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
Flexible Electronics and Wearables for Biomedical, Health and Sport Monitoring
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

Flexible Electronics and Wearables for Biomedical, Health and Sport Monitoring

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

Deadline for manuscript submissions: 15 July 2026 | Viewed by 1880

Special Issue Editor

Dr. Raúl Fernández-García

E-Mail Website
Guest Editor
Department of Electronic Engineering, School of Industrial, Aerospace and Audiovisual Engineering, Polytechnic University of Catalonia, 08222 Terrassa, Spain
Interests: wearable sensors; flexible electronics; sport and heath monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of flexible electronics and wearable technologies is revolutionizing the way biomedical, health, and sport parameters are monitored. Advances in smart materials, flexible electronics, embedded sensors, and wireless communication enable continuous, unobtrusive, and personalized data acquisition in real-world environments. These innovations open new opportunities for preventive healthcare, rehabilitation, chronic disease management, and performance optimization in sport and physical activity.

This Special Issue invites high-quality contributions addressing the design, development, and application of intelligent textiles and wearable systems.

We encourage original research articles, reviews, and case studies that demonstrate novel approaches and impactful applications.

Topics of interest include, but are not limited to, the following:

  • Smart textiles, e-textiles, and flexible sensors for physiological and biomechanical monitoring.
  • Wearable systems for healthcare, rehabilitation, and sport performance.
  • Data fusion, machine learning, and artificial intelligence for multimodal sensor data analysis.
  • User-centered design, ergonomics, and human–device interaction.
  • Energy efficiency, wireless power transfer, and communication protocols.
  • Data security, privacy, and ethical considerations in health and sport monitoring.
  • Case studies, pilot implementations, and clinical or sport validation.

Dr. Raúl Fernández-García
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 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. 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

  • e-textiles
  • flexible electronics
  • biomedical monitoring
  • health monitoring
  • sport performance monitoring
  • physiological sensing
  • machine learning

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 (2 papers)

Download All Papers
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, 1650 KB  
Article
Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality
by O. DelCastillo-Andrés, R. Fernández-García, J. C. Pastor-Vicedo, M. A. Lira, M. C. Campos-Mesa, C. Castañeda-Vázquez, E. Genovesi, S. Krstulović, G. Kuvačić, K. Morvay-Sey and R. Sánchez-Reolid
Sensors 2026, 26(8), 2491; https://doi.org/10.3390/s26082491 - 17 Apr 2026
Viewed by 855
Abstract
Falls are a leading cause of paediatric injuries, yet school-based prevention relies heavily on subjective observation rather than objective biomechanical assessment. This paper introduces the Safe Fall framework, integrating a judo-inspired educational programme with an occlusion-robust computer vision pipeline to quantify safe falling [...] Read more.
Falls are a leading cause of paediatric injuries, yet school-based prevention relies heavily on subjective observation rather than objective biomechanical assessment. This paper introduces the Safe Fall framework, integrating a judo-inspired educational programme with an occlusion-robust computer vision pipeline to quantify safe falling strategies. We analysed video recordings of 285 schoolchildren using a multi-stage architecture combining YOLOv8 for detection, SAM 2 for segmentation, and MMPose for skeletal tracking. The intervention yielded significant improvements in 60% of kinematic metrics (p<0.05), most notably a +61.4% increase in descent rate and expanded rolling ranges, indicating a shift from hazardous “freezing” behaviours to controlled energy dissipation. Unsupervised clustering confirmed a migration of students towards safe motor profiles, while a Random Forest classifier achieved an accuracy of 98.3% and an AUC of 0.998 in distinguishing fall quality. These findings demonstrate that integrating pedagogical training with automated vision modelling provides a scalable and evidence-based approach for reducing injury risk in real-world school environments. Full article
(This article belongs to the Special Issue Flexible Electronics and Wearables for Biomedical, Health and Sport Monitoring)
►▼ Show Figures

Figure 1

13 pages, 601 KB  
Article
Wearable-Based Assessment of Cardiac Recovery After a Modified Bruce Test in Women with Breast Cancer: Role of Physical Activity and Treatment Duration
by Carlos Navarro-Martínez, Natalia Ferrer-Artero, Keven Santamaria-Guzman and José Pino-Ortega
Sensors 2026, 26(6), 1996; https://doi.org/10.3390/s26061996 - 23 Mar 2026
Viewed by 594
Abstract
Heart rate recovery (HRR) is an important indicator of cardiovascular autonomic function, yet evidence in women with breast cancer remains limited. This study aimed to analyze heart rate recovery during the first two minutes following a maximal exercise test and to examine its [...] Read more.
Heart rate recovery (HRR) is an important indicator of cardiovascular autonomic function, yet evidence in women with breast cancer remains limited. This study aimed to analyze heart rate recovery during the first two minutes following a maximal exercise test and to examine its association with age, weekly physical activity, and oncological treatment duration using wearable technology. A cross-sectional design was applied in 22 women with breast cancer enrolled in an oncological exercise program. Participants performed a maximal treadmill test using the Modified Bruce Protocol, after which the mean heart rate was recorded across eight 15 s recovery intervals using a wearable chest-strap heart rate sensor integrated with an inertial device (WIMU PRO). Results showed a progressive and significant decrease in heart rate during recovery, with the first statistically significant pairwise difference emerging at 45–60 s post-exercise compared to the initial recovery interval (p < 0.05), within the context of a continuous HR decline. Regression analysis identified weekly physical activity hours (β = −0.281, p = 0.013) and oncological treatment duration (β = −0.245, p = 0.038) as significant predictors of mean heart rate recovery, explaining 4.8% of the variance, while age was not significantly associated (β = 0.049, p = 0.622). In conclusion, a differentiated recovery pattern emerged at approximately 45–60 s post-exercise, with weekly physical activity and oncological treatment duration as determinants. These findings support the use of wearable-based monitoring to inform individualized exercise prescription in women with breast cancer. Full article
(This article belongs to the Special Issue Flexible Electronics and Wearables for Biomedical, Health and Sport Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop