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The Sensing Technologies and Computational Methods for Biomedical Engineering: Next-Generation Perspectives

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 497

Special Issue Editors

Dr. Pedro Morais

E-Mail Website
Guest Editor
2Ai-School of Technology, IPCA, 4750-810 Barcelos, Portugal
Interests: biomedical applications; medical sensors; medical imaging; artificial intelligence and robotics
Special Issues, Collections and Topics in MDPI journals
Dr. João Vilaça

E-Mail Website
Guest Editor
2Ai-School of Technology, IPCA, 4750-810 Barcelos, Portugal
Interests: smart interventions; personalized health and care; biomedical imaging; natural user interfaces; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Helena R. Torres

E-Mail Website
Guest Editor
2Ai-School of Technology, IPCA, 4750-810 Barcelos, Portugal
Interests: biomedical applications; artificial intelligence; medical imaging; medical electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing technologies and computational methods are currently revolutionizing the field of biomedical engineering by enabling the development of innovative diagnostic and therapeutic solutions. As a result, research on new wearable sensors, imaging technologies and image-based diagnostic software, as well as the simulation and prediction of physiological mechanisms, among other things, has been conducted. In addition, following the fast growth of AI, specific attention has been paid to machine learning and deep learning methods to process clinical data (e.g., medical images and physiological biosignals). The aim of this Special Issue is to present the current developments in sensing technologies and computational methods with an impact on biomedical engineering, particularly through medically validated applications. New prototypes, specific and practical proof-of-concept examples as well as exhaustive validation studies and reviews are of particular interest.

Dr. Pedro Morais
Dr. João Vilaça
Dr. Helena R. Torres
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
  • medical imaging sensors
  • AI-driven biosignal processing
  • multimodal biomedical sensor fusion
  • sensor-based physiological modeling

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Published Papers (1 paper)

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Research

25 pages, 1716 KB  
Article
Comparison of Wearable and Depth-Sensing Technologies with Electronic Walkway for Comprehensive Gait Analysis
by Marjan Nassajpour, Mahmoud Seifallahi, Amie Rosenfeld, Magdalena I. Tolea, James E. Galvin and Behnaz Ghoraani
Sensors 2025, 25(17), 5501; https://doi.org/10.3390/s25175501 - 4 Sep 2025
Viewed by 376
Abstract
Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial [...] Read more.
Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies—APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera—against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera’s field of view. Gait data from 20 older adults (mean age 70.06±9.45 years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland–Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE =0.00–6.12, r=0.92–1.00), followed closely by the Azure Kinect (MAE =0.01–6.07, r=0.68–0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations. Full article
(This article belongs to the Special Issue The Sensing Technologies and Computational Methods for Biomedical Engineering: Next-Generation Perspectives)
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