ArticleSensors (Basel, Switzerland)2021
Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 31 citations in OpenAlex.
- 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 and pre-impact prediction technologies in older adults: a scoping review of translational maturity and public health integration.Frontiers in public health · 2026Article
- The Impact of the Accelerometer Sampling Rate on the Performance of Machine and Deep Learning Models in Wearable Fall-Detection Systems.Sensors (Basel, Switzerland) · 2025Article
- Comparative Study and Real-World Validation of Vertical Load Estimation Techniques for Intelligent Tire Systems.Sensors (Basel, Switzerland) · 2025Article
- Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults.Sensors (Basel, Switzerland) · 2024Article
- An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.Journal of medical Internet research · 2024Article
- CNN-Based Self-Attention Weight Extraction for Fall Event Prediction Using Balance Test Score.Sensors (Basel, Switzerland) · 2023Article
- Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method.Sensors (Basel, Switzerland) · 2023Article
- Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.
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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.