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Integrated IoT and Sensing in Healthcare System

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Editors

Dr. Bader Alsharif

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Guest Editor

Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: artificial intelligence; deep learning; cybersecurity; intrusion detection; IoT/IoMT; computer vision; healthcare technology

Prof. Dr. Mohammad Ilyas

E-Mail Website
Guest Editor

Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: wireless communication networks; sensor networks and smart systems; healthcare technologies; IoT; performance modeling

Dr. Imadeldin Mahgoub

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Guest Editor

Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: mobile computing and wireless networking; sensor networks; parallel and distributed processing; performance evaluation

Dr. Ali Ibrahim

E-Mail Website
Guest Editor

Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: acoustics; machine learning; signal processing; data mining; computer vision; sparse learning

Dr. Easa Alalwany

E-Mail Website
Guest Editor Assistant

Department of Computer Science, College of Computer Science and Engineering at Yanbu, Taibah University, Medina 46421, Saudi Arabia
Interests: vehicular networks; security; machine learning; IoT; IoMT

Special Issue Information

Dear Colleagues,

The rapid growth of the Internet of Things (IoT) has opened new frontiers in healthcare, enabling continuous patient monitoring, early disease detection, and more personalized treatment strategies. IoT-enabled sensing technologies, from wearable biosensors to smart medical devices, are reshaping how clinical data is collected, transmitted, and analyzed. At the same time, advances in machine learning, edge computing, and big data analytics are making it possible to process massive streams of health data in near real-time. These developments hold particular promise for underserved communities, individuals with disabilities, and aging populations, who stand to benefit from remote and accessible care solutions. However, integrating IoT and sensing into clinical workflows also raises pressing questions around data security, device interoperability, energy efficiency, and patient privacy.

This Special Issue aims to present and disseminate recent advances related to integrated IoT and sensing technologies in healthcare systems. We welcome original research articles, review papers, and case studies addressing the design, deployment, and evaluation of smart health monitoring platforms. Topics of interest for publication include, but are not limited to, the following:

Wearable and implantable biosensors for continuous health monitoring;
IoT architectures and communication protocols for healthcare applications;
Machine learning and deep learning in medical data analytics;
Edge and fog computing for real-time clinical decision support;
Cybersecurity and intrusion detection in Internet of Medical Things (IoMT) networks;
Assistive technologies powered by IoT and computer vision;
Energy-efficient sensor network design for healthcare environments;
Interoperability and standards for connected medical devices;
Privacy-preserving data sharing in smart health systems;
Big data analytics and predictive modeling in patient care.

Dr. Bader Alsharif
Prof. Dr. Mohammad Ilyas
Dr. Imadeldin Mahgoub
Dr. Ali Ibrahim
Guest Editors

Dr. Easa Alalwany
Guest Editor Assistant

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Research

37 pages, 67321 KB

Open AccessArticle

Improving Autism Diagnosis Across Ages Using Eye-Tracking and Temporal Transformer Models

by Mohammed A. AlZain, Mahmoud Rokaya, Dalia I. Hemdan, Ibrahim Gad, Malik Almaliki and Elsayed Atlam

Sensors 2026, 26(16), 5302; https://doi.org/10.3390/s26165302 - 21 Aug 2026

Abstract

Variation in gaze behavior due to age is currently a considerable challenge in building reliable eye-tracking systems for Autism Spectrum Disorder (ASD) diagnosis. However, existing strategies often focus on static gaze representation or dataset-based information, which can lead to limited generalization of findings depending on developmental groups and heterogeneous recording conditions. In this paper, we present a temporal transformer-based system for ASD classification using eye-tracking sequences. This allows you to model gaze behavior as a structured temporal process in the context of contextual attention, as well as employing entropy-based modeling for various distributions of variability over time and temporal consistency constraints to capture sequential gaze dynamics related to ASD behavioral patterns. The framework was evaluated using public eye-tracking corpus containing temporally ordered gaze recordings from ASD and TD participants across age groups. Five sequential experiments on baseline classification, class-balancing analysis, cross-age evaluation, ablation analysis, and cross-dataset transfer learning were performed to conduct experiment-based evaluations. Model performed 0.91 in in-domain Area Under the Receiver Operating Characteristic Curve (AUC) and 0.81 in F1-score on the primary eye-tracking dataset. In the cross-dataset assessment stage, the framework presented a relatively stable performance, with an AUC of 0.85 and an average F1-score of 0.74, irrespective of differences in participant distributions and recording conditions. Ablation analysis also revealed that entropy regularization and temporal consistency mechanisms played a significant role in model stability and classification performance. The ablation analysis provides additional insight into the contribution of the proposed framework components beyond the overall classification performance. Removing the entropy-based regularization reduced the model’s ability to represent variability in gaze allocation, whereas removing the temporal-consistency regularization resulted in less stable sequence representations during learning. These observations indicate that the proposed components complement the transformer-based sequence encoder by improving representation stability and preserving diagnostically relevant temporal information. Rather than acting as independent classifiers, the regularization mechanisms serve as supporting constraints that enhance the quality and robustness of the learned temporal representations. The results indicate that temporally structured gaze modeling is more robust, interpretable, and general in comparison to static gaze representations. In summary, the presented framework can represent a scalable and developmentally appropriate approach to gaze-based ASD classification and support the implementation of trusted neurodevelopmental screening systems. Full article

(This article belongs to the Special Issue Integrated IoT and Sensing in Healthcare System)

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