Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.

ABSTRACT: There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors or activities to achieve higher accuracy. However, multiple sensors and activities reduce the availability of these systems. This study aims to develop a system to perform fall prediction for the elderly by using signals recorded from a single sensor during a short-term activity. A total of 168 features in the time and frequency domains were created using acceleration signals obtained from 71 elderly people. The features were weighted based on the ReliefF algorithm, and the artificial neural networks model was developed using the most importan

SUBMITTER: Altunkaya S 

PROVIDER: S-EPMC11568989 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature