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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 12230

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Special Issue Editors

Dr. Falk Magnus

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Guest Editor

Prof. Dr. Sergey Shleev

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Guest Editor

Department of Biomedical Science, Faculty of Health and Society and Biofilms, Research Center for Biointerfaces, Malmo University, 205 06 Malmö, Sweden
Interests: bioelectronics; biosensors; biological power sources; biofuel cells; biosupercapacitor; physiological sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

(Bio)sensing is a rapidly growing interdisciplinary field that integrates engineering, chemistry, biology, physics, medicine, and computational data analytics to develop advanced sensors for monitoring various physiological conditions. These sensors often provide continuous, real-time, and nonintrusive monitoring capabilities, significantly enhancing our understanding and management of human health and performance.

Monitoring human physiological signals plays a crucial role in guiding health management and exercise training. These signals can be categorized into physical signals, such as blood pressure and temperature, and chemical signals, which detect bioanalytes in saliva, blood, tears, and sweat. Traditionally, the term biosensor implies the incorporation of a biological component to recognize and detect analytes. At present, we use the term (bio)sensor to more generally describe all sensors aimed at monitoring and detecting human physiological signals, regardless of the origin of the recognition component.

This Special Issue aims to provide a platform for researchers from diverse scientific disciplines to exchange cutting-edge research in the field of (bio)sensors for physiological monitoring. We welcome authors to contribute reviews and original research articles that will illustrate the latest advancements and stimulate ongoing research in this exciting and emerging field. By fostering interdisciplinary collaboration, this Special Issue seeks to drive innovation and improve the capabilities of (bio)sensors in enhancing personalized healthcare and human performance monitoring.

Dr. Falk Magnus
Prof. Dr. Sergey Shleev
Guest Editors

Manuscript Submission Information

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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.

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Keywords

(bio)sensors
physical and chemical sensors
electrical, optical, mechanical, and thermal sensors
physiological monitoring
health monitoring
real-time sensing
wearable technology
personalized healthcare

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Published Papers (10 papers)

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Research

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20 pages, 8423 KiB

Open AccessArticle

Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm² and an Ultra-High Input Impedance

by Esmaeil Ranjbar Koleibi, William Lemaire, Konin Koua, Maher Benhouria, Reza Bostani, Mahziar Serri Mazandarani, Luis-Philip Gauthier, Marwan Besrour, Jérémy Ménard, Mahdi Majdoub, Benoit Gosselin, Sébastien Roy and Réjean Fontaine

Sensors 2025, 25(7), 2320; https://doi.org/10.3390/s25072320 - 5 Apr 2025

Viewed by 424

Abstract

Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a −3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μ

V_{rms}

, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μ

m^{2}

, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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20 pages, 1786 KiB

Open AccessArticle

The Functional Signature of Decision Making Across Dyads During a Persuasive Scenario: Hemodynamic fNIRS Coherence Measure

by Michela Balconi, Roberta A. Allegretta, Carlotta Acconito, Federica Saquella and Laura Angioletti

Sensors 2025, 25(6), 1880; https://doi.org/10.3390/s25061880 - 18 Mar 2025

Viewed by 324

Abstract

Introduction: Within a shared decision-making process, persuasion dynamics develop as a communication sub-process that can be characterized by different phases. This study examines hemodynamic functional Near-Infrared Spectroscopy (fNIRS) coherence measures in dyads of decision-makers. The interaction occurs in two phases: Phase 1, where the persuader (Pr) introduces the decision topic and uses persuasive strategies, and Phase 2, where the Persuaded (Pd) responds and may agree with the Pr’s selected option. Method: Fourteen dyads participated, with fNIRS measuring oxygenated (O₂Hb) and deoxygenated (HHb) hemoglobin concentration changes in the prefrontal cortex (PFC) during both phases. Hemodynamic coherence within dyads was explored through the computation of a dissimilarity index (Euclidean distance). Results: Phase 2 showed increased HHb dissimilarity, indicating greater divergence in brain activity during the Pd’s response phase. Discussion: These findings suggest that, during persuasion, when Pd responds, there is increased dissimilarity in cognitive and neural processes, without implying a loss of synergy. The study highlights the importance of interactional dynamics in shaping decision outcomes and underscores the potential of fNIRS as a non-invasive tool for monitoring brain activity in clinical and collaborative settings. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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27 pages, 4621 KiB

Open AccessArticle

A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor

by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Emad Muteb Alharbi, Hibah Qasem Salman Alatawi, Kousalya Prabahar, Jawhara Bader Aljabri and Anand Paul

Sensors 2025, 25(6), 1868; https://doi.org/10.3390/s25061868 - 18 Mar 2025

Viewed by 494

Abstract

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving average filter were applied to remove noise and preserve pulse form and high-frequency components. The DSCNet model was then applied to predict the sugar level. Two models were developed for prediction: a baseline model, DSCNet, and an enhanced model, DSCNet with self-attention. DSCNet’s performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Relative Difference (MARD), and coefficient of determination (R²), yielding values of 3.022, 0.05, 0.058, 0.062, 10.81, and 0.98, respectively. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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20 pages, 5052 KiB

