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• W4383344985 endingPage "1938" @default.
• W4383344985 startingPage "1938" @default.
• W4383344985 abstract "Fall-risk assessment studies generally focus on identifying characteristics that affect postural balance in a specific group of subjects. However, falls affect a multitude of individuals. Among the groups with the most recurrent fallers are the community-dwelling elderly and stroke survivors. Thus, this study focuses on identifying a set of features that can explain fall risk for these two groups of subjects. Sixty-five community dwelling elderly (forty-nine female, sixteen male) and thirty-five stroke-survivors (twenty-two male, thirteen male) participated in our study. With the use of an inertial sensor, some features are extracted from the acceleration data of a Timed Up and Go (TUG) test performed by both groups of individuals. A short-form berg balance scale (SFBBS) score and the TUG test score were used for labeling the data. With the use of a 100-fold cross-validation approach, Relief-F and Extra Trees Classifier algorithms were used to extract sets of the top 5, 10, 15, 20, 25, and 30 features. Random Forest classifiers were trained for each set of features. The best models were selected, and the repeated features for each group of subjects were analyzed and discussed. The results show that only the stand duration was an important feature for the prediction of fall risk across all clinical tests and both groups of individuals." @default.
• W4383344985 created "2023-07-07" @default.
• W4383344985 creator A5008600245 @default.
• W4383344985 creator A5022717901 @default.
• W4383344985 creator A5057594423 @default.
• W4383344985 creator A5071776339 @default.
• W4383344985 date "2023-07-05" @default.
• W4383344985 modified "2023-10-15" @default.
• W4383344985 title "Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data" @default.
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• W4383344985 doi "https://doi.org/10.3390/healthcare11131938" @default.
• W4383344985 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37444772" @default.
• W4383344985 hasPublicationYear "2023" @default.

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