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Feature Papers in Biomedical Sensors 2025
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Feature Papers in Biomedical Sensors 2025

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2209

Special Issue Editors

Prof. Dr. Thomas M. Deserno

E-Mail Website
Guest Editor
Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany
Interests: accident and emergency informatics; continuous health monitoring; smart car; smart home; biomedical image and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhong Chen

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
Interests: nuclear magnetic resonance spectroscopy and imaging; instrument development; signal and image processing

Special Issue Information

Dear Colleagues,

Continuous health monitoring is the key technology to transform our curative medical systems into preventive systems. Avoiding adverse health events (stroke, heart failure, fall, …) and detecting the development of chronic diseases (asthma, diabetes, hypertonia, …) at early stages improves the quality of life (QoL) of patients and saves money in the health systems. The latter is particularly important as our societies become older and older, paired with a growing lack of medical experts.

The World Health Organization (WHO) defines health and wellbeing in six domains: environmental, behavioral, physiological, psychological, social, and spiritual. Including indirect measurements, such as cameras recording heart and respiratory rates, biomedical sensors are available on all these levels. In addition, artificial intelligence is available to analyze automatically the big data recorded by such sensors.

This special issue invites original research and reviews on sensors that support implementing preventive treatments. We cover

  • Sensor technology and evaluation
  • Multimodal health recordings
  • Data recording, synchronization, and fusion
  • Data analytics
  • Medical applications

Prof. Dr. Thomas M. Deserno
Prof. Dr. Zhong Chen
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

  • health monitoring
  • remote monitoring
  • continuous monitoring
  • preventive medicine
  • biological sensors
  • vital signs
  • artificial intelligence
  • machine learning

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

Published Papers (4 papers)

