Detecting Abnormal Health Conditions in Smart Home Using a Drone

TL;DR

This paper presents a drone-based system utilizing CNNs to detect falls among elderly individuals, achieving high precision in identifying abnormal behaviors for improved health monitoring.

Contribution

It introduces a novel drone-assisted approach with CNN classification for real-time fall detection in smart home environments.

Findings

Fall detection precision of 0.9948

Effective use of drone imaging for health monitoring

Potential for autonomous elderly care systems

Abstract

Nowadays, detecting aberrant health issues is a difficult process. Falling, especially among the elderly, is a severe concern worldwide. Falls can result in deadly consequences, including unconsciousness, internal bleeding, and often times, death. A practical and optimal, smart approach of detecting falling is currently a concern. The use of vision-based fall monitoring is becoming more common among scientists as it enables senior citizens and those with other health conditions to live independently. For tracking, surveillance, and rescue, unmanned aerial vehicles use video or image segmentation and object detection methods. The Tello drone is equipped with a camera and with this device we determined normal and abnormal behaviors among our participants. The autonomous falling objects are classified using a convolutional neural network (CNN) classifier. The results demonstrate that the systems can identify falling objects with a precision of 0.9948.