Open AccessArticle

Assessment of the Mechanical Properties of Soft Tissue Phantoms Using Impact Analysis

by Arthur Bouffandeau, Anne-Sophie Poudrel, Chloé Brossier, Giuseppe Rosi, Vu-Hieu Nguyen, Charles-Henri Flouzat-Lachaniette, Jean-Paul Meningaud and Guillaume Haïat

Sensors 2025, 25(5), 1344; https://doi.org/10.3390/s25051344 - 22 Feb 2025

Viewed by 317

Abstract

Skin physiopathological conditions have a strong influence on its biomechanical properties. However, it remains difficult to accurately assess the surface stiffness of soft tissues. The aim of this study was to evaluate the performances of an impact-based analysis method (IBAM) and to compare them with those of an existing digital palpation device, MyotonPro^®. The IBAM is based on the impact of an instrumented hammer equipped with a force sensor on a cylindrical punch in contact with agar-based phantoms mimicking soft tissues. The indicator

Δ t

is estimated by analyzing the force signal obtained from the instrumented hammer. Various phantom geometries, stiffnesses and structures (homogeneous and bilayer) were used to estimate the performances of both methods. Measurements show that the IBAM is sensitive to a volume of interest equivalent to a sphere approximately twice the punch diameter. The sensitivity of the IBAM to changes in Young’s modulus is similar to that of dynamic mechanical analysis (DMA) and significantly better compared to MyotonPro. The axial (respectively, lateral) resolution is two (respectively, five) times lower with the IBAM than with MyotonPro. The present study paves the way for the development of a simple, quantitative and non-invasive method to measure skin biomechanical properties. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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10 pages, 2195 KiB

Open AccessArticle

An Optical Wireless Communication System for Physiological Data Transmission in Small Animals

by Ana R. Domingues, Diogo Pereira, Manuel F. Silva, Sara Pimenta and José H. Correia

Sensors 2025, 25(1), 138; https://doi.org/10.3390/s25010138 - 29 Dec 2024

Viewed by 3751

Abstract

In biomedical research, telemetry is used to take automated physiological measurements wirelessly from animals, as it reduces their stress and allows recordings for large data collection over long periods. The ability to transmit high-throughput data from an in-body device (e.g., implantable systems, endoscopic capsules) to external devices can also be achieved by radiofrequency (RF), a standard wireless communication procedure. However, wireless in-body RF devices do not exceed a transmission speed of 2 Mbit/s, as signal absorption increases dramatically with tissue thickness and at higher frequencies. This paper presents the design of an optical wireless communication system (OWCS) for neural probes with an optical transmitter, sending out physiological data through an optical signal that is detected by an optical receiver. The optical receiver position is controlled by a tracking system of the small animal position, based on a cage with a piezoelectric floor. To validate the concept, an OWCS based on a wavelength of 850 nm for a data transfer of 5 Mbit/s, with an optical power of 55 mW, was demonstrated for a tissue thickness of approximately 10 mm, measured in an optical tissue phantom. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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18 pages, 1362 KiB

Open AccessArticle

Does Heart Rate Variability Predict Impairment of Operational Performance in Divers?

by John Freiberger, Bruce Derrick, Ki H. Chon, Md Billal Hossain, Hugo F. Posada-Quintero, Mary Cooter and Richard Moon

Sensors 2024, 24(23), 7726; https://doi.org/10.3390/s24237726 - 3 Dec 2024

Viewed by 957

Abstract

We examined data from Naval Sea Systems Command grant project N0463A-12-C-001, “Hypercapnia: cognitive effects and monitoring”, with the objective of validating or repudiating heart rate variability (HRV) as a warning sign of cognitive impairment from diving gas narcosis or oxygen toxicity. We compared HRV feature scores to their temporally corresponding cognitive outcomes under normal and narcotizing conditions to identify specific HRV features associated with cognitive changes. N0463A-12-C-001 was conducted between 17 September 2013 and 29 January 2016 and employed NASA’s multi-attribute task battery (MATB-II) flight simulator to examine the independent effects of CO₂, N₂, and O₂ partial pressure on diver performance at simulated depths up to 61 msw (200 fsw). We assessed the association of 23 distinct HRV features scores from 432 of the study’s analyzable exposure stages in relation to MATB-II’s four performance subclasses (motor, memory, attention, strategy) while controlling for exercise and CO₂, N₂, and O₂ gas partial pressure. Performance decrements were associated with normalized high-frequency HRVfeatures (HFnu, p = 0.0016) and the number of pairs of successive R-R intervals that differed by more than 50 ms (NN50count1, p = 0.04). Secondary analysis with stratification restricted to non-exercise stages showed that several HRV parameters, including root mean square of the successive difference (RMSSD, p = 0.0015), width of Poincaré plot (p = 0.0017), NN50count1 (p = 0.0019), and standard deviation of normal-to-normal R peaks (p = 0.0082), were associated with performance impairment. The RMSSD association retained statistical significance after Bonferroni correction for multiple tests. HRV features collected from divers tested under narcotizing conditions of breathing gas partial pressure and exercise were associated with performance impairment. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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19 pages, 6266 KiB

