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Machine Learning-Based Classification of Muscle Fatigue Using Force Profile Features

Machine Learning-Based Classification of Muscle Fatigue Using Force Profile Features

Status
Active, not recruiting
Phases
Unknown
Study type
Interventional
Source
CRIS
Registry ID
KCT0010167
Enrollment
32
Registered
2025-02-05
Start date
2024-10-30
Completion date
Unknown
Last updated
2025-03-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

Others(Bench Press Exercises) : All tests were conducted using a modified, isokinetic-based Smith machine (XIM Machine, Ronfic Inc., South Korea). The machine’s calibrated was conducted following the

Sponsors

Yonsei University
Lead Sponsor

Eligibility

Sex/Gender
Male

Inclusion criteria

Inclusion criteria: All participants met the following eligibility criteria: (1) recreational weightlifters with at least two years of training experience, (2) engaging in bench press exercises 2–3 times per week, and (3) no history of upper body injuries that could influence performance.

Exclusion criteria

Exclusion criteria: (1) Participants must be male and between the ages of 20 and 40. (2) Participants must meet the criteria for recreational athletes/exercisers. (3) Participants must not have experienced any upper body surgery or injury within 12 months prior to participating in this study. (4) Participants must not have any cardiovascular, cardiopulmonary, or neurological diseases, or any other conditions that would impede participation in sports activities.

Design outcomes

Primary

MeasureTime frame
Classification performance outcomes of the model (AUROC, specificity, sensitivity)

Countries

Korea, Republic of

Contacts

Public ContactJunhyeong Kwon

Yonsei University

kwonjh15@gmail.com+82-70-5220-0243

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Feb 4, 2026