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

Deadline for manuscript submissions: 5 November 2026 | Viewed by 11923

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Special Issue Editors

Dr. Ye Tian

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Guest Editor

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
Interests: wearable or implantable devices; health management; wearable photoacoustic probe; electronics-free wireless strain sensors

Prof. Dr. Emiliano Schena

E-Mail Website
Guest Editor

Laboratory of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: physiological monitoring; wearable systems; wearable sensors; physiological measurements; active living; cardiorespiratory monitoring; soft sensors
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Panicos Kyriacou

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Guest Editor

Biomedical Engineering Research Centre, School of Science and Technology, City St George’s, University of London, Northampton Square, London EC1V 0HB, UK
Interests: tissue optics; chemometrics; optical sensors; wearable devices; photoplethysmography; pulse oximetry; blood and tissue perfusion; biomedical sensors and instrumentation; physiological/clinical measurement; spectrophotometry; bioinstrumentation
Special Issues, Collections and Topics in MDPI journals

Dr. Matiar Howlader

E-Mail Website
Guest Editor

Department of Electrical and Computer Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada
Interests: soft and hard materials integration; wearable electrochemical sensors; sweat pH and glucose sensing; glutamate sensing; cannabis sensing; water pH and heavy metals sensing; two-dimensional nanomaterials; energy harvesting; surface activated nanobonding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of sensor technology has revolutionized human health management by enabling real-time monitoring, early disease detection, and personalized healthcare solutions. From wearable devices to implantable biosensors, these innovations are transforming medical diagnostics, treatment strategies, and preventive care. This Special Issue aims to bring together original research and review articles on the latest developments, applications, and challenges in advanced sensors for human health management.

Potential topics include, but are not limited to, the following:

Wearable or implantable sensors;
Flexible and stretchable electronics;
Wireless medical sensor networks;
Non-invasive/invasive health monitoring technology;
Smart healthcare systems;
Personalized medicine;
Artificial intelligence in health monitoring;
Multi-modal health sensing;
Physiological data analysis;
Automated diagnosis technology;
Disease diagnosis and treatment.

Dr. Ye Tian
Prof. Dr. Emiliano Schena
Prof. Dr. Panicos Kyriacou
Dr. Matiar Howlader
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 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

health monitoring
disease diagnosis
medical sensors
healthcare
sensing

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Published Papers (8 papers)

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Research

Jump to: Review

20 pages, 2561 KB

Open AccessArticle

Wearable Sensor-Based Analysis of Punch Acceleration and Plantar Pressure Distribution in Boxing

by Liwa Sha and Wen Hsin Chiu

Sensors 2026, 26(9), 2707; https://doi.org/10.3390/s26092707 - 27 Apr 2026

Viewed by 813

Abstract

Punch velocity is a key performance indicator in boxing and reflects effective coordination along the kinetic chain. This study aimed to investigate the relationship between punch acceleration and plantar pressure distribution using wearable sensing technologies. Twenty-four collegiate boxers (12 professional-level and 12 amateur-level athletes) performed jab and cross punches under controlled conditions. Punch acceleration was measured using a glove-mounted inertial measurement unit (IMU), while plantar pressure distribution was recorded using pressure-sensing insoles. Professional boxers demonstrated significantly higher punch acceleration (22–31%, p < 0.05) and greater forefoot plantar pressure (18–27%, p < 0.05) compared to amateur athletes. Correlation analysis revealed significant positive associations between forefoot pressure and punch acceleration (r = 0.62–0.71, p < 0.01), indicating that increased lower-limb force contributes to higher upper-limb striking performance. These findings demonstrate that combined wearable sensing provides a practical approach for quantifying punching biomechanics and identifying level-dependent kinetic-chain characteristics in boxing. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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24 pages, 7261 KB

Open AccessArticle

IFIANet: Frequency Attention Network for Time–Frequency in sEMG-Based Motion Intent Recognition

by Gang Zheng, Jiankai Lin, Jiawei Zhang, Heming Jia, Jiayang Tang and Longtao Shi

