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Innovative Sensors and IoT for AI-Enabled Smart Healthcare
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Innovative Sensors and IoT for AI-Enabled Smart Healthcare

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 7526

Special Issue Editors

Dr. Chan Hwang See

E-Mail Website
Guest Editor
School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: optimization and learning; acoustics; smart cities; sensors; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Arfan Ghani

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, School of Engineering, American University of Ras al Khaimah, Ras al Khaimah P.O. Box 10021, United Arab Emirates
Interests: neural engineering; low power sensors/IoT devices; hardware accelerators; FPGAs/ASIC; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Simeon Keates

E-Mail Website
Guest Editor
College Lane, University of Chichester, Chichester P19 6PE, UK
Interests: artificial intelligence; human–computer interaction; building robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intersection of the innovative sensors, Internet of Things (IoT) and healthcare has ushered in a new era of medical advancements, revolutionizing patient care, data-driven diagnosis, and health monitoring. This special issue aims to explore the latest developments and innovations in the field of IoT and Smart Healthcare, focusing on cutting-edge research and technologies that enable the seamless integration of IoT devices into the healthcare ecosystem. As we move into a more connected and data-centric world, the importance of efficient and secure healthcare solutions cannot be overstated. This special issue will provide a platform for researchers, engineers, and practitioners to share their expertise and insights in this rapidly evolving domain.

In recent years, the healthcare industry has seen a dramatic transformation with the adoption of IoT technologies. The COVID-19 pandemic accelerated the need for remote healthcare monitoring and telemedicine, underlining the urgency of this special issue. Furthermore, advancements in Artificial Intelligence (AI), hardware accelerators, bendable electronics, and reconfigurable low-power IoT devices are crucial components of smart healthcare systems. These technologies enable real-time data analysis, personalized treatment, and efficient resource management, ultimately improving patient outcomes and reducing healthcare costs.

Dr. Chan Hwang See
Dr. Arfan Ghani
Prof. Dr. Simeon Keates
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 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

  • machine learning and deep learning applications in disease detection, prediction, and personalized treatment
  • RFID Sensors for the IoT healthcare system
  • neuromorphic healthcare sensors Thin film, bendable electronics for healthcare monitoring FPGA and GPU-based accelerators for real-time data processing in medical devices
  • sensors and devices for continuous vital sign monitoring and health assessment
  • smart drug administration and dosage control
  • data encryption, authentication, and privacy in healthcare IoT
  • telehealth solutions for remote patient care and disease management
  • big data analytics and data-driven decision-making in healthcare
  • IoT integration in medical imaging modalities for improved diagnostics
  • building scalable and resilient IoT architectures for healthcare applications
  • user-friendly interfaces for patients and healthcare professionals
  • addressing the ethical and regulatory challenges of IoT in healthcare

 

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Published Papers (7 papers)

