ArticleSensors (Basel, Switzerland)2017
An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Fall Detection in Elderly People: A Systematic Review of Ambient Assisted Living and Smart Home-Related Technology Performance.Sensors (Basel, Switzerland) · 2025Pooled it
- Human fall simulation testing method: where we are.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Pooled it
- Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- An investigation into the acceptance of intelligent care systems: an extended technology acceptance model (TAM).Scientific reports · 2025Article
- Next-generation fall detection: harnessing human pose estimation and transformer technology.Health systems (Basingstoke, England) · 2025Review
- PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers.Sensors (Basel, Switzerland) · 2023Article
- Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT.Sensors (Basel, Switzerland) · 2023Article
- Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes.Sensors (Basel, Switzerland) · 2022Article
- Application of Accelerometer to Monitor Students' Exercise Load in 50 m Round Trip.Computational intelligence and neuroscience · 2022Article
- Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.Sensors (Basel, Switzerland) · 2021Article
- The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.Sensors (Basel, Switzerland) · 2021Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor.Sensors (Basel, Switzerland) · 2021Article
- Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring.Sensors (Basel, Switzerland) · 2020Article
- Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.Sensors (Basel, Switzerland) · 2020Article
- Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method.Sensors (Basel, Switzerland) · 2020Article
- A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets.Sensors (Basel, Switzerland) · 2020Article
- Elderly Fall Detection Systems: A Literature Survey.Frontiers in robotics and AI · 2020Review
- A Survey on Recent Advances in Wearable Fall Detection Systems.BioMed research international · 2020Review
- Robust Self-Adaptation Fall-Detection System Based on Camera Height.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART),
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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.