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State of the Art in Wearable Sensors for Health Monitoring
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State of the Art in Wearable Sensors for Health Monitoring

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 2359

Special Issue Editors

Dr. Yang Liu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 9DF, UK
Interests: mobile health; earable and wearable sensing; signal processing; on-device learning
Dr. Stephen Xia

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208-3109, USA
Interests: cyber–physical systems; edge computing; signal processing; machine learning; data science; wearables; robotics; health
Dr. Ting Dang

E-Mail Website
Guest Editor
School of Computing and Information Systems, University of Melbourne, Carlton, VIC 3052, Australia
Interests: mobile health; audio and speech processing; deep learning; affective computing; time series modeling

Special Issue Information

Dear Colleagues,

The rapid evolution of wearable sensors is revolutionizing health monitoring and personal wellness. By enabling the continuous and real-time tracking of a wide range of health and behavioral metrics; these devices provide valuable insights that can inform preventive measures; guide rehabilitation strategies; and support personalized healthcare. Beyond personal health; they also expand our capacity to understand and respond to environmental factors that affect our overall well-being.

This Special Issue will showcase the state of the art in wearable sensor technologies for health monitoring; highlighting recent advances in sensing materials; device engineering; and data analytics. We welcome contributions that explore novel sensor designs; groundbreaking applications; and multidisciplinary approaches that push the boundaries of wearable technologies and their role in healthcare.

We invite submissions on a variety of topics, including, but not limited to, the following:

  • Next-generation wearable sensors for health monitoring;
  • New sensing materials for health applications;
  • Innovations in physical rehabilitation using wearable devices;
  • Continuous activity tracking and physiological sensing;
  • Wearable solutions for personalized medicine and telehealth;
  • Environmental and lifestyle monitoring for preventive healthcare;
  • Advanced data analytics and machine learning for wearable health;
  • AI-driven innovations in wearable healthcare;
  • Security and privacy in wearable health platforms.

Dr. Yang Liu
Dr. Stephen Xia
Dr. Ting Dang
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

  • wearable sensors
  • health monitoring
  • physical rehabilitation
  • activity tracking
  • physiological sensing
  • sensing materials
  • personalized medicine
  • telehealth
  • artificial intelligence
  • machine learning
  • data analytics
  • security and privacy
  • lifestyle monitoring
  • preventive healthcare

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

Published Papers (3 papers)

