Development of squat posture evaluation app using deep learning and verification of effectiveness

New normal exercise plan: Application of squat posture evaluation app using deep learning

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

CRIS

Registry ID

KCT0008178

Enrollment

Registered

2023-02-10

Start date

2021-03-08

Completion date

Unknown

Last updated

2023-03-06

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

Conditions

None listed

Interventions

Behavioral : 20 squat beginners were classified into 10 experimental groups and 10 control groups. The experimental group uses the app developed by the research team to perform squat exercise for 30 m

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Subjects who have no experience learning squat exercise

Exclusion criteria

Exclusion criteria: Those with nervous, musculoskeletal, or cardiovascular diseases, those without a normal range of joint motion, and researchers affiliated with this laboratory are excluded.

Design outcomes

Primary

Measure	Time frame
The score of the squat posture evaluation app developed by our research team	—

Secondary

Measure	Time frame
Knee range of motion using IMU sensor;Knee joint strength and muscular endurance using CYBEX	—

Countries

Korea, Republic of

Contacts

Public ContactJi Been Kim

Yonsei University Wonju College of Medicine

jibeen7@pusan.ac.kr+82-51-510-1970

Outcome results