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Research

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11 pages, 1422 KiB  
Article
Towards Precision Nutrition: A Novel Smartphone-Connected Biosensor for Point-of-Care Detection of β-Hydroxybutyrate in Human Blood and Saliva
by Cristina Tortolini, Massimiliano Caprio, Daniele Gianfrilli, Andrea Lenzi and Riccarda Antiochia
Sensors 2025, 25(14), 4336; https://doi.org/10.3390/s25144336 - 11 Jul 2025
Viewed by 242
Abstract
Precision nutrition is an emerging approach that tailors dietary recommendations based on an individual’s unique genetic, metabolic, microbiome, and lifestyle factors. β-hydroxybutyrate (β-HB) is a key ketone body produced during fat metabolism, especially in states of fasting, low-carbohydrate intake, or prolonged exercise. Therefore, [...] Read more.
Precision nutrition is an emerging approach that tailors dietary recommendations based on an individual’s unique genetic, metabolic, microbiome, and lifestyle factors. β-hydroxybutyrate (β-HB) is a key ketone body produced during fat metabolism, especially in states of fasting, low-carbohydrate intake, or prolonged exercise. Therefore, monitoring β-HB levels provides valuable insights into an individual’s metabolic state, making it an essential biomarker for precision and personalized nutrition. A smartphone-connected electrochemical biosensor for single-use, rapid, low-cost, accurate, and selective detection of β-HB in whole blood and saliva at the Point-of-Care (POC) is reported. A graphite screen-printed carbon electrode modified with potassium ferricyanide (Fe(III)GSPE) was used as an electrode platform for the deposition of β-hydroxybutyrate dehydrogenase (HBDH), nicotinamide adenine dinucleotide oxidized form (NAD+), and chitosan nanoparticles (ChitNPs). An outer poly(vinyl) chloride (PVC) diffusion-limiting membrane was used to protect the modified electrode. The biosensor showed a linear range in the clinically relevant range, between 0.4 and 8 mM, with a detection limit (LOD) of 0.1 mM. The biosensor was tested on human blood and saliva samples, and the results were compared to those obtained with a commercial ketone meter, showing excellent agreement. Full article
(This article belongs to the Special Issue Feature Papers in Biomedical Sensors 2025)
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15 pages, 1279 KiB  
Article
A Stability- and Aggregation-Based Method for Heart Rate Estimation Using Photoplethysmographic Signals During Physical Activity
by Sabrina C. Crepaldi, Jiabin Wang, Fumiya Matsumoto, Hiroki Takeuchi, Tatsuhiko Watanabe and Yoshiharu Yamamoto
Sensors 2025, 25(14), 4315; https://doi.org/10.3390/s25144315 - 10 Jul 2025
Viewed by 263
Abstract
In recent years, the use of photoplethysmography (PPG)-based heart rate detection has gained considerable attention as a cost-effective alternative to conventional electrocardiography (ECG) for applications in healthcare and fitness tracking. Although deep learning methods have shown promise in heart rate estimation and motion [...] Read more.
In recent years, the use of photoplethysmography (PPG)-based heart rate detection has gained considerable attention as a cost-effective alternative to conventional electrocardiography (ECG) for applications in healthcare and fitness tracking. Although deep learning methods have shown promise in heart rate estimation and motion artifact removal from PPG signals recorded during physical activity, their computational requirements and need for extensive training data make them less practical for real-world conditions when ground truth data is unavailable for calibration. This study presents a one-size-fits-all approach for heart rate estimation during physical activity that employs aggregation-based techniques to track heart rate and minimize the effects of motion artifacts, without relying on complex machine learning or deep learning techniques. We evaluate our method on four publicly available datasets—PPG-DaLiA, WESAD, IEEE_Training, and IEEE_Test, all recorded using wrist-worn devices—along with a new dataset, UTOKYO, which includes PPG and accelerometer data collected from a smart ring. The proposed method outperforms the CNN ensemble model for the PPG-DaLiA dataset and the IEEE_Test dataset and reduces the mean absolute error (MAE) by 1.45 bpm and 5.71 bpm, respectively, demonstrating that effective signal processing techniques can match the performance of more complex deep learning models without requiring extensive computational resources or dataset-specific tuning. Full article
(This article belongs to the Special Issue Feature Papers in Biomedical Sensors 2025)
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15 pages, 2659 KiB  
Article
Wearable Spine Tracker vs. Video-Based Pose Estimation for Human Activity Recognition
by Jonas Walkling, Luca Sander, Arwed Masch and Thomas M. Deserno
Sensors 2025, 25(12), 3806; https://doi.org/10.3390/s25123806 - 18 Jun 2025
Viewed by 530
Abstract
This paper presents a comparative study for detecting the activities of daily living (ADLs) using two distinct sensor systems: the FlexTail wearable spine tracker and a camera-based pose estimation model. We developed a protocol to simultaneously record data with both systems and capture [...] Read more.
This paper presents a comparative study for detecting the activities of daily living (ADLs) using two distinct sensor systems: the FlexTail wearable spine tracker and a camera-based pose estimation model. We developed a protocol to simultaneously record data with both systems and capture eleven activities from general movement, household, and food handling. We tested a comprehensive selection of state-of-the-art time series classification algorithms. Both systems achieved high classification performance, with average F1 scores of 0.90 for both datasets using a 1-second time window and the random dilated shapelet transform (RDST) and QUANT classifier for FlexTail and camera data, respectively. We also explored the impact of hierarchical activity grouping and found that while it improved classification performance in some cases, the benefits were not consistent across all activities. Our findings suggest that both sensor systems recognize ADLs. The FlexTail model performs better for detecting sitting and transitions, like standing up, while the camera-based model is better for activities that involve arm and hand movements. Full article
(This article belongs to the Special Issue Feature Papers in Biomedical Sensors 2025)
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Review

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17 pages, 874 KiB  
Review
A Comprehensive Survey of Research Trends in mmWave Technologies for Medical Applications
by Xiaoyu Zhang, Chuhui Liu, Yanda Cheng, Zhengxiong Li, Chenhan Xu, Chuqin Huang, Ye Zhan, Wei Bo, Jun Xia and Wenyao Xu
Sensors 2025, 25(12), 3706; https://doi.org/10.3390/s25123706 - 13 Jun 2025
Viewed by 769
Abstract
Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave [...] Read more.
Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration. Full article
(This article belongs to the Special Issue Feature Papers in Biomedical Sensors 2025)
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