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Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition

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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 12421

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Special Issue Editor

Prof. Dr. Janusz Kulon

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Guest Editor

Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK
Interests: biomedical engineering and computing; design of medical instrumentation; non-invasive sensor applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of sensor technology is witnessing transformative change with the advent of non-invasive sensing methods. This non-invasive approach, preserving the integrity of the observed system or process, ensures a wide range of applications, from personal health monitoring to industrial automation, agriculture, and environmental sensing. Advancements in methodologies, ranging from design and fabrication to the processing algorithms, play a pivotal role in enhancing the efficiency and effectiveness of non-invasive sensors.

How can we advance non-invasive sensor technology to deliver higher performance and broader applicability? How can we leverage these advancements to innovate applications across various domains? We eagerly await innovative research papers that address these questions and illuminate the path forward for non-invasive sensing.

This Special Issue, entitled “Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition”, welcomes contributions that delve into every facet of non-invasive sensor technology.

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

Advanced design and fabrication of non-invasive sensors;
Innovative sensor applications in health monitoring, agriculture, industry, and environment;
Integration of AI and non-invasive sensors;
Non-invasive sensor networks;
Data analysis and processing for non-invasive sensor signals;
Wearable non-invasive devices;
Sensor-captured imaging and non-invasive techniques.

Let us collaborate to shed light on the exciting frontier of non-invasive sensor technology, its potential, and practical applications across diverse domains. We look forward to your valuable contributions.

Prof. Dr. Janusz Kulon
Guest Editor

Manuscript Submission Information

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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

non-invasive sensors
smartphones
non-invasive sensor networks
wearable non-invasive devices
data analysis
sensor design

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Advanced Non-Invasive Sensors: Methods and Applications in Sensors (7 articles)

Published Papers (7 papers)

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Research

23 pages, 6892 KB

Open AccessArticle

A Multi-Scale Edge-Preserving Decomposition and Fusion Framework for Multi-Polarization Passive Millimeter-Wave Imaging

by Xinpeng Chen, Fei Hu, Dong Zhu, Jinlong Su, Bo Fang and Jingyu Tao

Sensors 2026, 26(11), 3577; https://doi.org/10.3390/s26113577 - 4 Jun 2026

Abstract

Passive millimeter-wave (PMMW) imaging technology has become a highly promising technology that can protect privacy in human body security inspections. However, most existing methods rely on single-pixel and single-polarization processing mechanisms, which often lead to discrete false-alarm pixels or missed detections in practical applications. Although multi-polarization information can provide richer distinguishing features, the current methods typically depend on limited Stokes parameters or artificially designed polarization features, lacking a systematic framework to fully exploit the intrinsic potential of multi-polarization information. In this paper, we propose a novel multi-scale edge-preserving decomposition model, termed Gaussian and weighted average curvature filtering (GWACF), to hierarchically decompose a multi-polarization PMMW image into three structural layers: base structural (BS) layer, coarse structural (CS) layer, and fine structural (FS) layer. Furthermore, we also propose a fusion strategy in which a gradient-domain pulse-coupled neural network (PCNN) is employed to fuse the texture-rich CS and FS layers, while the energy attribute fusion method is applied to the BS layer where primary structure and background information play a dominant role. This method effectively leverages complementary polarimetric information without introducing artifacts or compromising edge sharpness. Experimental results demonstrate that the proposed method effectively enhances the brightness temperature (BT) contrast of concealed objects. Compared with existing mainstream methods, it exhibits notable advantages in both detection accuracy and robustness. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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24 pages, 2806 KB

Open AccessArticle

Contactless Cardiac Health Monitoring with Millimeter-Wave Radar Based on PMG-SATNet

by Tianjiao Guo, Jianqi Wang, Nianzeng Yuan, Hao Lv, Fulai Liang, Zhiyuan Zhang, Jingzhe Wang, Yunuo Long and Huijun Xue

