IFRA: A Machine Learning-Based Instrumented Fall Risk Assessment Scale Derived from an Instrumented Timed Up and Go Test in Stroke Patients.

ABSTRACT: Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 1

SUBMITTER: Maccio S 

PROVIDER: S-EPMC12841214 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature