ReviewSensors (Basel, Switzerland)2023
The Methods of Fall Detection: A Literature Review.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Privacy-Preserving Detection of Post-Fall Lying Posture Using a Low-Resolution Infrared Sensor and an Edge FOMO Neural Network.Sensors (Basel, Switzerland) · 2026Article
- Article
- Benchmarking Time-Series Artificial Intelligence Architectures for Wearable Sensor-Based Fall Prediction: A Synthetic Data Simulation Framework.Sensors (Basel, Switzerland) · 2026Article
- Innovative Technologies: Optimizing Nursing Practice.Computers, informatics, nursing : CIN · 2026Article
- Research on fall detection algorithm for older adults in complex lighting environments based on improved YOLOv8n.Frontiers in public health · 2026Article
- Lightweight Attention-Augmented YOLOv5s for Accurate and Real-Time Fall Detection in Elderly Care Environments.Sensors (Basel, Switzerland) · 2025Article
- Differentiating Human Falls from Daily Activities Using Machine Learning Methods Based on Accelerometer and Altimeter Sensor Fusion Feature Engineering.Sensors (Basel, Switzerland) · 2025Article
- AI-based patient monitoring for fall prevention in stroke patients: a pilot study at a Malaysian acute stroke unit.Journal of neuroengineering and rehabilitation · 2025Article
- RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.Sensors (Basel, Switzerland) · 2025Review
- LFD-YOLO: a lightweight fall detection network with enhanced feature extraction and fusion.Scientific reports · 2025Article
- A hybrid human fall detection method based on modified YOLOv8s and AlphaPose.Scientific reports · 2025Article
- An Approach to Fall Detection Using Statistical Distributions of Thermal Signatures Obtained by a Stand-Alone Low-Resolution IR Array Sensor Device.Sensors (Basel, Switzerland) · 2025Article
- Next-generation fall detection: harnessing human pose estimation and transformer technology.Health systems (Basingstoke, England) · 2025Review
- BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- Approaches to Evaluating Digital Health Technologies: Scoping Review.Journal of medical Internet research · 2024Article
- Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Exploring factors affecting the acceptance of fall detection technology among older adults and their families: a content analysis.BMC geriatrics · 2024Article
- Fall Detection Method for Infrared Videos Based on Spatial-Temporal Graph Convolutional Network.Sensors (Basel, Switzerland) · 2024Article
- A Low-Resolution Infrared Array for Unobtrusive Human Activity Recognition That Preserves Privacy.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. This literature review explores the current state of research on FDS and its applications. The review shows various types and strategies of fall detection methods. Each type of fall detection is discussed with its pros and cons. Datasets of fall detection systems are also discussed. Security and privacy issues related to fall detection systems are also considered in the discussion. The review also examines the challenges of fall detection methods. Sensors, algorithms, and validation methods related to fall detection are also talked over. This work found that fall detection research has gradually increased and become popular in the last four decades. The effectiveness and popularity of all strategies are also discussed. The literature review underscores the promising potential of FDS and highlights areas for further research and development.
Indexed as
Identifiers
What OpenQuestion holds
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.