Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Recent Advances in Wearable and Non-Invasive Sensors
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances in Wearable and Non-Invasive Sensors

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

Deadline for manuscript submissions: 25 May 2026 | Viewed by 1417

Special Issue Editor

Dr. Changhao Xu

E-Mail Website
Guest Editor
Department of Chemical Engineering, Stanford University, Stanford, CA, USA
Interests: machine learning; wearable sensors; medical device; robotics

Special Issue Information

Dear Colleagues,

The field of wearable and non-invasive sensors is rapidly evolving, bringing transformative innovations to healthcare, fitness, and beyond. These cutting-edge technologies enable the continuous monitoring of physiological, biochemical, and environmental parameters, offering real-time insights for personalized health management and lifestyle optimization. From wearable devices like smart patches, rings, and glasses to non-invasive methods such as sweat, saliva, and tear sensing, these advancements are redefining the boundaries of sensor technology. They not only minimize user discomfort but also improve accessibility and usability for widespread applications.

This Special Issue seeks to highlight the latest breakthroughs and applications in wearable and non-invasive sensing technologies. It aims to bring together original research and reviews that address the challenges and opportunities in this multidisciplinary field. Researchers are invited to submit contributions on topics including, but not limited to, the following:

  • Novel sensor materials for enhanced sensitivity and durability;
  • Innovative fabrication techniques for the scalable and cost-effective production of wearable sensors;
  • Non-invasive approaches for detecting critical biomarkers;
  • Advanced device designs for seamless integration with the human body;
  • Energy-efficient systems for prolonged use in wearable applications;
  • Data analytics and machine learning for improved accuracy and predictive capabilities;
  • Applications in healthcare for disease management, rehabilitation, and preventive care;
  • Human-machine interaction systems enabled by wearable or non-invasive technologies.

Dr. Changhao Xu
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

  • wearable sensors
  • non-invasive monitoring
  • personalized healthcare

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 7707 KB  
Article
GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data
by Methun Kamruzzaman, Jorge S. Salinas, Hemanth Kolla, Kenneth L. Sale, Uma Balakrishnan and Kunal Poorey
Sensors 2025, 25(17), 5586; https://doi.org/10.3390/s25175586 - 7 Sep 2025
Viewed by 907
Abstract
Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, [...] Read more.
Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics. Full article
(This article belongs to the Special Issue Recent Advances in Wearable and Non-Invasive Sensors)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop