Quantitative analysis of gait and balance using deep learning on monocular videos and the timed up and go test in idiopathic normal-pressure hydrocephalus
This study identified important associations between gait parameters measured by vision-based gait analysis and TUG scores in INPH patients. An automated machine learning model based on gait parameters measured by vision-based gait analysis can predict falling risk with excellent performance in INPH patients. We suggest that our vision-based gait analysis method using monocular videos has the potential to bridge the gap between laboratory testing and clinical assessment of gait and balance in INPH patients.
Authors
- Sungmoon Jeong (ORCID: https://orcid.org/0000-0002-4579-3150)
- Hosang Yu (ORCID: https://orcid.org/0000-0001-6219-2317)
- Sang‐Wook Kim (ORCID: https://orcid.org/0000-0002-6345-9084)
- Kyunghun Kang (ORCID: https://orcid.org/0000-0003-2221-8046)
- Hee‐Jin Cho
Institutions
- Kyungpook National University Hospital (KR)
- Kyungpook National University (KR)
- Taegu Science University (KR)
- Daegu University (KR)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-10-14
- DOI
- https://doi.org/10.3389/fnagi.2025.1644543
- Primary Topic
- Cerebrospinal fluid and hydrocephalus
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea Health Industry Development Institute
- Iran Telecommunication Research Center
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion