SynthesisSensors (Basel, Switzerland)2021
Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review.
Synthesis 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 62 papers, 7 of them syntheses 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
62 citing papers in PubMed, 7 syntheses or guidelines pooled it, 226 citations in OpenAlex.
- Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence.Journal of medical Internet research · 2026Pooled it
- Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis.BMJ open · 2025Pooled it
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.Sensors (Basel, Switzerland) · 2022Pooled it
- A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research.BMC musculoskeletal disorders · 2022Pooled it
- Falls in older adults: a practical approach.Arquivos de neuro-psiquiatria · 2022Pooled it
- How Algorithmic Technologies 'Constitute' the Older Body: A Study of Fall-Detection Wearables.Sociology of health & illness · 2026Article
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone.Sensors (Basel, Switzerland) · 2026Article
- Predicting risk of falling in older adults using supervised machine learning: a comparative analysis of model performance.Zeitschrift fur Gerontologie und Geriatrie · 2026Article
- Are We Missing the Environmental Factors in AI-Based Fall Risk Models?: A Systematic Review.Research square · 2026Article
- An IMU-based dataset of falls, activities of daily living, and prayer movements (AybuFall).BMC research notes · 2026Article
- Dual-Stream deep learning for multimodal feature fusion and classification of balance control in elite freestyle aerial skiers.PloS one · 2026Article
- Interpretable machine learning-based predictive model for fall risk in older adults receiving maintenance hemodialysis.Frontiers in medicine · 2026Article
- A Comparative Overview of Technological Advances in Fall Detection Systems for Elderly People.Sensors (Basel, Switzerland) · 2025Article
- Navigating the future of artificial intelligence technologies for improving the care of older adults.Innovation in aging · 2025Article
- Smart e-Textile Singlet Prototype and Concept: Multi Sensor Sensing for Geriatric Monitoring.Bioengineering (Basel, Switzerland) · 2025Article
- Dynamic fall risk prediction in hospitalized cancer patients: development and validation of a machine learning model using multidimensional clinical data to overcome over-sensitivity in traditional scales.BMC medical informatics and decision making · 2025Article
- The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment.Journal of medical Internet research · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
Indexed as
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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.