ReviewSensors (Basel, Switzerland)2021
Comprehensive Review of Vision-Based Fall Detection Systems.
Review 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 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Real-Time Vision-Based Fall Detection Systems for the Elderly: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- Fall-from-Bed Risk Prediction Using Physics-Based Bed Simulation.Sensors (Basel, Switzerland) · 2026Article
- Human Fall Detection with Infrared Imaging: A Comparison of Graph Convolutional Networks and YOLO.Sensors (Basel, Switzerland) · 2026Article
- Exploring the Feasibility of Fall Detection Using Bluetooth Low Energy Channel Sounding in Residential Environments.Sensors (Basel, Switzerland) · 2026Article
- Bridging the visual-to-physical gap: physically aligned representations for fall risk analysis.Frontiers in medicine · 2026Article
- Research on fall detection algorithm for older adults in complex lighting environments based on improved YOLOv8n.Frontiers in public health · 2026Article
- A Comparative Overview of Technological Advances in Fall Detection Systems for Elderly People.Sensors (Basel, Switzerland) · 2025Article
- Towards Sensor-Based Mobility Assessment for Older Adults: A Multimodal Framework Integrating PoseNet Gait Dynamics and InBody Composition.Sensors (Basel, Switzerland) · 2025Article
- Multistage fall detection framework via 3D pose sequences and TCN integration.Scientific reports · 2025Article
- Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation.Scientific reports · 2025Article
- Artificial Intelligence in Chronic Disease Management for Aging Populations: A Systematic Review of Machine Learning and NLP Applications.International journal of general medicine · 2025Review
- Next-generation fall detection: harnessing human pose estimation and transformer technology.Health systems (Basingstoke, England) · 2025Review
- CNN-Based Self-Attention Weight Extraction for Fall Event Prediction Using Balance Test Score.Sensors (Basel, Switzerland) · 2023Article
- Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing.Sensors (Basel, Switzerland) · 2023Article
- Imitating Emergencies: Generating Thermal Surveillance Fall Data Using Low-Cost Human-like Dolls.Sensors (Basel, Switzerland) · 2022Article
- Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.Sensors (Basel, Switzerland) · 2021Article
- New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection.Sensors (Basel, Switzerland) · 2021Article
- Survey and Synthesis of State of the Art in Driver Monitoring.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.
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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.