ArticleSensors (Basel, Switzerland)2020
Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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, 1 synthesis or guideline pooled it.
- Meta-analysis of AI posture recognition for work-related musculoskeletal disorders prevention in occupational activities: comparison between untransformed, logit and Freeman-Tukey transformed performance.Frontiers in artificial intelligence · 2026Pooled it
- Designing machine learning-based models to predict residual pain after functional treatment of acute lateral ankle ligamentous sprains.Journal of orthopaedic surgery and research · 2026Article
- Research of Fall Detection and Fall Prevention Technologies: A Review.Sensors (Basel, Switzerland) · 2026Review
- Real-world feasibility of privacy-preserving, non-wearable AI for real-time fall detection with disease-specific video classification in parkinsonian syndromes: a proof-of-concept clinical study.Frontiers in neurology · 2026Article
- Fall detection among elderly persons using FallCNN and transfer learning models.Frontiers in artificial intelligence · 2026Article
- Unlocking opportunities to transform patient care: an expert insight on limitations and opportunities in patient monitoring.Intensive care medicine experimental · 2025Review
- Improving Fall Classification Accuracy of Multi-Input Models Using Three-Axis Accelerometer and Heart Rate Variability Data.Sensors (Basel, Switzerland) · 2025Article
- Detection of Fall Risk in Multiple Sclerosis by Gait Analysis-An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms.Brain sciences · 2022Article
- Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.Sensors (Basel, Switzerland) · 2022Article
- Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.Sensors (Basel, Switzerland) · 2021Article
- A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling.Sensors (Basel, Switzerland) · 2021Article
- Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units.Sensors (Basel, Switzerland) · 2021Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.
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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.