Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis
Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.
Authors
- Athanasios Gkrekidis (ORCID: https://orcid.org/0009-0003-2056-6271)
- Georgios Ch. Sirakoulis (ORCID: https://orcid.org/0000-0001-8240-484X)
- Maria Michalopoulou (ORCID: https://orcid.org/0000-0001-9370-3343)
- Ερασμία Γιαννακού
- Ioannis Kansizoglou (ORCID: https://orcid.org/0000-0003-2064-6442)
- Evangelos Karakasis (ORCID: https://orcid.org/0000-0002-7469-6464)
- Christos Kokkotis (ORCID: https://orcid.org/0000-0003-2972-3117)
- Kyriakos D. Apostolidis (ORCID: https://orcid.org/0000-0003-0394-7454)
- Serafeim Moustakidis (ORCID: https://orcid.org/0000-0002-1090-2177)
- Nikolaos Aggelousis (ORCID: https://orcid.org/0000-0001-5108-7335)
- Evangeli Karampina (ORCID: https://orcid.org/0009-0005-7492-290X)
- Dimitrios Menychtas (ORCID: https://orcid.org/0000-0001-5172-8542)
- Maria Karageorgopoulou
Institutions
- Democritus University of Thrace (GR)
Publication Details
- Journal
- Journal of Functional Morphology and Kinesiology
- Published
- 2025-02-22
- DOI
- https://doi.org/10.3390/jfmk10010073
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00