer

  3. Borg rate of perceived exertion score (RPE)

    Time frame: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)

    RPE scale records physical fatigue level for two exercise sessions

Sponsors and collaborators

Lead sponsor

National Taipei University

Other

Registry information

Official study title

Effect of Exercise on Human Fatigue and Performance in Healthy Individuals

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Jul 15, 2025
Registry last updated
Apr 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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