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Sensors
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Wearable Medical Sensors and AI-Driven Signal Processing for Next-Generation Healthcare
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Wearable Medical Sensors and AI-Driven Signal Processing for Next-Generation Healthcare

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 610

Special Issue Editor

Dr. Wanqing Wu

E-Mail Website
Guest Editor
School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 528406, China
Interests: wearable biosensors; biosignal processing; pattern recognition; multimodal electrophysiological data fusion; AI-based health informatics; medical decision-making system; biomedical applications; smart medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of wearable medical sensors with artificial intelligence (AI)-driven signal processing technologies is revolutionizing next-generation healthcare. Recent advances in flexible electronics, biosensors, and edge computing have enabled real-time, continuous, and non-invasive monitoring of diverse physiological parameters. These innovations are transforming traditional healthcare models from reactive treatment to proactive, personalized, and preventive care.

This Special Issue aims to showcase cutting-edge research and developments in wearable sensing technologies and intelligent signal processing algorithms that facilitate accurate health status assessment, early disease detection, and remote patient monitoring. We welcome original research articles, reviews, and case studies that address key challenges in sensor design, multimodal data acquisition, AI-based interpretation, and system-level integration in clinical or daily life scenarios.

Topics of interest include, but are not limited to, novel wearable biosensors, AI algorithms for physiological signal analysis, edge/cloud health monitoring systems, real-world deployment of wearable devices, and their clinical applications in chronic disease management, mental health assessment, rehabilitation, and more.

We invite contributions from researchers, clinicians, and engineers working at the intersection of biomedical sensing, signal processing, machine learning, and digital health.

Dr. Wanqing Wu
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

  • smart biosensors
  • physiological monitoring
  • health informatics
  • edge computing in healthcare
  • multimodal biomedical data
  • digital health technologies
  • chronic disease management

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Published Papers (1 paper)

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Research

22 pages, 4786 KB  
Article
Multi-Signal Acquisition System for Continuous Blood Pressure Monitoring
by Naiwen Zhang, Yu Zhang, Jintao Chen, Shaoxuan Qiu, Jinting Ma, Lihai Tan and Guo Dan
Sensors 2025, 25(18), 5910; https://doi.org/10.3390/s25185910 - 21 Sep 2025
Viewed by 294
Abstract
Continuous blood pressure (BP) monitoring is essential for the early detection and prevention of cardiovascular diseases like hypertension. Recently, interest in continuous BP estimation systems and algorithms has grown. Various physiological signals reflect BP variations from different perspectives, and combining multiple signals can [...] Read more.
Continuous blood pressure (BP) monitoring is essential for the early detection and prevention of cardiovascular diseases like hypertension. Recently, interest in continuous BP estimation systems and algorithms has grown. Various physiological signals reflect BP variations from different perspectives, and combining multiple signals can enhance the accuracy of BP measurements. However, research integrating electrocardiogram (ECG), photoplethysmography (PPG), and impedance cardiography (ICG) signals for BP monitoring remains limited, with related technologies still in early development. A major challenge is the increased system complexity associated with acquiring multiple signals simultaneously, along with the difficulty of efficiently extracting and integrating key features for accurate BP estimation. To address this, we developed a BP monitoring system that can synchronously acquire and process ECG, PPG, and ICG signals. Optimizing the circuit design allowed ECG and ICG modules to share electrodes, reducing components and improving compactness. Using this system, we collected 400 min of signals from 40 healthy subjects, yielding 4390 records. Experiments were conducted to evaluate the system’s performance in BP estimation. The results demonstrated that combining pulse wave analysis features with the XGBoost model yielded the most accurate BP predictions. Specifically, the mean absolute error for systolic blood pressure was 3.76 ± 3.98 mmHg, and for diastolic blood pressure, it was 2.71 ± 2.57 mmHg, both of which achieved grade A performance under the BHS standard. These results are comparable to or better than existing studies based on multi-signal methods. These findings suggest that the proposed system offers an efficient and practical solution for BP monitoring. Full article
(This article belongs to the Special Issue Wearable Medical Sensors and AI-Driven Signal Processing for Next-Generation Healthcare)
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