Detecting Abnormal Health Conditions in Smart Home Using a Drone
- URL: http://arxiv.org/abs/2310.05012v3
- Date: Thu, 11 Jan 2024 02:59:42 GMT
- Title: Detecting Abnormal Health Conditions in Smart Home Using a Drone
- Authors: Pronob Kumar Barman
- Abstract summary: We develop a vision-based fall monitoring system using a drone.
We show that the system can identify falling objects with a precision of 0.9948.
Results demonstrate that the systems can identify falling objects with a precision of 0.9948.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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