Treffer: Fall prediction algorithm with built-in instability metrics.

Title:
Fall prediction algorithm with built-in instability metrics.
Authors:
Al-Hammouri S; Biomedical Engineering Department, The University of Arizona, Tucson, AZ, USA; Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan., Wung SF; Betty Irene Moore School of Nursing, University of California, Davis, CA, USA., Chen Z; Aerospace and Mechanical Engineering Department, The University of Arizona, Tucson, AZ, USA., Chen CC; College of Nursing, The University of Arizona, Tucson, AZ, USA., Ortega I; Biomedical Engineering Department, The University of Arizona, Tucson, AZ, USA., Jalali B; Imagine Care INC, El Segundo, CA, USA., Roveda J; Biomedical Engineering Department, The University of Arizona, Tucson, AZ, USA; Imagine Care INC, El Segundo, CA, USA; Electrical and Computer Engineering Department, The University of Arizona, Tucson, AZ, USA., Hazeli K; Biomedical Engineering Department, The University of Arizona, Tucson, AZ, USA; Aerospace and Mechanical Engineering Department, The University of Arizona, Tucson, AZ, USA; Imagine Care INC, El Segundo, CA, USA. Electronic address: hazeli@arizona.edu.
Source:
Journal of biomechanics [J Biomech] 2026 Jan; Vol. 194, pp. 113066. Date of Electronic Publication: 2025 Nov 15.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science Country of Publication: United States NLM ID: 0157375 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2380 (Electronic) Linking ISSN: 00219290 NLM ISO Abbreviation: J Biomech Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York ; Oxford : Elsevier Science
MeSH Terms:
Accidental Falls* , Algorithms* , Artificial Intelligence*, Humans ; Posture/physiology ; Video Recording ; Prediction Algorithms
Contributed Indexing:
Keywords: Camera based system; Fall prediction; Long short-term memory; Posture monitoring
Entry Date(s):
Date Created: 20251120 Date Completed: 20251203 Latest Revision: 20251203
Update Code:
20251204
DOI:
10.1016/j.jbiomech.2025.113066
PMID:
41264953
Database:
MEDLINE

Weitere Informationen

This article introduces an artificial intelligence (AI) platform that uses computer vision to monitor human body posture and predict falls. Typically, camera-based systems for "fall prediction" are rarely investigated compared to "fall detection" systems because falls represent a nonlinear, time-dependent challenge. This complexity is especially prominent when attempts are made to predict falls in uncontrolled environments. In addition, privacy concerns and the requirement to install expensive cameras or sensors are considered key limitations for a camera-based system. To address this gap, we introduce the extraction of new features independent of the camera type that help eliminate the need for high-cost cameras, and improve prediction accuracy without requiring extensive room modification or the addition of body markers or wearable sensors for the user. In this study, we used a 4 K AKASO camera to record different fall scenarios, and the features were extracted from these videos. These features include key landmarks, the centroid's locations, and the angular position of body segments. The results show that this system can accurately predict falls with an approximate accuracy of 91 %. Additionally, the feature importance analysis highlights the significance of the extracted features, indicating that these features have a significant effect on improving the prediction of a fall up to two seconds before it happens, which is three times faster than today's single camera systems.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.