Aerobic gymnastics training performance, fatigue, psychological readiness, and injury risk assessment Other
Conditions
Interventions
Participants were randomly assigned to either an AI-assisted feedback group or a conventional training/control group for 12 weeks using a computer-generated randomisation sequence. The sequence was ge
Sponsors
Hanyang University
Eligibility
Sex/Gender
All
Age
18 Years to 30 Years
Inclusion criteria
Inclusion criteria: 1. Adults aged 18–30 years 2. At least 6 months of aerobic gymnastics experience 3. Active training at least twice per week 4. No musculoskeletal injury within the past 3 months 5. Able to complete the 12-week training protocol 6. Provided written informed consent
Exclusion criteria
Exclusion criteria: 1. Cardiovascular disease 2. Recent surgery 3. Musculoskeletal injury within the past 3 months 4. Inability to complete the 12-week training protocol 5. Inability or unwillingness to provide written informed consent
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| AI model accuracy for aerobic gymnastics performance assessment measured using classification accuracy of the AI-driven multimodal system compared with certified evaluator ratings using multimodal data from video analysis, motion capture, physiological monitoring, cognitive tracking, and psychological assessment, at baseline, week 6, and week 12 | — |
Secondary
| Measure | Time frame |
|---|---|
| Movement quality measured using AI-generated performance scores and certified evaluator ratings based on standardized aerobic gymnastics scoring procedures at baseline, week 6, and week 12 | — |
Countries
China, Korea, South
Contacts
Public ContactGang Qin
Outcome results
None listed