Sensors 2026, 26(9), 2579; https://doi.org/10.3390/s26092579 - 22 Apr 2026

Viewed by 783

Abstract

Cardiovascular diseases are the primary causes of mortality worldwide, often characterized by subtle onset and acute progression. Traditional ECG electrodes may cause skin irritation, limiting routine monitoring and early risk assessment. Relying on the advantages of non-contact monitoring, millimeter-wave radar-based cardiac monitoring combined with deep learning has become a popular research direction recently. To overcome the poor generalization of methods trained from single-source datasets, this study designed seven experimental scenarios covering wakefulness and sleep. A novel deep learning network consisting of encoder and decoder structures named PMG-SATNet was proposed. The encoder comprises a parallel multi-scale feature extraction module and a global temporal relationship modeling module to capture fine-grained local patterns and long-range dependencies. The decoder employs a temporal convolutional network augmented with a spectral attention mechanism to emphasize clinically relevant ECG frequency bands and suppress respiration and body motion interference. After being validated on the self-built dataset, PMG-SATNet outperformed baseline models in terms of Pearson correlation coefficient and root mean square error, with an improvement of 3.3% and 3.8%, and 16.4% and 23.8%, respectively. The validation results imply that PMG-SATNet is capable of recovering ECG signals from millimeter-wave radar-derived chest vibrations with high fidelity and can potentially be implemented in real-life cardiac health monitoring. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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16 pages, 4513 KB

Open AccessArticle

On the Use of a Depth Camera for the Assessment of Upper Extremity Movements in Healthy Individuals

by Serkan Çizmecioğulları, Şenay Mihçin and Aydin Akan

Sensors 2026, 26(6), 1762; https://doi.org/10.3390/s26061762 - 11 Mar 2026

Viewed by 443

Abstract

Upper extremity impairments often lead to reduced joint range of motion (ROM), making reliable assessment essential for rehabilitation planning. This study investigated the within-day and between-day reliability of the Microsoft Kinect V2 depth camera for active upper extremity ROM assessment in 30 healthy adults. Ten predefined shoulder and elbow movements were recorded, and joint angles were computed using a custom vector-based algorithm. Within-day reliability ranged from moderate to excellent (ICC: 0.754–0.953), while between-day reliability ranged from moderate to good (ICC: 0.654–0.881). Absolute reliability varies substantially across movements. The SEM% values ranged from 2.1% to 17.3% within-day and from 2.8% to 23.6% between-day. The between-day MDC values were particularly high for certain movements (e.g., >20° for shoulder extension and >50° for elbow flexion), indicating limited sensitivity to detect small clinical changes. Additionally, shoulder adduction could not be reliably analyzed in 36.7% of participants due to self-occlusion-related tracking instability, highlighting a practical limitation of the Kinect V2 for certain upper extremity movements. These findings suggest that Kinect V2-based ROM assessment demonstrates acceptable reliability for large-amplitude planar movements under controlled conditions but shows substantial limitations for rotational and occlusion-prone tasks. The device may be suitable for research or screening applications; however, caution is warranted when interpreting small changes in clinical settings. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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26 pages, 4148 KB

Open AccessArticle

Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning

by Jose Ignacio Sánchez Méndez, Elsa Fernandez, Alberto Bergareche and Karmele Lopez-de-Ipina

Sensors 2026, 26(1), 244; https://doi.org/10.3390/s26010244 - 31 Dec 2025

Viewed by 1248

Abstract

Background: Essential tremor (ET) is among the most common neurological disorders, requiring precise diagnosis and severity assessment for personalized and effective management. Methods: This study explores an innovative approach to evaluate ET severity using the gold-standard Archimedes spiral test. The family-based dataset covers the entire range of tremor severity, from very mild (level 1) to advanced stages, offering a valuable resource for studying early diagnosis and tracking disease progression. The proposed method introduces a machine learning pipeline that combines Principal Component Analysis (PCA), linear discriminant analysis (LDA), and support vector machines (SVMs) to classify ET severity based on Archimedean spiral radius data. Results: By incorporating the Fahn–Tolosa–Marin Tremor Rating Scale (FMT-TRS), the pipeline effectively distinguishes between tremor presence and severity. Its robustness was demonstrated through rigorous cross-validation and tests involving Gaussian noise perturbations. Conclusions: These results underscore the machine learning-based pipeline’s potential as a non-invasive and trustworthy diagnostic tool for clinical use and telemedicine applications. Moreover, the combination of geometric features, FMT-TRS scores, clinically oriented evaluation metrics, and classical statistical and machine learning models offers a robust, interpretable, explainable, and clinically meaningful analytical framework. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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17 pages, 3129 KB

Open AccessArticle

A Framework to Evaluate Feasibility, Safety, and Accuracy of Wireless Sensors in the Neonatal Intensive Care Unit: Oxygen Saturation Monitoring

by Eva Senechal, Daniel Radeschi, Emily Jeanne, Ana Saveedra Ruiz, Brittany Dulmage, Wissam Shalish, Robert E. Kearney and Guilherme Sant’Anna

