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Integrating Smart Sensors and Artificial Intelligence Technologies for Human Healthcare Applications

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 898

Special Issue Editors

Dr. Basheer Qolomany

E-Mail Website
Guest Editor
School of Information Technology, University of Cincinnati, Cincinnati, OH 45221, USA
Interests: network science; evolutionary computation; and artificial intelligence; including natural language processing; machine learning; deep learning; graph representation learning; swarm intelligence and big data analytics in support of smart services; cybersecurity; population health
Special Issues, Collections and Topics in MDPI journals
Dr. Jacques Bou Abdo

E-Mail Website
Guest Editor
School of Information Technology, University of Cincinnati, Cincinnati, OH 45221, USA
Interests: cyber and strategic deterrence; flow of information and disinformation in irregular warfare; flow of cyberattacks and network resiliency in cyber warfare; flow of infectious diseases in biological warfare and resilience of supply chains
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liaquat Hossain

E-Mail Website
Guest Editor
School of Computing, Montclair State University, Montclair, NJ 07043, USA
Interests: disasters; disaster medicine; social medicine; resilient systems; biosecurity

Special Issue Information

Dear Colleagues,

This Special Issue, titled ‘Integrating Smart Sensors and Artificial Intelligence Technologies for Human Healthcare Applications’, seeks to explore the synergistic integration of the latest smart sensor systems and artificial intelligence (AI) technologies to modernize human healthcare. This Special Issue will focus on innovative research that leverages smart sensors to collect real-time health data and employ AI techniques such as machine learning, deep learning, natural language processing, predictive analytics and computer vision to classify and process health information to achieve early diagnosis and treatment. We welcome contributions to innovative approaches related to all aspects of smart healthcare for implementing these technologies in early disease detection and diagnosis, early intervention, personalized healthcare, disease prediction, human-centric disease management, and telemedicine and home healthcare. In addition, studies addressing the integration of AI with wearable sensors, internet-based health monitoring smart systems, healthcare robotics, and the inclusion of robots in the clinical and health sciences for enhancing health professionals’ decision-making and patient outcomes are also invited. Contributions can be original research, review articles, or case studies that emphasize the key roles of AI-driven sensors in improving the efficiency of healthcare services, their accuracy in determining anomalous physiological events, and the reduction in burden on clinical analysis findings.

Dr. Basheer Qolomany
Dr. Jacques Bou Abdo
Prof. Dr. Liaquat Hossain
Guest Editors

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

  • smart sensors in healthcare
  • artificial intelligence in healthcare
  • predictive healthcare analytics
  • wearable health technologies
  • remote patient monitoring
  • personalized medicine

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 4276 KiB  
Article
Robust Estimation of Unsteady Beat-to-Beat Systolic Blood Pressure Trends Using Photoplethysmography Contextual Cycles
by Xinyi Huang, Xianbin Zhang, Richard Millham, Lin Xu and Wanqing Wu
Sensors 2025, 25(12), 3625; https://doi.org/10.3390/s25123625 (registering DOI) - 9 Jun 2025
Abstract
Hypertension and blood pressure variability (BPV) are major risk factors for cardiovascular disease (CVD). Single-channel photoplethysmography (PPG) has emerged as a promising daily blood pressure (BP) monitoring tool. However, estimating BP trends presents challenges due to complex temporal dependencies and continuous fluctuations. Traditional [...] Read more.
Hypertension and blood pressure variability (BPV) are major risk factors for cardiovascular disease (CVD). Single-channel photoplethysmography (PPG) has emerged as a promising daily blood pressure (BP) monitoring tool. However, estimating BP trends presents challenges due to complex temporal dependencies and continuous fluctuations. Traditional methods often address BP prediction as isolated tasks and focus solely on temporal dependencies within a limited time window, which may fall short of capturing the intricate BP fluctuation patterns implied in varying time spans, particularly amidst constant BP variations. To address this, we propose a novel deep learning model featuring a two-stage architecture and a new input structure called contextual cycles. This model estimates beat-to-beat systolic blood pressure (SBP) trends as a sequence prediction task, transforming the output from a single SBP value into a sequence. In the first stage, parallel ResU Blocks are utilized to extract fine-grained features from each cycle. The generated feature vectors are then processed by Transformer layers with relative position encoding (RPE) to capture inter-cycle interactions and temporal dependencies in the second stage. Our proposed model demonstrates robust performance in beat-to-beat SBP trend estimation, achieving a mean absolute error (MAE) of 3.186 mmHg, a Pearson correlation coefficient applied to sequences (Rseq) of 0.743, and a variability error (VE) of 1.199 mmHg. It excels in steady and abrupt substantial fluctuation states, outperforming baseline models. The results reveal that our method meets the requirements of the AAMI standard and achieves grade A according to the BHS standard. Overall, our proposed method shows significant potential for reliable daily health monitoring. Full article
(This article belongs to the Special Issue Integrating Smart Sensors and Artificial Intelligence Technologies for Human Healthcare Applications)
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18 pages, 1379 KiB  
Article
An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT
by Jiaxuan Wu, Yuxin Lu and Yueqiu Jiang
Sensors 2025, 25(7), 2299; https://doi.org/10.3390/s25072299 - 4 Apr 2025
Viewed by 350
Abstract
With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates [...] Read more.
With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone. Full article
(This article belongs to the Special Issue Integrating Smart Sensors and Artificial Intelligence Technologies for Human Healthcare Applications)
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