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Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring

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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: 20 September 2026 | Viewed by 18111

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Special Issue Editors

Dr. Elisabetta Peri

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Guest Editor

Electrical Engineering Dept, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: biomedical signal processing; electrophysiological monitoring; non-invasive patient monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Alessandra Galli

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Guest Editor

Dr. Sarah Tonello

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Guest Editor

Information Engineering Dept, University of Brescia, Brescia, Italy
Interests: printed sensors; wearable sensors; electrochemical biosensors; electrophysiological monitoring

Dr. Ana Neacșu

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Guest Editor

Campus Research Institute, National University of Science and Technology POLITEHNICA, Bucharest, Romania
Interests: signal processing; deep learning; machine learning; automatic biomedical analysis

Dr. Maria G. Signorini

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Guest Editor

Department of Electronic, Information and Bioengineering, Politecnico of Milan, Milan, Italy
Interests: biomedical signal processing; bioengineering

Special Issue Information

Dear Colleagues,

The rapid advancement in non-invasive biomedical diagnostics and monitoring holds the potential to transform healthcare by offering portable and unobtrusive devices that provide real-time, continuous monitoring of human physiological conditions. These innovations pave the way to early detection of diseases, facilitating timely, less invasive interventions, improving patient outcomes, and reducing hospitalization and associated costs.

Robust monitoring requires joint development of sensing technology and signal processing algorithms for extracting, analyzing, and interpreting complex biological signals, including‑but not limited to‑electrocardiograms, electroencephalograms, photoplethysmograms, and other physiological data.

This Special Issue aims to highlight cutting-edge research and encourage innovation in the field of non-invasive biomedical diagnostics and monitoring, by covering a wide spectrum of technological and methodological advances.

Topics of interest include, but are not limited to, the following:

The development of novel sensor technologies;
Advanced algorithms for signal denoising, feature extraction, and classification;
Integration of machine learning models for diagnostic and monitoring applications;
Strategies for minimizing power consumption;
Algorithms for real-time processing in wearable and portable devices;
Methods for ensuring signal integrity across diverse clinical and home environments;

Both original contributions and reviews focused on the latest achievements of scientific research and emerging techniques are welcome.

Dr. Elisabetta Peri
Dr. Alessandra Galli
Dr. Sarah Tonello
Dr. Ana Neacșu
Dr. Maria G. Signorini
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

biomedical diagnostics
biomedical signal processing
non-invasive monitoring
biosensors
non-obtrusive devices
machine learning for biomedical applications

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Published Papers (6 papers)

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Research

32 pages, 28708 KB

Open AccessArticle

Adaptive Thermal Imaging Signal Analysis for Real-Time Non-Invasive Respiratory Rate Monitoring

by Riska Analia, Anne Forster, Sheng-Quan Xie and Zhiqiang Zhang

Sensors 2026, 26(1), 278; https://doi.org/10.3390/s26010278 - 1 Jan 2026

Viewed by 1008

Abstract

(1) Background: This study presents an adaptive, contactless, and privacy-preserving respiratory-rate monitoring system based on thermal imaging, designed for real-time operation on embedded edge hardware. The system continuously processes temperature data from a compact thermal camera without external computation, enabling practical deployment for [...] Read more.

256 \times 192

TOPDON TC001 camera and processed entirely on an NVIDIA Jetson Orin Nano. A YOLO-based detector localizes the nostril region in every even frame (stride = 2) to reduce the computation load, while a Kalman filter predicts the ROI position on skipped frames to maintain spatial continuity and suppress motion jitter. From the stabilized ROI, a temperature-based breathing signal is extracted and analyzed through an adaptive median–MAD hysteresis algorithm that dynamically adjusts to signal amplitude and noise variations for breathing phase detection. Respiratory rate (

R R

) is computed from inter-breath intervals (IBI) validated within physiological constraints. (3) Results: Ten healthy subjects participated in six experimental conditions including resting, paced breathing, speech, off-axis yaw, posture (supine), and distance variations up to 2.0 m. Across these conditions, the system attained a MAE of

0.57 \pm 0.36

BPM and an RMSE of

0.64 \pm 0.42

BPM, demonstrating stable accuracy under motion and thermal drift. Compared with peak-based and FFT spectral baselines, the proposed method reduced errors by a large margin across all conditions. (4) Conclusions: The findings confirm that accurate and robust respiratory-rate estimation can be achieved using a low-resolution thermal sensor running entirely on an embedded edge device. The combination of YOLO-based nostril detector, Kalman ROI prediction, and adaptive MAD–hysteresis phase that self-adjusts to signal variability provides a compact, efficient, and privacy-preserving solution for non-invasive vital-sign monitoring in real-world environments. Full article

(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)

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24 pages, 1482 KB

Open AccessArticle

CONECT: Novel Weighted Networks Framework Leveraging Angle-Relation Connection (ARC) and Metaheuristic Algorithms for EEG-Based Dementia Classification

by Akashdeep Singh, Supriya Supriya, Siuly Siuly and Hua Wang

Sensors 2025, 25(24), 7439; https://doi.org/10.3390/s25247439 - 7 Dec 2025

Cited by 2 | Viewed by 806

Abstract

Accurate and robust classification of dementia subtypes using non-invasive electroencephalography (EEG) signals remains a critical challenge for clinicians and researchers in the field of neuroscience. Traditional methods often rely on limited spectral features, overlooking the rich structural and geometric information inherent in EEG dynamics. CONECT (Complex Network Conversion and Topology), a novel framework, is introduced and built upon four core innovations. First, EEG time series are transformed into weighted networks using a novel Angle-Relation Connection (ARC) rule, a geometry-based approach that links time points based on angular monotonicity. Secondly, a tunable edge-weighting function is introduced by integrating amplitude, temporal, and angular components, providing adaptable heuristics adaptable to the most promising biomarker, i.e., curvature-driven features in dementia. Additionally, two new graph-based EEG features, the Weighted Angular Irregularity Index (WAII) and the Curvature-Based Edge Feature Index (CBEFI), are proposed as potential biomarkers to capture localized irregularity and signal geometry, respectively. For the first time in a dementia EEG classification study using the OpenNeuro ds004504 dataset (raw), Ant Colony Optimization (ACO) is applied as a feature selection technique to select the most discriminative features and improve model classification and transparency. The classification results demonstrate CONECT’s potential as a promising, interpretable, and geometry-informed framework for accurate and practical dementia subtype diagnosis. Full article

