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

Abstract

ABSTRACT Background 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. Aim 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. Methods 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. Results 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. Conclusion 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.