ReviewHealth systems (Basingstoke, England)2025
Next-generation fall detection: harnessing human pose estimation and transformer technology.
Review in Health systems (Basingstoke, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Accelerator-Free Thermal Pose Estimation: Accuracy-Efficiency Trade-Offs on the Raspberry Pi 5.Sensors (Basel, Switzerland) · 2026Article
- Walking stability prediction for pedestrians using gait energy images and hybrid deep and few-shot learning models.Scientific reports · 2026Article
- Benchmarking Time-Series Artificial Intelligence Architectures for Wearable Sensor-Based Fall Prediction: A Synthetic Data Simulation Framework.Sensors (Basel, Switzerland) · 2026Article
- Efficient Thermal Pose Estimation: Balancing Accuracy and Edge Deployment for Smart Home Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- Privacy, Security & Governance Frameworks for AI-Powered Wearable Internet of Health Things in Elderly Care: A Comprehensive Review.Risk management and healthcare policy · 2026Review
- Fall detection among elderly persons using FallCNN and transfer learning models.Frontiers in artificial intelligence · 2026Article
- Lightweight Attention-Augmented YOLOv5s for Accurate and Real-Time Fall Detection in Elderly Care Environments.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Elderly falls are occurring at an alarming rate, with significant health risks for seniors. Current fall detection systems often lack accuracy, efficacy, and privacy considerations. This study examines three leading human pose estimation frameworks combined with transformer deep learning models to develop a lightweight, privacy-preserving fall detection system. Key features include: 1) It runs on low-power devices like Raspberry Pis; 2) It monitors seniors passively, without requiring active participation; 3) It can be deployed in any residential or senior care setting; 4) It does not rely on wearables; and 5) All processing occurs locally, ensuring privacy with only fall alerts transmitted to caregivers. In real-world tests, the model achieved 95.24% sensitivity, 89.80% specificity, 98.00% accuracy, a 90.91% F1 score, and 95.24% precision, highlighting its effectiveness in detecting falls among the elderly while maintaining privacy and security.
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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.