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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 209

Special Issue Editors


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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

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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

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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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Published Papers (1 paper)

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Research

15 pages, 4940 KiB  
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
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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