ElderFallGuard: Real-Time IoT and Computer Vision-Based Fall Detection System for Elderly Safety

• URL: http://arxiv.org/abs/2505.11845v1
• Date: Sat, 17 May 2025 05:09:47 GMT
• Title: ElderFallGuard: Real-Time IoT and Computer Vision-Based Fall Detection System for Elderly Safety
• Authors: Tasrifur Riahi, Md. Azizul Hakim Bappy, Md. Mehedi Islam,
• Abstract summary: ElderFallGuard is a vision-based IoT solution to enhance elderly safety and provide peace of mind for caregivers through intelligent, timely alerts.<n>The system instantly dispatches an alert, including a snapshot of the event, to a designated Telegram group via a custom bot, incorporating logic to prevent notification overload. Rigorous testing on our dataset demonstrated exceptional results, achieving 100% accuracy, precision, recall, and F1-score.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation [1.8637109806158185]
  We propose a framework for detecting falls using several complementary systems.<n>A wearable device and an edge device identify a fall scenario in the first system.<n>A vision-based detection system running on an edge device with a mounted camera on a robot is used to recognize fallen people.
  arXiv  Detail & Related papers  (2025-07-14T16:55:11Z)
• Long-term Detection System for Six Kinds of Abnormal Behavior of the Elderly Living Alone [1.4061979259370274]
  We propose a simulator-based detection system for six typical anomalies: being semi-bedridden, being housebound, forgetting, wandering, fall while walking and fall while standing. Our detection system can be customized for various room layout, sensor forgetting arrangement and resident's characteristics. We propose a method that standardizes the processing of sensor data, and uses a simple detection approach.
  arXiv  Detail & Related papers  (2024-11-20T09:42:08Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• A Multimodal Dangerous State Recognition and Early Warning System for Elderly with Intermittent Dementia [8.299841779645014]
  This system comprises an anti-loss smart helmet, a cloud computing module, and an intelligent early warning application on the caregiver's mobile device. The smart helmet integrates a miniature camera module, a GPS module, and a 5G communication module to collect first-person images and location information of the elderly. In the cloud computing module, our team has proposed for the first time a multimodal dangerous state recognition network based on scene and location information to accurately assess the risk of elderly individuals going missing.
  arXiv  Detail & Related papers  (2024-05-30T15:12:18Z)
• Semi-supervised Open-World Object Detection [74.95267079505145]
  We introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD) We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-02-25T07:12:51Z)
• Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
  Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception. We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
  arXiv  Detail & Related papers  (2023-10-18T11:36:42Z)
• BlockTheFall: Wearable Device-based Fall Detection Framework Powered by Machine Learning and Blockchain for Elderly Care [0.44739156031315913]
  "BlockTheFall," a wearable device-based fall detection framework, detects falls in real time by using sensor data from wearable devices. The collected sensor data is analyzed using machine learning algorithms. The proposed framework stores and verifies fall event data using blockchain technology.
  arXiv  Detail & Related papers  (2023-06-10T14:18:44Z)
• In-vehicle alertness monitoring for older adults [63.359033532099204]
  We present a system for in-vehicle alertness monitoring for older adults. We implemented a prototype traveler monitoring system and evaluated the alertness detection algorithm on ten older adults (70 years and older. This study is the first of its kind for a hitherto under-studied population and has implications for future work on algorithm development and system design through participatory methods.
  arXiv  Detail & Related papers  (2022-08-17T06:07:37Z)
• Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition [11.178325140443446]
  Real-time monitoring and action recognition essential to raise an alert timely when abnormal behaviors or unusual activities occur. We propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology.
  arXiv  Detail & Related papers  (2022-07-21T15:00:54Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• Detecting Invisible People [58.49425715635312]
  We re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects. We demonstrate that current detection and tracking systems perform dramatically worse on this task. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks.
  arXiv  Detail & Related papers  (2020-12-15T16:54:45Z)
• SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience [10.719603033631952]
  Single Shot Human Fall Detector is a deep learning based framework for automatic fall detection from a single image. First, we present a human pose based fall representation which is invariant to appearance characteristics. Second, we present neural network models for 3d pose estimation and fall recognition which are resilient to missing joints due to occluded body parts.
  arXiv  Detail & Related papers  (2020-04-02T03:41:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.