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AI-Based Sensing and Analysis on Healthcare Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2744

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Guest Editor
Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea
Interests: Internet of Things; machine learning; data science; big data; edge computing
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Special Issue Information

Dear Colleagues,

Advances in artificial intelligence (AI) have revolutionized various industries, including healthcare. AI-based sensing and analysis technologies have shown immense potential in improving patient care, disease diagnosis, treatment planning, and overall healthcare management. The integration of AI algorithms with sensing technologies has paved the way for innovative solutions that can enhance medical decision-making, optimize resource allocation, and provide personalized healthcare services. This special issue aims to bring together cutting-edge research in the field of AI-based sensing and analysis for healthcare and provide a platform for sharing novel ideas, methodologies, and applications.

This special issue invites original research papers, review articles, and case studies related to AI-based sensing and analysis for healthcare. The topics of interest include, but are not limited to:

  • AI-enabled sensing technologies for disease diagnosis and monitoring
  • AI-powered wearable devices and their applications in healthcare
  • AI-based approaches for drug discovery and personalized medicine
  • AI in remote patient monitoring and telehealth systems
  • Ethical and regulatory considerations in AI-driven healthcare applications
  • AI-based approaches for early disease detection and prevention
  • AI-driven smart healthcare environments and ambient-assisted living
  • AI-driven robotics and automation in healthcare settings
  • Security and privacy challenges in AI-based healthcare solutions
  • AI-based bioinformatics and genomics research

Dr. Shabir Ahmad
Dr. Faisal Jamil
Guest Editors

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Published Papers (1 paper)

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Research

21 pages, 6401 KiB  
Article
mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar
by Zhanjun Hao, Yue Wang, Fenfang Li, Guozhen Ding and Yifei Gao
Sensors 2024, 24(13), 4315; https://doi.org/10.3390/s24134315 - 2 Jul 2024
Cited by 3 | Viewed by 2197
Abstract
Breathing is one of the body’s most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave [...] Read more.
Breathing is one of the body’s most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring. Full article
(This article belongs to the Special Issue AI-Based Sensing and Analysis on Healthcare Applications)
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