Sensors 2026, 26(1), 169; https://doi.org/10.3390/s26010169 - 26 Dec 2025

Cited by 1 | Viewed by 680

Abstract

Lower limb exoskeleton systems require accurate recognition of the wearer’s movement intentions prior to action execution in order to achieve natural and smooth human–machine interaction. Surface electromyography (sEMG) signals can reflect neural activation of muscles before movement onset, making them a key physiological source for movement intention recognition. To improve sEMG-based recognition performance, this study proposes an innovative deep learning framework, IFIANet. First, a CNN–TCN-based spatiotemporal feature learning network is constructed, which efficiently models and represents multi-scale temporal–frequency features while effectively reducing model parameter complexity. Second, an IFIA (Frequency-Informed Integration Attention) module is designed to incorporate global frequency information, compensating for frequency components potentially lost during time–frequency transformations, thereby enhancing the discriminability and robustness of temporal–frequency features. Extensive ablation and comparative experiments on the publicly available MyPredict1 dataset demonstrate that the proposed framework maintains stable performance across different prediction times and achieves over 82% average recognition accuracy in within-experiments involving nine participants. The results indicate that IFIANet effectively fuses local temporal–frequency features with global frequency priors, providing an efficient and reliable approach for sEMG-based movement intention recognition and intelligent control of exoskeleton systems. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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18 pages, 901 KB

Open AccessArticle

Towards Generalized Bioimpedance Models for Bladder Monitoring: The Role of Waist Circumference and Fat Thickness

by H. Trask Crane, John A. Berkebile, Samer Mabrouk, Nicholas Riccardelli and Omer T. Inan

Sensors 2025, 25(24), 7635; https://doi.org/10.3390/s25247635 - 16 Dec 2025

Viewed by 1012

Abstract

Continuous bladder volume monitoring in a wearable format can improve outcomes for patients with bladder dysfunction, heart failure, and other conditions requiring precise fluid management. Bioimpedance-based methods offer a promising, noninvasive solution; however, the influence of patient-specific anatomy, particularly waist circumference and subcutaneous fat thickness, remains poorly characterized. In this study, we use in silico finite element modeling to quantify how these anatomical factors affect two key bioimpedance metrics: voltage change (

Δ V

) and voltage change ratio (VCR). Comprehensive simulations were performed across 15 virtual anatomies, generating a reference dataset for guiding future analog front-end and algorithm designs. We further compared generalized volume estimation models against conventional patient-specific void regression approaches. With appropriate input scaling, the generalized models achieved performance within 10% of patient-specific calibrations and, in some cases, surpassed them. Certain configurations reduced mean average error (MAE) by more than 20% relative to individualized models, potentially enabling a streamlined setup without the need for laborious ground-truth acquisition such as voided volume collection. These results demonstrate that incorporating simple anatomical scaling can yield robust, generalizable bladder volume estimation models suitable for wearable systems across diverse patient populations. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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18 pages, 4994 KB

Open AccessArticle

Enhanced Design and Characterization of a Wearable IMU for High-Frequency Motion Capture

by Diego Valdés-Tirado, Gonzalo García Carro, Juan C. Alvarez, Diego Álvarez and Antonio López

Sensors 2025, 25(19), 6224; https://doi.org/10.3390/s25196224 - 8 Oct 2025

Viewed by 2589

Abstract

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation—including noise, bias, scale factor, power consumption, and drift—demonstrates the device’s reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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26 pages, 1958 KB

Open AccessArticle

Real-Time Heartbeat Classification on Distributed Edge Devices: A Performance and Resource Utilization Study

by Eko Sakti Pramukantoro, Kasyful Amron, Putri Annisa Kamila and Viera Wardhani

Sensors 2025, 25(19), 6116; https://doi.org/10.3390/s25196116 - 3 Oct 2025

Viewed by 1415

Abstract

Early detection is crucial for preventing heart disease. Advances in health technology, particularly wearable devices for automated heartbeat detection and machine learning, can enhance early diagnosis efforts. However, previous studies on heartbeat classification inference systems have primarily relied on batch processing, which introduces delays. To address this limitation, a real-time system utilizing stream processing with a distributed computing architecture is needed for continuous, immediate, and scalable data analysis. Real-time ECG inference is particularly crucial for immediate heartbeat classification, as human heartbeats occur with durations between 0.6 and 1 s, requiring inference times significantly below this threshold for effective real-time processing. This study implements a real-time heartbeat classification inference system using distributed stream processing with LSTM-512, LSTM-256, and FCN models, incorporating RR-interval, morphology, and wavelet features. The system is developed as a distributed web-based application using the Flask framework with distributed backend processing, integrating Polar H10 sensors via Bluetooth and Web Bluetooth API in JavaScript. The implementation consists of a frontend interface, distributed backend services, and coordinated inference processing. The frontend handles sensor pairing and manages real-time streaming for continuous ECG data transmission. The backend processes incoming ECG streams, performing preprocessing and model inference. Performance evaluations demonstrate that LSTM-based heartbeat classification can achieve real-time performance on distributed edge devices by carefully selecting features and models. Wavelet-based features with an LSTM-Sequential architecture deliver optimal results, achieving 99% accuracy with balanced precision-recall metrics and an inference time of 0.12 s—well below the 0.6–1 s heartbeat duration requirement. Resource analysis on Jetson Orin devices reveals that Wavelet-FCN models offer exceptional efficiency with 24.75% CPU usage, minimal GPU utilization (0.34%), and 293 MB memory consumption. The distributed architecture’s dynamic load balancing ensures resilience under varying workloads, enabling effective horizontal scaling. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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14 pages, 1289 KB

