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Electronics
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Smart Devices and Wearable Sensors: Recent Advances and Prospects
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Smart Devices and Wearable Sensors: Recent Advances and Prospects

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 1830

Special Issue Editors

Dr. Iqram Hussain

E-Mail Website
Guest Editor
Weill Cornell Medicine, Cornell University, New York City, NY 10065, USA
Interests: large language models; computational physiology; biomedical sensors; health informatics; medical physics
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Di Rienzo

E-Mail Website
Guest Editor
Information Engineering Department, University of Pisa, Pisa, Italy
Interests: computer science; wearable sensors; mobile health
Dr. Jahowa Islam

E-Mail Website
Guest Editor
Electrochemical Materials & Devices Laboratory, The City University of New York, New York, NY 10007, USA
Interests: nanomaterials; energy efficiency; electrocatalyst
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing advancement of wearable sensors and smart devices is reshaping modern healthcare, human activity monitoring, and personal wellness through increasingly sophisticated, miniaturized, and connected technologies. These devices, often integrated within the broader Internet of Things (IoT) ecosystem, enable seamless acquisition and transmission of biosignals and contextual data in real-time. Ranging from heart rate and electrodermal activity to motion tracking and environmental sensing, wearable systems are capable of capturing high-resolution, multimodal data that underpin novel insights into human physiology and behavior.

Mobile health (mHealth) applications are benefiting significantly from these technological developments, providing scalable, personalized, and often remote solutions for health monitoring and disease management. Remote monitoring capabilities—ranging from post-operative rehabilitation and chronic disease surveillance to eldercare and mental health tracking—are facilitating proactive and preventive healthcare models. These solutions are particularly critical in underserved or geographically isolated communities, where access to traditional healthcare services is limited.

Human activity recognition (HAR), powered by embedded inertial sensors and enhanced through artificial intelligence (AI) techniques, is enabling precise classification of daily activities and detection of anomalies related to health conditions, fatigue, or performance decline. Machine learning and deep learning algorithms are increasingly integrated within wearable systems for tasks such as biosignal analysis, early diagnosis, and behavioral modeling, driving forward the vision of intelligent, adaptive, and context-aware health technologies.

Personalized medicine stands to benefit enormously from smart sensing platforms, as continuous and individualized data streams enable tailored interventions and adaptive therapeutic strategies. Sensor fusion techniques, combining data from multiple sources (e.g., accelerometers, ECG, PPG, and GPS), are essential for improving the reliability, accuracy, and robustness of health-related insights.

Despite these promising advances, several challenges remain. Ensuring data privacy and security, optimizing energy efficiency for prolonged device autonomy, enhancing user compliance and wearability, and validating the clinical reliability of sensor-based assessments are crucial areas of ongoing research. Interdisciplinary collaboration—bridging engineering, computer science, medicine, and behavioral sciences—is essential to address these multifaceted issues.

This Special Issue aims to serve as a comprehensive platform for disseminating the latest research and developments in fields of wearable sensors and smart devices, with a focus on their integration into real-world healthcare and monitoring systems. We welcome contributions that highlight technological innovations, novel applications, algorithmic approaches, and cross-sector collaborations, with the ultimate goal of advancing the field toward more intelligent, accessible, and impactful solutions in health and human performance.

We look forward to your contributions.

Dr. Iqram Hussain
Dr. Francesco Di Rienzo
Dr. Jahowa Islam
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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 2400 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

  • wearable sensors
  • smart devices
  • mobile health
  • remote monitoring
  • Internet of Things (IoT)
  • human activity recognition
  • biosignal acquisition
  • artificial intelligence in healthcare
  • personalized medicine
  • sensor fusion

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Published Papers (2 papers)

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21 pages, 1407 KB  
Article
PrevOccupAI-HAR: A Public Domain Dataset for Smartphone Sensor-Based Human Activity Recognition in Office Environments
by Phillip Probst, Sara Santos, Gonçalo Barros, Philipp Koch, Ricardo Vigário and Hugo Gamboa
Electronics 2026, 15(4), 807; https://doi.org/10.3390/electronics15040807 - 13 Feb 2026
Cited by 1 | Viewed by 762
Abstract
This article presents PrevOccupAI-HAR, a new publicly available dataset designed to advance smartphone-based human activity recognition (HAR) in office environments. PrevOccupAI-HAR comprises two sub-datasets: (1) a model development dataset collected under controlled conditions, featuring 20 subjects performing nine sub-activities associated to three main [...] Read more.
This article presents PrevOccupAI-HAR, a new publicly available dataset designed to advance smartphone-based human activity recognition (HAR) in office environments. PrevOccupAI-HAR comprises two sub-datasets: (1) a model development dataset collected under controlled conditions, featuring 20 subjects performing nine sub-activities associated to three main activity classes (sitting, standing, and walking), and (2) a real-world dataset captured in an unconstrained office setting captured from 13 subjects carrying out their daily office work for six hours continuously. Three machine learning models—namely, k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF)—were trained on the model development dataset to classify the three main classes independently of sub-activity variation. The KNN, SVM, and RF models achieved accuracies of 90.94%, 92.33%, and 93.02%, respectively, on the development dataset. When deployed on the real-world dataset, the models attained mean accuracies of 69.32%, 79.43%, and 77.81%, reflecting performance degradations between 21.62% and 12.90%. Analysis of sequential predictions revealed frequent short-duration misclassifications, predominantly between sitting and standing, resulting in unstable model outputs. The findings highlight key challenges in transitioning HAR models from controlled to real-world contexts and point to future research directions involving temporal deep learning architectures or post-processing methods to enhance prediction consistency. Full article
(This article belongs to the Special Issue Smart Devices and Wearable Sensors: Recent Advances and Prospects)
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Review

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25 pages, 1601 KB  
Review
Applications of Heart Rate Variability Metrics in Wearable Sensor Technologies: A Comprehensive Review
by Emi Yuda
Electronics 2026, 15(8), 1707; https://doi.org/10.3390/electronics15081707 - 17 Apr 2026
Viewed by 701
Abstract
Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes [...] Read more.
Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article
(This article belongs to the Special Issue Smart Devices and Wearable Sensors: Recent Advances and Prospects)
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