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Validity and reliability of deep-learning-based 3D markerless motion capture to measure functional movement

Validity and reliability of deep-learning-based 3D markerless motion capture to measure functional movement

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0009062
Enrollment
33
Registered
2023-12-20
Start date
2023-07-10
Completion date
Unknown
Last updated
2024-01-22

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

Conditions

None listed

Interventions

None listed

Sponsors

Yonsei University Mirae Campus
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: A person who can understand motion instructions and can perform motion without pain for more than 3 seconds

Exclusion criteria

Exclusion criteria: A person with a history of orthopedic surgery and neurological history

Design outcomes

Primary

MeasureTime frame
time series joint angle—

Secondary

MeasureTime frame
peak joint angle—

Countries

Korea, Republic of

Contacts

Public ContactKyungun Bae

Yonsei Graduate School(Mirae)

bku19@naver.com+82-33-760-2497

Outcome results

None listed

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