ElderFallGuard: Real-Time IoT and Computer Vision-Based Fall Detection System for Elderly Safety
Tasrifur Riahi, Md. Azizul Hakim Bappy, Md. Mehedi Islam

TL;DR
ElderFallGuard is a real-time, non-invasive fall detection system for the elderly that uses computer vision and IoT to accurately identify falls and notify caregivers instantly, enhancing safety and independence.
Contribution
The paper introduces a novel vision-based IoT system utilizing MediaPipe and machine learning for accurate, real-time fall detection with a custom dataset and alert mechanism.
Findings
Achieved 100% accuracy, precision, recall, and F1-score on the dataset.
Developed a custom fall detection logic based on pose and motion analysis.
Implemented instant alerts with snapshot sharing via Telegram.
Abstract
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…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Human Pose and Action Recognition · Balance, Gait, and Falls Prevention
