Healthy Young AdultsNCT07066462
Exercise Fatigue Prediction in Healthy Individuals
A study testing Fatigue Exercise Protocol with Biosignal Monitoring in people with Healthy Young Adults, run by National Taipei University at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 30 yearsHealthy volunteers can take part
- Conditions the study is for
- Healthy Young Adults
- What is being tested?
- Fatigue Exercise Protocol with Biosignal Monitoring
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 17 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Mar 2025to 30 Nov 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 30 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- 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
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
Taiwan1 site
National Taipei University, Master Program in Smart Healthcare Management
New Taipei City, 237303
About this study
Official title: 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:
- Can muscle, heart, and brain signals be used to predict physical fatigue in real time?
- How accurately can an AI model detect fatigue based on these signals?
Read more
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
- Being tested
- Fatigue Exercise Protocol with Biosignal Monitoring
- Keywords
- Physical Fatigue Detection, Surface Electromyography (sEMG), Fatigue Monitoring, Wearable Sensors, Biosignal Analysis, Electroenchephalography (EEG), Heart rate (HR)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Fatigue Exercise Protocol with Biosignal MonitoringOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Single Group Protocol | Experimental | Participants in this arm will perform two physical exercises on different days while wearing sensors that measure muscle activity (sEMG), brain activity (EEG), and heart rate (HR). Receives: Other: Fatigue Exercise Protocol with Biosignal Monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
EEG (Electroencephalography) Alpha, Beta, Delta, and Theta Band Frequency (Hz)
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.
Measured: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)
sEMG (Surface Electromyography) amplitude (μV) and median frequency (MDF) (Hz)
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.
Measured: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)
Heart rate (HR) and Heart rate variability (HRV)
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.
Measured: Day 1 (Cycling session) and Day 2 (Squatting session)
Secondary outcomes (3)
Body mass index (BMI)
BMI is recorded by measuring body weight and height
Measured: Two times: before and after exercise sessions
Static muscle strength (N)
Static muscle strength in Newton of both sides of the quadriceps, hamstrings, tibialis anterior, and gastrocnemius is recorded using a dynamometer
Measured: Two times: before and after exercise sessions
Borg rate of perceived exertion score (RPE)
RPE scale records physical fatigue level for two exercise sessions
Measured: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jul 2025
- Study start1 Mar 2025
- Primary completion31 Aug 2025
- Study completion30 Nov 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Healthy Young AdultsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 17 participants (actual)
Sponsor and investigators
- Lead sponsor
- National Taipei University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, National Taipei University, National Taipei University
- Investigators
- Muhammad Adeel, PhD, Principal investigator, National Taipei University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07066462
- Sponsor's protocol ID
- ECKIRB1121004
- Status last verified
- Apr 2026
- First submitted
- 17 Jun 2025
- Last updated
- 13 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Chen YL, Chen CC, Hsia PY, Lin SK. Relationships of Borg's RPE 6-20 scale and heart rate in dynamic and static exercises among a sample of young Taiwanese men. Percept Mot Skills. 2013 Dec;117(3):971-82. doi: 10.2466/03.08.PMS.117x32z6.
PubMed 24665812Buzsaki, G. (2006). Rhythms of the Brain: Oxford university press
Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput JP, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020 Dec;54(24):1451-1462. doi: 10.1136/bjsports-2020-102955.
PubMed 33239350Borghini G, Vecchiato G, Toppi J, Astolfi L, Maglione A, Isabella R, Caltagirone C, Kong W, Wei D, Zhou Z, Polidori L, Vitiello S, Babiloni F. Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6442-5. doi: 10.1109/EMBC.2012.6347469.
PubMed 23367404Bedo BLS, Pereira DR, Moraes R, Kalva-Filho CA, Will-de-Lemos T, Santiago PRP. The rapid recovery of vertical force propulsion production and postural sway after a specific fatigue protocol in female handball athletes. Gait Posture. 2020 Mar;77:52-58. doi: 10.1016/j.gaitpost.2020.01.017. Epub 2020 Jan 21.
