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Advanced Sensing and Signal Processing Technologies for Medical Applications
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Advanced Sensing and Signal Processing Technologies for Medical Applications

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 4346

Special Issue Editor

Dr. Jordi Cusidó

E-Mail Website
Guest Editor
1.Data and Signal Processing Research Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain
2. Enginyeria de Projectes i de la Construcció EPC, Polytechnic University of Catalonia, 08028 Barcelona, Catalonia, Spain
Interests: sensors; medical sensors; IoT; wearables; turbines; renewable power; fault diagnosis

Special Issue Information

Dear Colleagues, 

The rapid advancement of Internet of Things (IoT) technologies, signal processing, and data algorithms is revolutionizing the healthcare and medical landscape. This Special Issue invites researchers to contribute novel research, methodologies, and applications in advanced data and signal processing technologies for medical purposes. This Special Issue focuses on innovations that leverage IoT, virtual sensors, and cutting-edge data algorithms to enhance patient monitoring, improve diagnosis accuracy, and optimize healthcare delivery systems. The goal is to explore the intersection of technology and medicine, fostering interdisciplinary approaches that address emerging challenges and pave the way for smarter, more efficient healthcare solutions. Topics of interest include, but are not limited to, virtual sensing in medical applications, AI-driven diagnostic systems, real-time patient monitoring, and secure data transmission in IoT healthcare ecosystems.

Dr. Jordi Cusidó
Guest Editor

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

  • IoT in healthcare
  • virtual sensors
  • advanced signal processing
  • patient monitoring
  • AI in medical diagnosis
  • real-time data analytics
  • predictive healthcare algorithms
  • wearable medical devices
  • secure data in IoT healthcare
  • smart healthcare solutions

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Published Papers (6 papers)