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Research

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18 pages, 1381 KiB  
Article
PET Foils Functionalized with Reactive Copolymers as Adaptable Microvolume ELISA Spot Array Platforms for Multiplex Serological Analysis of SARS-CoV-2 Infections
by Sylwia Pniewska, Marcin Drozd, Alessandro Mussida, Dario Brambilla, Marcella Chiari, Waldemar Rastawicki and Elżbieta Malinowska
Sensors 2024, 24(23), 7766; https://doi.org/10.3390/s24237766 - 4 Dec 2024
Viewed by 221
Abstract
Microvolume ELISA platforms have become vital in diagnostics for their high-throughput capabilities and minimal sample requirements. High-quality substrates with advanced surface properties are essential for these applications. They enable both efficient biomolecule immobilization and antifouling properties, which are critical for assay sensitivity and [...] Read more.
Microvolume ELISA platforms have become vital in diagnostics for their high-throughput capabilities and minimal sample requirements. High-quality substrates with advanced surface properties are essential for these applications. They enable both efficient biomolecule immobilization and antifouling properties, which are critical for assay sensitivity and specificity. This study presents PET-based microvolume ELISA spot arrays coated with amine- and DBCO-reactive copolymers MCP-2 and Copoly Azide. The platforms were designed for the sensitive and specific detection of specific antibodies such as COVID-19 biomarkers. Supporting robust attachment of the SARS-CoV-2 nucleoprotein (NP), these arrays outperform traditional approaches. It was demonstrated that covalent attachment methods proved more efficient than passive adsorption, together with the reduction of non-specific binding. Analytical performance was verified with classical ELISA and real-time Surface Plasmon Resonance (SPR) analysis. It enables sensitive detection of IgG and IgA antibodies, including IgG subclasses, in human serum. Clinically, the platform achieved 100.0% sensitivity and 92.9% specificity for anti-NP antibody detection in COVID-19-positive and negative samples. Additionally, DNA-directed immobilization extended the platform’s utility to multiplex serological measurements. These findings underscore the potential of PET-based microvolume ELISA arrays as scalable, high-throughput diagnostic tools suitable for detecting multiple biomarkers in a single assay and easily integrated into microfluidic devices. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
21 pages, 3342 KiB  
Article
Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring
by Rinaldi Anwar Buyung, Alhadi Bustamam and Muhammad Remzy Syah Ramazhan
Sensors 2024, 24(23), 7537; https://doi.org/10.3390/s24237537 - 26 Nov 2024
Viewed by 367
Abstract
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood [...] Read more.
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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13 pages, 1058 KiB  
Article
Subjective and Objective Day-to-Day Performance Measures of People with Essential Tremor
by Navit Roth, Adham Salih and Sara Rosenblum
Sensors 2024, 24(15), 4854; https://doi.org/10.3390/s24154854 - 26 Jul 2024
Viewed by 633
Abstract
This paper aims to map the daily functional characteristics of people diagnosed with essential tremor (ET) based on their subjective self-reports. In addition, we provide objective measurements of a cup-drinking task. This study involved 20 participants diagnosed with ET who completed the Columbia [...] Read more.
This paper aims to map the daily functional characteristics of people diagnosed with essential tremor (ET) based on their subjective self-reports. In addition, we provide objective measurements of a cup-drinking task. This study involved 20 participants diagnosed with ET who completed the Columbia University Assessment of Disability in Essential Tremor (CADET) questionnaire that included five additional tasks related to digital equipment operation we wrote. Participants also described task-performance modifications they implemented. To create objective personal performance profiles, they performed a cup-drinking task while being monitored using a sensor measurement system. The CADET’s subjective self-report results indicate that the most prevalent tasks participants reported as having difficulty with or requiring modifications were writing, threading a needle, carrying a cup, using a spoon, pouring, and taking a photo or video on a mobile phone. Analysis of participants’ modifications revealed that holding the object with two hands or with one hand supporting the other were the most prevalent types. No significant correlation was found between the CADET total scores and the cup drinking objective measures. Capturing patients’ perspectives on their functional disability, alongside objective performance measures, is envisioned to contribute to the development of custom-tailored interventions aligned with individual profiles, i.e., patient-based/smart healthcare. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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18 pages, 3555 KiB  
Article
Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks
by Arvin Razavi, Mikael Forsman and Farhad Abtahi
Sensors 2024, 24(13), 4173; https://doi.org/10.3390/s24134173 - 27 Jun 2024
Viewed by 1149
Abstract
Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry [...] Read more.
Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75–0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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12 pages, 3876 KiB  
Article
SPR and Double Resonance LPG Biosensors for Helicobacter pylori BabA Antigen Detection
by Georgi Dyankov, Tinko Eftimov, Evdokiya Hikova, Hristo Najdenski, Vesselin Kussovski, Petia Genova-Kalou, Vihar Mankov, Hristo Kisov, Petar Veselinov, Sanaz Shoar Ghaffari, Mila Kovacheva-Slavova, Borislav Vladimirov and Nikola Malinowski
Sensors 2024, 24(7), 2118; https://doi.org/10.3390/s24072118 - 26 Mar 2024
Cited by 3 | Viewed by 1289
Abstract
Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance [...] Read more.
Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance in the study of H. pylori outer membrane proteins and their identified receptors. In the present study we assess the way in which outer membrane protein–cell receptor reactions are applicable in establishing a reliable diagnosis. Herein, as well as in other previous studies of ours, we explore the reliability of the binding reaction between the best characterized H. pylori adhesin BabA and its receptor, the blood antigen Leb. For the purpose we developed surface plasmon resonance (SPR) and double resonance long period grating (DR LPG) biosensors based on the BabA–Leb binding reaction for diagnosing H. pylori infection. In SPR detection, the sensitivity was estimated at 3000 CFU/mL—a much higher sensitivity than that of the RUT test. The DR LPG biosensor proved to be superior in terms of accuracy and sensitivity—concentrations as low as 102 CFU/mL were detected. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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18 pages, 2728 KiB  
Article
Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels
by Adam Wawerski, Barbara Siemiątkowska, Michał Józwik, Bartłomiej Fajdek and Małgorzata Partyka
Sensors 2024, 24(4), 1306; https://doi.org/10.3390/s24041306 - 18 Feb 2024
Cited by 2 | Viewed by 1560
Abstract
This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is [...] Read more.
This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm’s effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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Review

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24 pages, 1845 KiB  
Review
Unveiling Colorectal Cancer Biomarkers: Harnessing Biosensor Technology for Volatile Organic Compound Detection
by Rebecca Golfinopoulou, Kyriaki Hatziagapiou, Sophie Mavrikou and Spyridon Kintzios
Sensors 2024, 24(14), 4712; https://doi.org/10.3390/s24144712 - 20 Jul 2024
Viewed by 1422
Abstract
Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such [...] Read more.
Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring. The present review is a comprehensive presentation of the current state-of-the-art VOC-based CRC diagnostics, with a specific focus on recent advancements in biosensor design and application. Among them, breath-based chromatography pattern analysis and sampling techniques are overviewed, along with nanoparticle-based optical and electrochemical biosensor approaches. Limitations of the currently available technologies are also discussed with an outlook for improvement in combination with big data analytics and advanced instrumentation, as well as expanding the scope and specificity of CRC-related volatile biomarkers. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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