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Research

19 pages, 4805 KB  
Article
Comparative Analysis of Passive Movement During Robot-Assisted and Therapist-Led Rehabilitation Exercises
by Iwona Chuchnowska, Jolanta Mikulska, Michał Burkacki, Marta Chmura, Miłosz Chrzan, Jan Kalinowski, Sławomir Suchoń, Marek Ples, Mariusz Sobiech, Piotr Szaflik, Hanna Zadoń and Beniamin Watoła
Sensors 2025, 25(17), 5334; https://doi.org/10.3390/s25175334 - 28 Aug 2025
Viewed by 507
Abstract
The growing number of patients in need of rehabilitation, largely due to an aging population and the increasing incidence of strokes, drives the search for more effective therapeutic methods. Stroke remains a leading cause of adult disability, increasing demand for rehabilitation services. Robotic-assisted [...] Read more.
The growing number of patients in need of rehabilitation, largely due to an aging population and the increasing incidence of strokes, drives the search for more effective therapeutic methods. Stroke remains a leading cause of adult disability, increasing demand for rehabilitation services. Robotic-assisted therapy presents a promising solution by offering precision and repeatability, complementing traditional methods. This study compared traditional rehabilitation led by a physiotherapist with robotic-assisted therapy using the UR10e robot. The research consisted of two stages: in the first, a physiotherapist guided passive upper limb movements, and in the second, the same movements were replicated by the UR10e robot with a specialized adapter for arm positioning. Movements were measured using the Noraxon Ultium Motion system, analyzing flexion, extension, and rotation angles at the shoulder and elbow joints. Full article
(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)
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13 pages, 8086 KB  
Article
Flexible FLIG-Based Temperature Sensor Enabled by Femtosecond Laser Direct Writing for Thermal Monitoring in Health Systems
by Huansheng Wu, Cong Wang, Linpeng Liu and Ji’an Duan
Sensors 2025, 25(15), 4643; https://doi.org/10.3390/s25154643 - 26 Jul 2025
Viewed by 604
Abstract
In this study, a facile and mask-free femtosecond laser direct writing (FLDW) approach is proposed to fabricate porous graphene (FLIG) patterns directly on polyimide (PI) substrates. By systematically adjusting the laser scanning spacing (10–25 μm), denser and more continuous microstructures are obtained, resulting [...] Read more.
In this study, a facile and mask-free femtosecond laser direct writing (FLDW) approach is proposed to fabricate porous graphene (FLIG) patterns directly on polyimide (PI) substrates. By systematically adjusting the laser scanning spacing (10–25 μm), denser and more continuous microstructures are obtained, resulting in significantly enhanced thermal sensitivity. The optimized sensor demonstrated a temperature coefficient of 0.698% °C−1 within the range of 40–120 °C, with response and recovery times of 10.3 s and 20.9 s, respectively. Furthermore, it exhibits remarkable signal stability across multiple thermal cycles, a testament to its reliability in extreme conditions. Moreover, the sensor was successfully integrated into a 3D-printed robotic platform, achieving both contact and non-contact temperature detection. These results underscore the sensor’s practical adaptability for real-time thermal sensing. This work presents a viable and scalable methodology for fabricating high-performance FLIG-based flexible temperature sensors, with extensive application prospects in wearable electronics, electronic skin, and intelligent human–machine interfaces. Full article
(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)
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15 pages, 4940 KB  
Article
Consistency Is Key: A Secondary Analysis of Wearable Motion Sensor Accuracy Measuring Knee Angles Across Activities of Daily Living Before and After Knee Arthroplasty
by Robert C. Marchand, Kelly B. Taylor, Emily C. Kaczynski, Skye Richards, Jayson B. Hutchinson, Shayan Khodabakhsh and Ryan M. Chapman
Sensors 2025, 25(13), 3942; https://doi.org/10.3390/s25133942 - 25 Jun 2025
Viewed by 730
Abstract
Background: Monitoring knee range of motion (ROM) after total knee arthroplasty (TKA) via clinically deployed wearable motion sensors is increasingly common. Prior work from our own lab showed promising results in one wearable motion sensor system; however, we did not investigate errors across [...] Read more.
Background: Monitoring knee range of motion (ROM) after total knee arthroplasty (TKA) via clinically deployed wearable motion sensors is increasingly common. Prior work from our own lab showed promising results in one wearable motion sensor system; however, we did not investigate errors across different activities. Accordingly, herein we conducted secondary analyses of error using wearable inertial measurement units (IMUs) quantifying sagittal knee angles across activities in TKA patients. Methods: After Institutional Review Board (IRB) approval, TKA patients were recruited for participation in two visits (n = 20 enrolled, n = 5 lost to follow-up). Following a sensor tutorial (MotionSense, Stryker, Mahwah, NJ, USA), sensors and motion capture (MOCAP) markers were applied for data capture before surgery. One surgeon then performed TKA. An identical data capture was then completed postoperatively. MOCAP and wearable motion sensor knee angles were computed during a series of activities and compared. Two-way ANOVA evaluated the impact of time (pre- vs. post-TKA) and activity on average error. Another two-way ANOVA was completed, assessing if error at local maxima was different than at local minima and if either was different across activities. Results: Pre-TKA/post-TKA errors were not different. No differences were noted across activities. On average, the errors were under clinically acceptable thresholds (i.e., 4.9 ± 2.6° vs. ≤5°). Conclusions: With average error ≤ 5°, these specific sensors accurately quantify knee angles before/after surgical intervention. Future investigations should explore leveraging this type of technology to evaluate preoperative function decline and postoperative function recovery. Full article
(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)
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