2025/05/17 by Tasrifur Riahi, Riahi, Tasrifur, Md. Azizul Hakim Bappy +3
Computer Science · Health Professions · #Balance, Gait, and Falls Prevention #Computer Vision and Pattern Recognition (cs.CV) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human Pose and Action Recognition
paper · pdf · doi:10.48550/arxiv.2505.11845
openalex publication_date 2025/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For the elderly population, falls pose a serious and increasing risk of serious injury and loss of independence. In order to overcome this difficulty, we present ElderFallGuard: A Computer Vision Based IoT Solution for Elderly Fall Detection and Notification, a cutting-edge, non-invasive system intended for quick caregiver alerts and real-time fall detection. Our approach leverages the power of computer vision, utilizing MediaPipe for accurate human pose estimation from standard video streams. We developed a custom dataset comprising 7200 samples across 12 distinct human poses to train and evaluate various machine learning classifiers, with Random Forest ultimately selected for its superior performance. ElderFallGuard employs a specific detection logic, identifying a fall when a designated prone pose ("Pose6") is held for over 3 seconds coupled with a significant drop in motion detected for more than 2 seconds. Upon confirmation, the system instantly dispatches an alert, including a snapshot of the event, to a designated Telegram group via a custom bot, incorporating cooldown logic to prevent notification overload. Rigorous testing on our dataset demonstrated exceptional results, achieving 100% accuracy, precision, recall, and F1-score. ElderFallGuard offers a promising, vision-based IoT solution to enhance elderly safety and provide peace of mind for caregivers through intelligent, timely alerts.