Sensors 2025, 25(18), 5647; https://doi.org/10.3390/s25185647 - 10 Sep 2025

Viewed by 1755

Abstract

Monitoring vital signs in the Neonatal Intensive Care Unit (NICU) typically relies on wired skin sensors, which can limit mobility, cause skin issues, and interfere with parent–infant bonding. Wireless sensors offer promising alternatives, but evaluations to date often emphasize accuracy alone, lack NICU-specific validation, and rarely use standardized frameworks. Our objective was to develop and apply a comprehensive framework for evaluating the feasibility, safety, and accuracy of wireless monitoring technologies using a wireless pulse oximeter, the Anne limb (Sibel Health, USA), in real-world NICU conditions. A prospective study was conducted on a diverse NICU population. A custom system enabled synchronized data recordings from both standard and wireless devices. Feasibility was assessed as signal coverage across a variety of daily care activities and during routine procedures. Safety was evaluated through skin assessments after extended wear. Accuracy was examined sample-by-sample and interpreted using the Clarke Error Grid for clinical relevance. The wireless oximeter device showed high feasibility with reliable Bluetooth connection across a range of patients and activities (median wireless PPG coverage = 100%, IQR: 99.85–100%). Skin assessments showed no significant adverse effects. Accuracy was strong overall (median bias 1.34%, 95% LoA −3.63 to 6.41), with most data points within clinically acceptable Clarke error grid zones A and B, though performance declined for infants on supplemental oxygen. This study presents a robust, multidimensional framework for evaluating wireless monitoring devices in NICUs and offers recommendations for future research design and reporting. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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24 pages, 6133 KB

Open AccessArticle

A Smart System for Continuous Sitting Posture Monitoring, Assessment, and Personalized Feedback

by David Faith Odesola, Janusz Kulon, Shiny Verghese, Adam Partlow and Colin Gibson

Sensors 2025, 25(18), 5610; https://doi.org/10.3390/s25185610 - 9 Sep 2025

Cited by 2 | Viewed by 5955

Abstract

Prolonged sitting and the adoption of unhealthy sitting postures have been a common issue generally seen among many adults and the working population in recent years. This alone has contributed to the alarming rise of various health issues, such as musculoskeletal disorders and a range of long-term health conditions. Hence, this study proposes the development of a novel smart-sensing chair system designed to analyze and provide actionable insights to help encourage better postural habits and promote well-being. The proposed system was equipped with two 32 × 32 pressure sensor mats, which were integrated into an office chair to facilitate the collection of postural data. Unlike traditional approaches that rely on generalized datasets collected from multiple healthy participants to train machine learning models, this study adopts a user-tailored methodology—collecting data from a single individual to account for their unique physiological characteristics and musculoskeletal conditions. The dataset was trained using five different machine learning models—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN)—to classify 19 distinct sitting postures. Overall, CNN achieved the highest accuracy, with 98.29%. To facilitate user engagement and support long-term behavior change, we developed SitWell—an intelligent postural feedback platform comprising both mobile and web applications. The platform’s core features include sitting posture classification, posture duration analytics, and sitting quality assessment. Additionally, the platform integrates OpenAI’s GPT-4o Large Language Model (LLM) to deliver personalized insights and recommendations based on users’ historical posture data. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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10 pages, 1071 KB

Open AccessArticle

Noninvasive Analysis of Biological Components Using Simplified Mid-Infrared Photothermal Deflection Spectroscopy

by Hiroto Ito, Saiko Kino and Yuji Matsuura

Sensors 2025, 25(14), 4368; https://doi.org/10.3390/s25144368 - 12 Jul 2025

Cited by 1 | Viewed by 1243

Abstract

We developed a photothermal deflection spectroscopy (PTDS) system for the noninvasive analysis of biological tissue. This system detects heat induced by irradiation with pulse-modulated mid-infrared light as the deflection of a probe laser. The probe light is incident on the sensing element horizontal with respect to its contact surface with the sample. This setup simplifies the optical alignment compared to conventional systems, which require the probe laser to be totally reflected at the prism contact surface and aligned with the point of mid-infrared light irradiation. In this study, we measured the PTDS spectra of biological samples to determine the characteristic features of their infrared absorption. We also compared the measurement reproducibility of two configurations: a horizontal optical path and a total reflection optical path. The horizontal optical path showed greater measurement reproducibility than the total reflection optical path when performing intermittent measurements on the wrist. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition)

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