PubMed 31986376Adeel M, Chen HC, Lin BS, Lai CH, Wu CW, Kang JH, Liou JC, Peng CW. Oxygen Consumption (VO2) and Surface Electromyography (sEMG) during Moderate-Strength Training Exercises. Int J Environ Res Public Health. 2022 Feb 16;19(4):2233. doi: 10.3390/ijerph19042233.
PubMed 35206420
Study at a glance
From the registry record, last updated by the sponsor on 13 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of Exercise on Human Fatigue and Performance in Healthy Individuals
- ClinicalTrials.gov ID
- NCT07066462
- Acronym / sponsor ID
- ECKIRB1121004
- Conditions
- Healthy Young Adults
- Keywords
- Physical Fatigue Detection, Surface Electromyography (sEMG), Fatigue Monitoring, Wearable Sensors, Biosignal Analysis, Electroenchephalography (EEG), Heart rate (HR)
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 17 participants (actual)
- Lead sponsor
- National Taipei UniversityUniversity, hospital or other
- Study start
- 1 Mar 2025 (actual)
- Primary completion
- 31 Aug 2025 (actual)
- Study completion
- 30 Nov 2025 (actual)
- First posted
- 15 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Can muscle, heart, and brain signals be used to predict physical fatigue in real time?
- 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.
Study design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Single Group Protocol | Experimental | Other: Fatigue Exercise Protocol with Biosignal MonitoringParticipants in this arm will perform two physical exercises on different days while wearing sensors that measure muscle activity (sEMG), brain activity (EEG), and heart rate (HR). |
Who may qualify
- Ages
- 18 to 30 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (4 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Fatigue Exercise Protocol with Biosignal MonitoringParticipants 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. | Other | Single Group Protocol |
Primary outcome
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.
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.
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.
3 secondary outcomes
Body mass index (BMI)
Time frame: Two times: before and after exercise sessions
Static muscle strength (N)
Time frame: Two times: before and after exercise sessions
Borg rate of perceived exertion score (RPE)
Time frame: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Apr 2026.
- Background: PubMed 24665812Chen YL, Chen CC, Hsia PY, Lin SK. Relationships of Borg's RPE 6-20 scale and heart rate in dynamic and static exercises among a sample of young Taiwanese men. Percept Mot Skills. 2013 Dec;117(3):971-82. doi: 10.2466/03.08.PMS.117x32z6.
- BackgroundBuzsaki, G. (2006). Rhythms of the Brain: Oxford university press
- Background: PubMed 33239350Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput JP, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020 Dec;54(24):1451-1462. doi: 10.1136/bjsports-2020-102955.
- Background: PubMed 23367404Borghini G, Vecchiato G, Toppi J, Astolfi L, Maglione A, Isabella R, Caltagirone C, Kong W, Wei D, Zhou Z, Polidori L, Vitiello S, Babiloni F. Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6442-5. doi: 10.1109/EMBC.2012.6347469.
- Background: PubMed 31986376Bedo BLS, Pereira DR, Moraes R, Kalva-Filho CA, Will-de-Lemos T, Santiago PRP. The rapid recovery of vertical force propulsion production and postural sway after a specific fatigue protocol in female handball athletes. Gait Posture. 2020 Mar;77:52-58. doi: 10.1016/j.gaitpost.2020.01.017. Epub 2020 Jan 21.
- Background: PubMed 35206420Adeel M, Chen HC, Lin BS, Lai CH, Wu CW, Kang JH, Liou JC, Peng CW. Oxygen Consumption (VO2) and Surface Electromyography (sEMG) during Moderate-Strength Training Exercises. Int J Environ Res Public Health. 2022 Feb 16;19(4):2233. doi: 10.3390/ijerph19042233.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Muhammad Adeel, PhD principal investigator
National Taipei University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Fatigue Exercise Protocol with Biosignal Monitoring in people with Healthy Young Adults, run by National Taipei University at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
EEG (Electroencephalography) Alpha, Beta, Delta, and Theta Band Frequency (Hz) (time frame: Two sessions: Day 1 (Cycling session) and Day 2 (Squat session)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07066462, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Apr 2026; AzTrials last refreshed its copy on 1 Nov 2026.