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AI reads your body signals to spot fatigue in real time

NCT ID NCT07066462

Knowledge-focused Sponsor: National Taipei University Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study tested whether an AI model can detect physical fatigue in healthy young adults by monitoring muscle, heart, and brain signals during exercise. Seventeen participants did cycling and squats while wearing non-invasive sensors. The goal was to see how accurately these signals predict fatigue, which could help improve training and prevent injury.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Phase

Not a phased trial

Phase numbers describe drug development. The registry uses this when they do not apply, as it does for trials of devices, procedures or behaviour changes, and for observational studies.

Participants

17 people

The number who actually took part.

Started

Mar 2025

Finished

Nov 2025

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Ages

18 to 30 years

Sex

Anyone

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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)

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As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Healthy young adults

Contacts and locations

Locations

  • National Taipei University, Master Program in Smart Healthcare Management

    New Taipei City, 237303, Taiwan

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