Application of machine learning for detecting high fall risk in middle-aged workers using video-based analysis of the first 3 steps
These findings suggest that fall risk prediction can be developed based on ML and the first 3 steps in men; however, the accuracy was only "sufficient" in women. Further development of the formula for women is required to improve its accuracy in the middle-aged working population.
Authors
- Junichi Hozumi
- Akiko Suganuma (ORCID: https://orcid.org/0000-0002-2005-5151)
- Mitsuharu Hosokawa
- Ken Hirano (ORCID: https://orcid.org/0000-0001-9696-5066)
- Naoki Sakane (ORCID: https://orcid.org/0000-0002-2625-571X)
- Yamauchi Ken
- Masayuki Domichi
- Masashi Ishimaru
- Yasukazu Izawa
- Kengo Wada
- Yoshihiro Matsumura
- Ippei Kutsuna
Institutions
- Panasonic (Japan) (JP)
- Keio University (JP)
- Kyoto Medical Center (JP)
Publication Details
- Journal
- Journal of Occupational Health
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/joccuh/uiae075
- Citations
- 3
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 14.28
Funders
- Japan Society for the Promotion of Science