FALL DETECTION FOR PEOPLE WITH REAL-TIME HEALTH MONITORING USING IOT
Author(s):
ASHA R, Dr.MALATESH S H, SRAVANI KUMARI.M, JAYASHREE.Y.K, SINDHU.P
Keywords:
FALL DETECTION FOR PEOPLE WITH REAL-TIME HEALTH MONITORING USING IOT
Abstract
— Falling is a major health issue that can cause serious injuries and even death, especially in the elderly. Falls are a leading cause of death in people over the age of 75. Computer vision approaches provide a promising and effective solution for detecting human falls. Traditional human detection methods may cause human shape deformation, reducing the performance of fall detection frameworks. This paper presents a solution for fall detection in real- time using Internet of Things (IoT) devices for people with health monitoring needs. The proposed system includes wearable devices equipped with sensors to monitor vital signs such as heart rate, blood pressure, and body temperature, as well as an accelerometer to detect falls. The data collected by these devices is transmitted to a centralized cloud-based platform where machine learning algorithms are used to analyze the data and detect falls. The system is designed to provide real-time alerts to caregivers and emergency services in case of a fall. The proposed solution is intended to improve the safety and well-being of individuals with health monitoring needs, particularly those who are at risk of falling, by providing timely assistance in the event of a fall.
Article Details
Unique Paper ID: 160231

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 221 - 226
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