Open AccessArticle

Method for Extracting Arterial Pulse Waveforms from Interferometric Signals

by Marian Janek, Ivan Martincek and Gabriela Tarjanyiova

Sensors 2025, 25(14), 4389; https://doi.org/10.3390/s25144389 - 14 Jul 2025

Cited by 1 | Viewed by 1291

Abstract

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min–max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology’s performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry–Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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Review

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31 pages, 3578 KB

Open AccessReview

Measurement of Percentage Depth–Dose Distributions in Clinical Dosimetry: Conventional Techniques and Emerging Sensor Technologies

by Giada Petringa, Luigi Raffaele, Giacomo Cuttone, Mariacristina Guarrera, Alma Kurmanova, Roberto Catalano and Giuseppe Antonio Pablo Cirrone

Sensors 2026, 26(6), 1908; https://doi.org/10.3390/s26061908 - 18 Mar 2026

Viewed by 752

Abstract

Percentage depth–dose (PDD) distributions are fundamental to characterizing radiation beams in radiotherapy. This review provides an overview of both methods and sensor technologies for measuring PDD in photon, electron, proton, and carbon-ion beams. We summarize conventional dosimetry techniques, including water-phantom scanning with ionization chambers (cylindrical and parallel-plate) and radiochromic film, and discuss their strengths (established accuracy, calibration traceability) and limitations (volume averaging, delayed readout). We then examine emerging sensor technologies designed to improve spatial resolution, speed, and radiation hardness: multi-layer ionization chambers and Faraday cups for one-shot PDD acquisition; scintillator-based detectors (liquid, plastic, and fiber-optic) enabling real-time and high-resolution depth–dose measurements; advanced semiconductor detectors including silicon carbide diodes; as well as novel approaches such as ionoacoustic range sensing for proton beams. For each modality and detector type, we emphasize clinical relevance, measurement accuracy, spatial resolution, radiation durability, and suitability for high dose-per-pulse environments (e.g., FLASH radiotherapy). Current challenges, such as detector response in regions of steep dose gradient, saturation or recombination at ultra-high dose rates, and energy-dependent sensitivity in mixed radiation fields, are analyzed in detail. We also highlight the limitations of each technique and discuss ongoing improvements and prospects for clinical implementation. In summary, no single detector technology fully satisfies all requirements for fast, high-accuracy, high-resolution, radiation-hard PDD measurement, but the integration of emerging sensor innovations into clinical dosimetry promises to enhance the precision and efficiency of radiotherapy quality assurance. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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24 pages, 8671 KB

Open AccessReview

Tactile Interaction with Socially Assistive Robots for Children with Physical Disabilities

by Leila Mouzehkesh Pirborj, Caroline Mills, Robert Gorkin III and Karthick Thiyagarajan

Sensors 2025, 25(13), 4215; https://doi.org/10.3390/s25134215 - 6 Jul 2025

Viewed by 2494

Abstract

Children with physical disabilities are increasingly using socially assistive robots (SARs) as part of therapy to enhance motivation, engagement, enjoyment, and adherence. Research on SARs in rehabilitation has primarily focused on verbal and visual interaction, but little is known about tactile interaction (physical touch). The objective of this scoping review was to examine empirical studies published between 2010 and 2024 focusing on tactile interaction between SARs and children with physical disabilities, such as cerebral palsy (CP). Nine studies were identified as being eligible after a rigorous selection process, showing that although touch-based SAR interventions have been used in pediatric rehabilitation, structured methodologies and standardized tools are lacking for measuring tactile engagement. In light of the studies’ findings, it is evident that few studies evaluate the therapeutic effects of touch-sensitive SARs, underscoring the need for validated frameworks to assess their efficacy. In this review, SAR and tactile sensing researchers, rehabilitation specialists, and designers are given critical insights into how tactile interaction can enhance the role of SARs in physical therapy. Full article

(This article belongs to the Special Issue Advanced Sensors for Human Health Management)

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