Publication: Internet of things (IOT) based elderly fall detection system
Loading...
Date
2024-08
Authors
Eng, Kai Jing
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Falls among elderly individuals present a significant health concern, often resulting in serious and complications. Rapid detection and response are crucial to
mitigate the impact of falls on elderly individuals' health and well-being. This project aims to address this challenge by developing an Internet of Things (IoT) based on a fall detection system tailored for the needs of elderly individuals. The research problem addressed in this project is the necessity for an efficient and reliable fall detection system that can provide timely alerts and assistance to elderly individuals in the event of a fall. To achieve this, an extensive review of existing literature and technologies related to fall detection systems and IoT devices was conducted to identify gaps and opportunities for improvement. Based on the findings, a novel IoT based fall detection system was designed and implemented. The system utilizes wearable sensors equipped with accelerometers to detect fall events accurately. Upon detecting a fall, the system employs wireless communication technology to send immediate alerts to designated caregivers or emergency services, enabling prompt assistance through a designed platform. Furthermore, the fall detection system incorporates online application for real-time analysis of sensor data, enhancing its accuracy in distinguishing between fall and non-fall events. The system's effectiveness was evaluated through rigorous testing, demonstrating its reliability and responsiveness in detecting falls and minimizing false alarms. Real-time data analysis using ThingSpeak is limited by its 15-second update interval, which can miss peak acceleration values during falls. Despite this, the system effectively distinguishes falls from normal activities by analysing synchronized peaks across three axes, ensuring reliable fall detection. The key outcomes of this project include the successful design and implementation of an IoT-based fall detection system tailored for elderly individuals, providing timely and effective assistance in the event of falls. The research
findings highlight the potential of such systems to enhance elderly’s safety and well being, thereby contributing to improved quality of life and reduced healthcare burden.