Physical Stint Virtual Representation of Biomedical Signals with Wireless Sensors Using Swarm Intelligence Optimization Algorithm

Many people in the society are facing problems related to health care and the diseases in the body are unable to be identified even with the presence of sensing technologies. The major reason for such failure in identification process is that no virtual technologies are identified in market. Most of...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal Vol. 23; no. 4; p. 1
Main Authors: Shitharth, S., Yonbawi, Saud, Manoharan, Hariprasath, Alahmari, Sultan, Yafoz, Ayman, Mujlid, Hana
Format: Journal Article
Language:English
Published: New York IEEE 15.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Augmented reality
Biomedical sensors
Force
Intelligent sensors
Mathematical models
Medical electronics
Medical services
Optimization
Real time
Real-time systems
Representations
Sensing units
Sensor phenomena and characterization
Sensors
Swarm intelligence
Virtual reality
ISSN:1530-437X, 1558-1748
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many people in the society are facing problems related to health care and the diseases in the body are unable to be identified even with the presence of sensing technologies. The major reason for such failure in identification process is that no virtual technologies are identified in market. Most of the health care solicitations aim to design a particular application that provides information only about sensing values and fail to recall the virtual representation of that represented values. Therefore, this article provides an integration platform that connects the sensing devices with Virtual/ Audio Reality (VR/AR) techniques where it is applied in real time for detecting the presence of infections inside the body. In addition, one type of swarm intelligent algorithm is implemented in the recognition procedure with modified fitness function and it is termed as Fruit Fly Optimization (FFO). The process of FFO provides much low layer perception thus enhancing the output for smooth operation. To examine the real time conditions the projected AR/VR procedure is applied with biomedical sensors where three different case studies are separated. From the comparative numerical results it is pragmatic that proposed method provides better numerical results with 65 percentage full scale representations, less than 0.5 dB of distortion at 0.3 percentage of tuning force.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3233407