Healthy Young Adults
Conditions
Keywords
Physical Fatigue Detection, Surface Electromyography (sEMG), Fatigue Monitoring, Wearable Sensors, Biosignal Analysis, Electroenchephalography (EEG), Heart rate (HR)
Brief 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.
Interventions
OTHERFatigue 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.
Sponsors
National Taipei University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 30 Years
Healthy volunteers
Yes
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| EEG (Electroencephalography) Alpha, Beta, Delta, and Theta Band Frequency (Hz) | 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. |
| sEMG (Surface Electromyography) amplitude (μV) and median frequency (MDF) (Hz) | 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. |
| Heart rate (HR) and Heart rate variability (HRV) | 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
| Measure | Time frame | Description |
|---|---|---|
| Body mass index (BMI) | Two times: before and after exercise sessions | BMI is recorded by measuring body weight and height |
| Static muscle strength (N) | 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 |
| Borg rate of perceived exertion score (RPE) | Two sessions: Day 1 (Cycling session) and Day 2 (Squat session) | RPE scale records physical fatigue level for two exercise sessions |
Countries
Taiwan
Contacts
PRINCIPAL_INVESTIGATORMuhammad Adeel, PhD
National Taipei University
Outcome results
None listed