Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Wearable Devices for Physical Activity and Healthcare 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

Wearable Devices for Physical Activity and Healthcare Monitoring

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2566

Special Issue Editor

Dr. Yehuda Weizman

E-Mail Website
Guest Editor
Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany
Interests: sensors; smart equipment; wearable technology; sports engineering; data analytics product innovation; electronics design; gait analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors have received much attention following the recent technological advances and the arrival of mobile devices. Their proven ability as an essential clinical tool provides unique insights into different physical activity and healthcare monitoring applications, both in day-to-day and controlled environments. These devices have become increasingly capable of collecting accurate data, and have demonstrated computing capabilities, including AI and machine learning. Now, more than ever, wearable technologies provide fertile ground for countless applications in public health, sedentary behaviour, remote clinical monitoring, and digital healthcare.

We welcome research studies, as well as review manuscripts, focusing on the application of wearables for objective recognition, which might include, but is not limited to, the following topics:

New keywords:

  • Physiological disorders;
  • Disability;
  • Orthopaedics;
  • Epidemiology;
  • Public health;
  • Detection;
  • Prevention;
  • Sedentary behaviour;
  • Digital healthcare;
  • Wearable motion and pressure sensors;
  • Textile sensors for human movement

Dr. Yehuda Weizman
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

  • physiological disorders
  • disability
  • orthopaedics
  • epidemiology
  • public health
  • detection
  • prevention
  • sedentary behaviour
  • digital healthcare
  • wearable motion and pressure sensors
  • textile sensors for human movement

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

29 pages, 34115 KiB  
Article
Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation
by Aoyang Bai, Hongyun Song, Yan Wu, Shurong Dong, Gang Feng and Hao Jin
Sensors 2025, 25(4), 1275; https://doi.org/10.3390/s25041275 - 19 Feb 2025
Viewed by 730
Abstract
Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention [...] Read more.
Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention Transformer Network), an advanced neural network specifically tailored for multichannel temporal tasks. SWCTNet integrates IMU and surface electromyography (sEMG) data through sliding window convolution and channel-time attention mechanisms, enabling the efficient extraction of temporal features. This model enables the prediction of muscle activation patterns and kinematics using exclusively IMU data. The experimental results demonstrate that the SWCTNet model achieves recognition accuracies ranging from 87.93% to 91.03% on public temporal datasets and an impressive 98% on self-collected datasets. Additionally, SWCTNet exhibits remarkable precision and stability in generative tasks: the normalized DTW distance was 0.12 for the normal group and 0.25 for the patient group when using the self-collected dataset. This study positions SWCTNet as an advanced tool for extracting musculoskeletal features from IMU data, paving the way for innovative applications in real-time monitoring and personalized rehabilitation at home. This approach demonstrates significant potential for long-term musculoskeletal function monitoring in non-clinical or home settings, advancing the capabilities of IMU-based wearable devices. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
►▼ Show Figures

Figure 1

11 pages, 454 KiB  
Article
Effectiveness of a Telerehabilitation-Based Exercise Program in Patients with Chronic Neck Pain—A Randomized Clinical Trial
by Laura Guerra-Arencibia, Cristina Santana-Déniz, Daniel Pecos-Martín, Samuel Fernández-Carnero, Nerea de Miguel-Hernando, Alexander Achalandabaso-Ochoa and Daniel Rodríguez-Almagro
Sensors 2024, 24(24), 8069; https://doi.org/10.3390/s24248069 - 18 Dec 2024
Viewed by 1498
Abstract
Background: Non-specific chronic neck pain is a prevalent musculoskeletal disorder with a significant impact on individuals’ quality of life. The lack of consensus on effective therapeutic management complicates the establishment of standardized treatment protocols. Home exercise programs have yielded positive results. This study [...] Read more.
Background: Non-specific chronic neck pain is a prevalent musculoskeletal disorder with a significant impact on individuals’ quality of life. The lack of consensus on effective therapeutic management complicates the establishment of standardized treatment protocols. Home exercise programs have yielded positive results. This study aimed to assess the effectiveness of a telerehabilitation program distributed through videoconferencing for patients with non-specific chronic neck pain compared to a home-based exercise program. Methods: A randomized controlled trial was conducted involving 36 participants who were divided into two groups: the experimental group (n = 18) received manual therapy combined with telerehabilitation, while the home-based group (n = 18) received the same manual therapy treatment along with recommendations for home exercises. Key outcome measures, including neck-related disability, kynesiophobia, anxiety and depression, pain intensity, pressure pain threshold, quality of life, and adherence to self-treatment, were evaluated at baseline and post-treatment. Results: No statistically significant differences were observed between groups. However, both groups demonstrated improvements in all study variables except for the mental component of quality of life immediately post-treatment. Conclusions: After eight weeks of manual therapy and exercise, both the telerehabilitation and home-based exercise programs resulted in significant improvements in disability, pain, and kynesiophobia, indicating that telerehabilitation is as effective as home-based exercise. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare 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