(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)

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13 pages, 2317 KB

Open AccessArticle

Non-Invasive Blood Cortisol Estimation from Sweat Analysis by Kinetic Modeling of Cortisol Transport Dynamics

by Xiaoyu Yin, Sophie Adelaars, Elisabetta Peri, Eduard Pelssers, Jaap den Toonder, Arthur Bouwman, Daan van de Kerkhof and Massimo Mischi

Sensors 2025, 25(15), 4551; https://doi.org/10.3390/s25154551 - 23 Jul 2025

Cited by 2 | Viewed by 4123

Abstract

We present a novel method to estimate blood cortisol concentration from sweat cortisol measurements, incorporating a kinetic model to simulate cortisol transport dynamics. Cortisol dysregulation is observed in conditions like Cushing’s syndrome, characterized by excessive cortisol production, and stress-related disorders, which can lead to metabolic disturbances, anxiety, and impaired overall health. Sweat-sensing technology offers a non-invasive and continuous alternative to blood sampling. However, the limited research exploring the sweat–blood cortisol relationship in patients shows a moderate correlation (

R < 0.6

), hindering its clinical application for long-term monitoring. In this paper, we propose a novel kinetic model describing cortisol transport from blood to sweat. The model was validated using data from 44 patients before and after cardiac surgery. A high Pearson correlation coefficient of 0.95 (95% CI: 0.92–0.97) was observed between our model’s estimated and experimental blood cortisol concentrations. Moreover, the method enables personalized estimation of physiological parameters, accurately reflecting patients’ status under varying clinical conditions. The method paves the way for the clinical application of long-term, non-invasive monitoring of cortisol using sweat-sensing technology. Enabling the personalized estimation of physiological parameters could potentially support clinical decision-making, helping doctors diagnose and monitor patients with health conditions involving cortisol dysregulation. Full article

(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)

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19 pages, 3002 KB

Open AccessArticle

A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease

by Elza Abdessater, Paniz Balali, Jimmy Pawlowski, Jérémy Rabineau, Cyril Tordeur, Vitalie Faoro, Philippe van de Borne and Amin Hossein

Sensors 2025, 25(11), 3360; https://doi.org/10.3390/s25113360 - 27 May 2025

Cited by 2 | Viewed by 2194

Abstract

Severe aortic valve diseases (AVD) cause changes in heart sounds, making phonocardiogram (PCG) analyses challenging. This study presents a novel method for segmenting heart sounds without relying on an electrocardiogram (ECG), specifically targeting patients with severe AVD. Our algorithm enhances traditional Hidden Semi-Markov Models by incorporating signal envelope calculations and statistical tests to improve the detection of the first and second heart sounds (S1 and S2). We evaluated the method on the PhysioNet/CinC 2016 Challenge dataset and a newly acquired AVD-specific dataset. The method was tested on a total of 27,400 cardiac cycles. The proposed approach outperformed the existing methods, achieving a higher sensitivity and positive predictive value for S2, especially in the presence of severe heart murmurs. Notably, in patients with severe aortic stenosis, our proposed ECG-free method improved S2 sensitivity from 41% to 70%. Full article

(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)

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12 pages, 361 KB

Open AccessEditor’s ChoiceArticle

Analysis of Electrodermal Signal Features as Indicators of Cognitive and Emotional Reactions—Comparison of the Effectiveness of Selected Statistical Measures

by Marcin Jukiewicz and Joanna Marcinkowska

Sensors 2025, 25(11), 3300; https://doi.org/10.3390/s25113300 - 24 May 2025

Cited by 5 | Viewed by 5399

Abstract

This study investigates which statistical measures of electrodermal activity (EDA) signal features most effectively differentiate between responses to stimuli and resting states in participants performing tasks with varying cognitive and emotional reactions. The study involved 30 healthy participants. Collected EDA data were statistically analyzed, comparing the effectiveness of twelve statistical signal measures in detecting stimulus-induced changes. The aim of this study is to answer the following research question: Which statistical features of the electrodermal activity signal most effectively indicate changes induced by cognitive and emotional reactions, and are there such significant similarities (high correlations) among these features that some of them can be considered redundant? The results indicated that amplitude-related measures—mean, median, maximum, and minimum—were most effective. It was also found that some signal features were highly correlated, suggesting the possibility of simplifying the analysis by choosing just one measure from each correlated pair. The results indicate that stronger emotional stimuli lead to more pronounced changes in EDA than stimuli with a low emotional load. These findings may contribute to the standardization of EDA analysis in future research on cognitive and emotional reaction engagement. Full article

(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)

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17 pages, 900 KB

Open AccessArticle

Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors

by Giacomo Peruzzi, Alessandra Galli, Giada Giorgi and Alessandro Pozzebon

Sensors 2025, 25(2), 458; https://doi.org/10.3390/s25020458 - 14 Jan 2025

Cited by 15 | Viewed by 3605

Abstract

Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements. Full article

(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)

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