Exploring the Application of Machine Learning for Daily-Life Near-Fall Detection in Older Adults.

Authors

Nina Marie Schmidt, Tania Zieschang, Lea Feld, Laura Himmelmann, Elisa-Marie Speckmann, Tim Stuckenschneider

Published in

Geriatrics & gerontology international. Volume 26. Issue 10. Pages e70862.

Abstract

Falls are a health concern among older adults, and near-falls may serve as an early indicator of future falls, functional decline, and the need for targeted preventive interventions. While previous research has successfully demonstrated near-fall detection using machine learning in controlled laboratory environments, it remains crucial to evaluate the application of such approaches in daily-life settings.
This study examines the application of a pre-trained machine learning algorithm to detect gait perturbations in daily-life and to explore how these classified gait perturbations are associated with participant characteristics.
Data from the SeFallED study was used, which followed up on individuals aged ≥ 60 years who presented to the emergency department following a fall. Daily stepping time was recorded using a thigh-worn accelerometer over 7 days, segmented into windows, and analyzed using a laboratory-based pre-trained one-dimensional convolutional neural network to detect near-falls.
A total of 202 participants (median age 75 years) were included in the analysis. On average, 58% of total stepping time windows were classified as gait perturbation windows. Higher age, lower cognitive and functional performance, greater concerns about falling, and higher levels of frailty were associated with a greater proportion of classified gait perturbations.
The laboratory-trained algorithm classified a high number of windows as gait perturbations, likely reflecting gait irregularities including misclassifications rather than exclusively near-falls. Although these classifications still meaningfully reflect participant characteristics, further training and validation are required to enhance the algorithm's ability to accurately distinguish and objectively assess gait perturbations and near-falls in daily-life.

PMID:
42878241
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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