Open AccessArticle

The Optical Parameter Optimization for Brain Implant Alzheimer Sensor Using Phototherapy Angle and Wavelength Simulation (PAWS) Methodology

by So-Hyun Cho, Chang-Hee Won, Chang-Hyun Kim and Jong-Ha Lee

Sensors 2024, 24(22), 7282; https://doi.org/10.3390/s24227282 - 14 Nov 2024

Viewed by 1100

Abstract

Photonic therapy is emerging as a promising method in neuroscience for addressing Alzheimer’s disease (AD). This study uses computational simulations to investigate the impact of specific wavelengths emitted by photodiodes on the light absorption rates in brain tissue for brain implant sensors. Additionally, it presents a novel methodology that enhances light absorption via multi-parameter optimization. By adjusting the angle and wavelength of the incident light, the absorption rate was significantly enhanced using four photodiodes, each emitting at 660 nm with a power input of 3 mW. Notably, an incident angle of 20 degrees optimized light absorption and minimized thermal effects on brain tissue. The findings indicate that photodiodes within the near-infrared spectrum are suitable for low-temperature therapeutic applications in brain tissues, affirming the viability of non-invasive and safe photonic therapy. This research contributes foundational data for advancing brain implant photonic sensor design and therapeutic strategies. Furthermore, it establishes conditions for achieving high light absorption rates with minimal heat generation, identifying optimal parameters for efficient energy transfer. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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19 pages, 2090 KiB

Open AccessArticle

Thermal Bed Design for Temperature-Controlled DNA Amplification Using Optoelectronic Sensors

by Guillermo Garcia-Torales, Hector Hugo Torres-Ortega, Ruben Estrada-Marmolejo, Anuar B. Beltran-Gonzalez and Marija Strojnik

Sensors 2024, 24(21), 7050; https://doi.org/10.3390/s24217050 - 31 Oct 2024

Viewed by 990

Abstract

Loop-Mediated Isothermal Loop-Mediated Isothermal Amplification (LAMP) is a widely used technique for nucleic acid amplification due to its high specificity, sensitivity, and rapid results. Advances in microfluidic lab-on-chip (LOC) technology have enabled the integration of LAMP into miniaturized devices, known as

μ

-LAMP, [...] Read more.

μ

-LAMP, which require precise thermal control for optimal DNA amplification. This paper introduces a novel thermal bed design using PCB copper traces and

F R - 4

dielectric materials, providing a reliable, modular, and repairable heating platform. The system achieves accurate and stable temperature control, which is critical for

μ

-LAMP applications, with temperature deviations within ±1.0 °C. The thermal bed’s performance is validated through finite element method (FEM) simulations, showing uniform temperature distribution and a rapid thermal response of 2.5 s to reach the target temperature. These results highlight the system’s potential for applications such as disease diagnostics, biological safety, and forensic analysis, where precision and reliability are paramount. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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11 pages, 1730 KiB

Open AccessArticle

Analytical Performance of the FreeStyle Libre 2 Glucose Sensor in Healthy Male Adults

by Eva Fellinger, Tom Brandt, Justin Creutzburg, Tessa Rommerskirchen and Annette Schmidt

Sensors 2024, 24(17), 5769; https://doi.org/10.3390/s24175769 - 5 Sep 2024

Cited by 2 | Viewed by 3461

Abstract

Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8–14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57–1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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Review

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31 pages, 1738 KiB

Open AccessReview

A Review of Developments in Carbon-Based Nanocomposite Electrodes for Noninvasive Electroencephalography

by Hector Medina and Nathan Child

Sensors 2025, 25(7), 2274; https://doi.org/10.3390/s25072274 - 3 Apr 2025

Viewed by 287

Abstract

Wearable biosensors have been of interest for their wide range of uses, varying from recording biological signals to measuring strain of bending joints. Carbon nanoparticles have been utilized in biocompatible polymers to create nanocomposites with highly tunable mechanical and electrical properties. These nanocomposites have been demonstrated to be highly effective as wearable sensors for recording physiological signals such as electroencephalography (EEG), offering advantages in mechanical and electrical properties and signal quality over commercially available sensors while maintaining feasibility and scalability in manufacturing. This review aims to provide a critical summary of the recent literature on the properties, design, fabrication, and performance of carbon-based nanocomposites for EEG electrodes. The goal of this review is to highlight the various design configurations and properties thereof, manufacturing methods, performance measurements, and related challenges associated with these promising noninvasive dry soft electrodes. While this technology offers many advantages over either other noninvasive or their invasive counterparts, there are still various challenges and opportunities for improvements and innovation. For example, the investigation of gradient composite structures, hybrid nanocomposite/composite materials, hierarchical contact surfaces, and the influence of loading and alignment of the dispersal phase in the performance of these electrodes could lead to novel and better designs. Finally, current practices for evaluating the performance of novel EEG electrodes are discussed and challenged, emphasizing the critical need for the development of standardized assessment protocols, which could provide reliability in the field, enable benchmarking, and hence promote innovation. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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