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Research

18 pages, 7212 KiB  
Article
Integrating Complex Permittivity Measurements with Histological Analysis for Advanced Tissue Characterization
by Sandra Lopez-Prades, Mónica Torrecilla-Vall-llossera, Mercedes Rus, Miriam Cuatrecasas and Joan M. O’Callaghan
Sensors 2025, 25(8), 2626; https://doi.org/10.3390/s25082626 - 21 Apr 2025
Viewed by 127
Abstract
We developed a measurement setup and protocol reliably relating complex permittivity measurements with tissue characterization and specific histological features. We measured 148 fresh human tissue samples across 14 tissue types at 51 frequencies ranging from 200 MHz to 20 GHz, using an open-ended [...] Read more.
We developed a measurement setup and protocol reliably relating complex permittivity measurements with tissue characterization and specific histological features. We measured 148 fresh human tissue samples across 14 tissue types at 51 frequencies ranging from 200 MHz to 20 GHz, using an open-ended coaxial slim probe. Tissue samples were collected using a punch biopsy, ensuring that the sampled area encompassed the region where complex permittivity measurements were performed. This approach minimized experimental uncertainty related to potential position-dependent variations in permittivity. Once measured, the samples were then formalin-fixed and paraffin-embedded (FFPE) to obtain histological slides for microscopic analysis of tissue features. We observed that complex permittivity values are strongly associated with key histological features, including fat content, necrosis, and fibrosis. Most tissue samples exhibiting these features could be differentiated from nominal values for that tissue type, even accounting for statistical variability and instrumental uncertainties. These findings demonstrate the potential of incorporating fast in situ complex permittivity for fresh tissue characterization in pathology workflows. Furthermore, our work lays the groundwork for enhancing databases where complex permittivity values are measured under histological control, enabling precise correlations between permittivity values, tissue characterization, and histological features. Full article
(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)
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19 pages, 1198 KiB  
Article
Assessing Vibrotactile Feedback Effects on Posture, Muscle Recruitment, and Cognitive Performance
by Demir Tuken, Ian Silva and Rachel V. Vitali
Sensors 2025, 25(8), 2416; https://doi.org/10.3390/s25082416 - 11 Apr 2025
Viewed by 332
Abstract
Musculoskeletal disorders are prevalent among medical professionals like dentists, who often maintain prolonged, ergonomically disadvantageous postures. This study aims to evaluate the feasibility and efficacy of a wearable sensor-based monitoring and feedback system designed to improve posture and evaluate muscle recruitment. Thirty-five healthy [...] Read more.
Musculoskeletal disorders are prevalent among medical professionals like dentists, who often maintain prolonged, ergonomically disadvantageous postures. This study aims to evaluate the feasibility and efficacy of a wearable sensor-based monitoring and feedback system designed to improve posture and evaluate muscle recruitment. Thirty-five healthy adults participated in a controlled experiment, performing a typing task under various postural conditions with and without haptic feedback. Surface electromyography sensors measured muscle activity in the upper trapezius and infraspinatus muscles, while inertial measurement units tracked spine orientation. The results indicated that haptic feedback significantly influenced muscle activity and posture. Feedback reduced deviations from the desired postures but increased muscle activity in certain conditions. Cognitive performance, measured by typing speed, decreased with feedback, suggesting a trade-off between maintaining posture and the performance of the task. These findings highlight the potential of haptic feedback in ergonomic interventions to mitigate MSDs. Future research should explore the long-term effects and optimize feedback mechanisms to balance posture correction and cognitive demands. Full article
(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)
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15 pages, 4513 KiB  
Article
A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging
by Arun Govindaiah, Tasin Bhuiyan, R. Theodore Smith, Mandip S. Dhamoon and Alauddin Bhuiyan
Sensors 2025, 25(6), 1917; https://doi.org/10.3390/s25061917 - 19 Mar 2025
Viewed by 294
Abstract
Stroke is a leading cause of death and disability in developed countries. We validated an AI-based prediction model for incident stroke using sensors such as fundus cameras and ophthalmoscopes for retinal images, along with socio-demographic data and traditional risk factors. The model was [...] Read more.
Stroke is a leading cause of death and disability in developed countries. We validated an AI-based prediction model for incident stroke using sensors such as fundus cameras and ophthalmoscopes for retinal images, along with socio-demographic data and traditional risk factors. The model was trained on a proprietary dataset of over 6500 participants, including 171 with 5-year incident strokes and 242 with 10-year incident strokes. The model provides separate 5-year and 10-year risk scores. The model was externally validated on the UK Biobank dataset (3000 subjects with 5-year incident strokes). Using retinal imaging, our models identified individuals with 5-year incident strokes with 80% sensitivity, 82% specificity, and an AUC of 0.83, and predicted 10-year incidents with 72% sensitivity, 78% specificity, and an AUC of 0.79. In comparison, for the 10-year model, the AUC for the Framingham score was 0.73, and the CHADS2 score was 0.74. On the Biobank external dataset, our 5-year model (without retinal features) demonstrated moderate but lower sensitivity (69.3%) and specificity (66.4%) compared to its performance on the proprietary dataset (with retinal features). Using a multi-ethnic dataset, we developed and validated a prediction model that improves stroke risk identification for 5-year and 10-year incidences by incorporating retinal features. Full article
(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)
