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Exercise Fatigue Prediction in Healthy Individuals

NCT07066462 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 17

Last updated 2026-04-13

No results posted yet for this study

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.

Conditions

  • Healthy Young Adults

Interventions

OTHER

Fatigue 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 & Collaborators

  • National Taipei University

    lead OTHER

Principal Investigators

  • Muhammad Adeel, PhD · National Taipei University

Study Design

Allocation
NA
Purpose
SCREENING
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
30 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-03-01
Primary Completion
2025-08-31
Completion
2025-11-30

Countries

  • Taiwan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07066462 on ClinicalTrials.gov