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Advancing Healthcare: Integrating AI and Smart Sensing Technologies
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Advancing Healthcare: Integrating AI and Smart Sensing Technologies

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

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 7558

Special Issue Editor

Prof. Dr. Kyungtae Kang

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
Interests: interdisciplinary area of cyber-physical systems; medical AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in Artificial Intelligence (AI) and smart sensing technologies have ushered in a new era in healthcare. These technologies offer unprecedented opportunities for enhancing patient care, improving diagnostic accuracy, and enabling the development of more personalized treatment approaches. This Special Issue aims to explore the diverse applications of AI and smart sensing in healthcare, highlighting innovative research and developments that leverage these technologies to address current healthcare challenges. The intersection of AI and smart sensors represents a burgeoning field that holds the potential to transform healthcare delivery, patient monitoring, and medical diagnostics.

Prof. Dr. Kyungtae Kang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in healthcare
  • biomedical signal processing
  • medical diagnostics AI
  • predictive disease models
  • medical diagnostics

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Published Papers (2 papers)

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Research

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19 pages, 3957 KiB  
Article
Enhanced Human Activity Recognition Using Wi-Fi Sensing: Leveraging Phase and Amplitude with Attention Mechanisms
by Thai Duy Quy, Chih-Yang Lin and Timothy K. Shih
Sensors 2025, 25(4), 1038; https://doi.org/10.3390/s25041038 - 9 Feb 2025
Viewed by 1709
Abstract
Wi-Fi-based human activity recognition (HAR) is a non-intrusive and privacy-preserving method that leverages Channel State Information (CSI) for identifying human activities. However, existing approaches often struggle with robust feature extraction, especially in dynamic and multi-environment scenarios, and fail to effectively integrate amplitude and [...] Read more.
Wi-Fi-based human activity recognition (HAR) is a non-intrusive and privacy-preserving method that leverages Channel State Information (CSI) for identifying human activities. However, existing approaches often struggle with robust feature extraction, especially in dynamic and multi-environment scenarios, and fail to effectively integrate amplitude and phase features of CSI. This study proposes a novel model, the Phase–Amplitude Channel State Information Network (PA-CSI), to address these challenges. The model introduces two key innovations: (1) a dual-feature approach combining amplitude and phase features for enhanced robustness, and (2) an attention-enhanced feature fusion mechanism incorporating multi-scale convolutional layers and Gated Residual Networks (GRN) to optimize feature extraction. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on three datasets, including StanWiFi (99.9%), MultiEnv (98.0%), and the MINE lab dataset (99.9%). These findings underscore the potential of the PA-CSI model to advance Wi-Fi-based HAR in real-world applications. Full article
(This article belongs to the Special Issue Advancing Healthcare: Integrating AI and Smart Sensing Technologies)
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Review

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30 pages, 2112 KiB  
Review
Current Diagnostic Techniques for Pneumonia: A Scoping Review
by Kehkashan Kanwal, Muhammad Asif, Syed Ghufran Khalid, Haipeng Liu, Aisha Ghazal Qurashi and Saad Abdullah
Sensors 2024, 24(13), 4291; https://doi.org/10.3390/s24134291 - 1 Jul 2024
Cited by 3 | Viewed by 5396
Abstract
Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic [...] Read more.
Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients. Full article
(This article belongs to the Special Issue Advancing Healthcare: Integrating AI and Smart Sensing Technologies)
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