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21 pages, 1680 KiB  
Article
Sensor-Based Assessment of Mental Fatigue Effects on Postural Stability and Multi-Sensory Integration
by Yao Sun, Yingjie Sun, Jia Zhang and Feng Ran
Sensors 2025, 25(5), 1470; https://doi.org/10.3390/s25051470 - 27 Feb 2025
Viewed by 640
Abstract
Objective: Mental fatigue (MF) induced by prolonged cognitive tasks poses significant risks to postural stability, yet its effects on multi-sensory integration remain poorly understood. Method: This study investigated how MF alters sensory reweighting and postural control in 27 healthy young males. A 45 [...] Read more.
Objective: Mental fatigue (MF) induced by prolonged cognitive tasks poses significant risks to postural stability, yet its effects on multi-sensory integration remain poorly understood. Method: This study investigated how MF alters sensory reweighting and postural control in 27 healthy young males. A 45 min incongruent Stroop task was employed to induce MF, validated via subjective Visual Analog Scale (VAS) scores and psychomotor vigilance tests. Postural stability was assessed under four sensory perturbation conditions (O-H: no interference; C-H: visual occlusion; O-S: proprioceptive perturbation; C-S: combined perturbations) using a Kistler force platform. Center of pressure (COP) signals were analyzed through time-domain metrics, sample entropy (SampEn), and Discrete Wavelet Transform (DWT) to quantify energy distributions across sensory-related frequency bands (visual: 0–0.1 Hz; vestibular: 0.1–0.39 Hz; cerebellar: 0.39–1.56 Hz; proprioceptive: 1.56–6.25 Hz). Results: MF significantly reduced proprioceptive energy contributions (p < 0.05) while increasing vestibular reliance under O-S conditions (p < 0.05). Time-domain metrics showed no significant changes in COP velocity or displacement, but SampEn decreased under closed-eye conditions (p < 0.001), indicating reduced postural adaptability. DWT analysis highlighted MF’s interaction with visual occlusion, altering cerebellar and proprioceptive energy dynamics (p < 0.01). Conclusion: These findings demonstrate that MF disrupts proprioceptive integration, prompting compensatory shifts toward vestibular and cerebellar inputs. The integration of nonlinear entropy and frequency-domain analyses advances methodological frameworks for fatigue research, offering insights into real-time sensor-based fatigue monitoring and balance rehabilitation strategies. This study underscores the hierarchical interplay of sensory systems under cognitive load and provides empirical evidence for optimizing interventions in high-risk occupational and clinical settings. Full article
(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)
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27 pages, 9283 KiB  
Article
A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram
by Omar M. T. Abdel Deen, Shou-Zen Fan and Jiann-Shing Shieh
Sensors 2025, 25(4), 1150; https://doi.org/10.3390/s25041150 - 13 Feb 2025
Viewed by 1107
Abstract
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), photoplethysmography (PPG), and electrocardiogram (ECG) signals to predict nociception. We collected [...] Read more.
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), photoplethysmography (PPG), and electrocardiogram (ECG) signals to predict nociception. We collected data from patients undergoing general anesthesia at two hospitals and developed and compared two deep learning models: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) network. Both models were trained on expert anesthesiologists’ assessments of nociception. We evaluated normalization strategies for offline and online usage and found that Min–Max normalization was most effective for our dataset. Our results demonstrate that the MLP model accurately captured nociceptive changes in response to painful surgical stimuli, whereas the LSTM model provided smoother predictions but with lower sensitivity to rapid changes. These findings underscore the potential of multimodal, deep learning-based solutions to improve real-time nociception monitoring in diverse clinical settings. Full article
(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)
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18 pages, 8911 KiB  
Article
Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM
by Jun Tang, Jie Chen, Miaojun Hu, Yao Hu, Zixi Zhang and Liuming Xiao
Sensors 2025, 25(1), 156; https://doi.org/10.3390/s25010156 - 30 Dec 2024
Cited by 1 | Viewed by 1292
Abstract
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical [...] Read more.
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical research, more and more evidence suggests that dynamic functional connectivity analysis can more comprehensively reveal the complex and variable characteristics of brain networks and their underlying mechanisms, thus providing more solid scientific support for computer-aided diagnosis of ASD. To overcome the lack of time-scale information in static functional connectivity analysis, in this paper, we proposes an innovative GNN-LSTM model, which combines the advantages of long short-term memory (LSTM) and graph neural networks (GNNs). The model captures the spatial features in fMRI data by GNN and aggregates the temporal information of dynamic functional connectivity using LSTM to generate a more comprehensive spatio-temporal feature representation of fMRI data. Further, a dynamic graph pooling method is proposed to extract the final node representations from the dynamic graph representations for classification tasks. To address the variable dependence of dynamic feature connectivity on time scales, the model introduces a jump connection mechanism to enhance information extraction between internal units and capture features at different time scales. The model achieves remarkable results on the ABIDE dataset, with accuracies of 80.4% on the ABIDE I and 79.63% on the ABIDE II, which strongly demonstrates the effectiveness and potential of the model for ASD detection. This study not only provides new perspectives and methods for computer-aided diagnosis of ASD but also provides useful references for research in related fields. Full article
(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)
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