ArticleFrontiers in digital health2023
Comparison of machine learning approaches for near-fall-detection with motion sensors.
Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Usefulness of Wearable Sensors for Detecting Freezing of Gait in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Research of Fall Detection and Fall Prevention Technologies: A Review.Sensors (Basel, Switzerland) · 2026Review
- Tailored interventions to prevent functional decline after a Sentinel Fall presenting to the Emergency Department (iSeFallED) - protocol for a pragmatic mixed-methods implementation study.BMC geriatrics · 2026Article
- Physical activity six months after a severe fall - moderating factors in older individuals.BMC geriatrics · 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
- An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Falls are one of the most common causes of emergency hospital visits in older people. Early recognition of an increased fall risk, which can be indicated by the occurrence of near-falls, is important to initiate interventions. Methods: In a study with 87 subjects we simulated near-fall events on a perturbation treadmill and recorded them with inertial measurement units (IMU) at seven different positions. We investigated different machine learning models for the near-fall detection including support vector machines, AdaBoost, convolutional neural networks, and bidirectional long short-term memory networks. Additionally, we analyzed the influence of the sensor position on the classification results. Results: The best results showed a DeepConvLSTM with an F1 score of 0.954 (precision 0.969, recall 0.942) at the sensor position "left wrist." Discussion: Since these results were obtained in the laboratory, the next step is to evaluate the suitability of the classifiers in the field.
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