ArticleGeriatrics & gerontology international2026
Exploring the Application of Machine Learning for Daily-Life Near-Fall Detection in Older Adults.
Article in Geriatrics & gerontology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundFalls 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.
aimThis 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.
methodsData 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.
resultsA 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.
conclusionThe 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.
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