ArticleSensors (Basel, Switzerland)2022
Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Development of a Wearable Sensor Platform for Fall Risk Assessment and Fall Detection.Sensors (Basel, Switzerland) · 2026Article
- Intelligent fall risk prediction and real-time warning system for elderly care based on multimodal deep learning and wearable sensor fusion.Scientific reports · 2026Article
- Article
- VIGMA: An Open-Access Framework for Visual Gait and Motion Analytics.IEEE transactions on visualization and computer graphics · 2025Article
- Differential Cortical Activations Among Young Adults Who Fall Versus Those Who Recover Successfully Following an Unexpected Slip During Walking.Brain sciences · 2025Article
- Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique.Sensors (Basel, Switzerland) · 2025Article
- Automatic Detection of Gait Perturbations With Everyday Wearable Technology.IEEE open journal of engineering in medicine and biology · 2025Article
- A framework to automatically detect near-falls using a wearable inertial measurement cluster.Communications engineering · 2024Article
- Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.Sensors (Basel, Switzerland) · 2024Article
- Comparison of professional and everyday wearable technology at different body positions in terms of recording gait perturbations.PLOS digital health · 2024Article
- The measurement and reporting of falls: Recommendations for research and practice on defining faller types.Journal of frailty, sarcopenia and falls · 2023Article
- Comparison of machine learning approaches for near-fall-detection with motion sensors.Frontiers in digital health · 2023Article
- Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients.Healthcare (Basel, Switzerland) · 2022Article
- Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Slip-induced falls are a growing health concern for older adults, and near-fall events are associated with an increased risk of falling. To detect older adults at a high risk of slip-related falls, this study aimed to develop models for near-fall event detection based on accelerometry data collected by body-fixed sensors. Thirty-four healthy older adults who experienced 24 laboratory-induced slips were included. The slip outcomes were first identified as loss of balance (LOB) and no LOB (NLOB), and then the kinematic measures were compared between these two outcomes. Next, all the slip trials were split into a training set (90%) and a test set (10%) at sample level. The training set was used to train both machine learning models (n = 2) and deep learning models (n = 2), and the test set was used to evaluate the performance of each model. Our results indicated that the deep learning models showed higher accuracy for both LOB (>64%) and NLOB (>90%) classifications than the machine learning models. Among all the models, the Inception model showed the highest classification accuracy (87.5%) and the largest area under the receiver operating characteristic curve (AUC), indicating that the model is an effective method for near-fall (LOB) detection. Our approach can be helpful in identifying individuals at the risk of slip-related falls before they experience an actual fall.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.