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Search Results (786)

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Keywords = medical of things

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19 pages, 1103 KiB  
Article
LAMT: Lightweight and Anonymous Authentication Scheme for Medical Internet of Things Services
by Hyang Jin Lee, Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee and Dongho Won
Sensors 2025, 25(3), 821; https://doi.org/10.3390/s25030821 (registering DOI) - 30 Jan 2025
Abstract
Medical Internet of Things (IoT) systems can be used to monitor and treat patient health conditions. Security and privacy issues in medical IoT services are more important than those in any other IoT-enabled service. Therefore, various mutual authentication and key-distribution schemes have been [...] Read more.
Medical Internet of Things (IoT) systems can be used to monitor and treat patient health conditions. Security and privacy issues in medical IoT services are more important than those in any other IoT-enabled service. Therefore, various mutual authentication and key-distribution schemes have been proposed for secure communication in medical IoT services. We analyzed Hu et al.’s scheme and found that an attacker can impersonate legitimate sensor nodes and generate illegitimate session keys using the information stored in the sensor node and the information transmitted over the public channel. To overcome these vulnerabilities, we propose a scheme that utilizes physically unclonable functions to ensure a secure session key distribution and increase the computational efficiency of resource-limited sensor nodes. In addition, the proposed scheme enhances privacy protection using pseudonyms, which we prove using a formal security analysis tool, ProVerif 2.05. Full article
(This article belongs to the Special Issue Trustless Biometric Sensors and Systems)
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34 pages, 2250 KiB  
Article
Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data
by Yoosef B. Abushark, Shabbir Hassan and Asif Irshad Khan
Sensors 2025, 25(3), 731; https://doi.org/10.3390/s25030731 - 25 Jan 2025
Viewed by 183
Abstract
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as [...] Read more.
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as maintaining a triad of confidentiality, integrity, and availability (CIA) and securing data transmission. This paper proposes a novel AdaBoost support vector machine (ASVM) based on the grey wolf optimization and international data encryption algorithm (ASVM-based GWO-IDEA) to secure medical data in an IoT-enabled healthcare system. The primary objective of this work was to prevent possible cyberattacks, unauthorized access, and tampering with the security of such healthcare systems. The proposed scheme encodes the healthcare data before transmitting them, protecting them from unauthorized access and other network vulnerabilities. The scheme was implemented in Python, and its efficiency was evaluated using a Kaggle-based public healthcare dataset. The performance of the model/scheme was evaluated with existing strategies in the context of effective security parameters, such as the confidentiality rate and throughput. When using the suggested methodology, the data transmission process was improved and achieved a high throughput of 97.86%, an improved resource utilization degree of 98.45%, and a high efficiency of 93.45% during data transmission. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
19 pages, 3007 KiB  
Review
Cellulose-Based Electrochemical Sensors
by Muhammad Sheraz, Xiao-Feng Sun, Adeena Siddiqui, Yongke Wang, Sihai Hu and Ran Sun
Sensors 2025, 25(3), 645; https://doi.org/10.3390/s25030645 - 22 Jan 2025
Viewed by 321
Abstract
Among the most promising areas of research, cellulose-based electrochemical sensors stand out for their intrinsic properties such as abundance, biocompatibility, and versatility. This review is concerned with the integration and application of cellulose-derived materials in electrochemical sensors, pointing out improvements in sensitivity, selectivity, [...] Read more.
Among the most promising areas of research, cellulose-based electrochemical sensors stand out for their intrinsic properties such as abundance, biocompatibility, and versatility. This review is concerned with the integration and application of cellulose-derived materials in electrochemical sensors, pointing out improvements in sensitivity, selectivity, stability, and functionality for a wide variety of applications. The most relevant developments on cellulose-based sensors have been concentrated on nanocellulose composite synthesis, advanced cellulose modification, and the successful embedding in wearable technologies, medical diagnostics, and environmental monitoring. Considering these, it is worth mentioning that significant challenges still need to be overcome regarding the scalability of production, selectivity improvement, and long-term stability under real operational conditions. Future research efforts will concern the union of cellulose-based sensors with the Internet of Things (IoT) and artificial intelligence (AI) toward wiser and more sustainable health and environmental solutions. Correspondingly, this work puts cellulose in the front line among the most perspective materials for enabling the development of eco-friendly and high-performance sensing technologies. Full article
(This article belongs to the Special Issue Wearable and Implantable Electrochemical Sensors)
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21 pages, 1339 KiB  
Article
Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments
by Easa Alalwany, Bader Alsharif, Yazeed Alotaibi, Abdullah Alfahaid, Imad Mahgoub and Mohammad Ilyas
Sensors 2025, 25(3), 624; https://doi.org/10.3390/s25030624 - 22 Jan 2025
Viewed by 334
Abstract
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This [...] Read more.
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services. Full article
(This article belongs to the Special Issue Sensors in mHealth Applications)
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25 pages, 2878 KiB  
Review
Optimizing Spectral Utilization in Healthcare Internet of Things
by Adeel Iqbal, Ali Nauman, Yazdan Ahmad Qadri and Sung Won Kim
Sensors 2025, 25(3), 615; https://doi.org/10.3390/s25030615 - 21 Jan 2025
Viewed by 361
Abstract
The mainstream adoption of Internet of Things (IoT) devices for health and lifestyle tracking has revolutionized health monitoring systems. Sixth-generation (6G) cellular networks enable IoT healthcare services to reduce the pressures on already resource-constrained facilities, leveraging enhanced ultra-reliable low-latency communication (eURLLC) to make [...] Read more.
The mainstream adoption of Internet of Things (IoT) devices for health and lifestyle tracking has revolutionized health monitoring systems. Sixth-generation (6G) cellular networks enable IoT healthcare services to reduce the pressures on already resource-constrained facilities, leveraging enhanced ultra-reliable low-latency communication (eURLLC) to make sure critical health data are transmitted with minimal delay. Any delay or information loss can result in serious consequences, making spectrum availability a crucial bottleneck. This study systematically identifies challenges in optimizing spectrum utilization in healthcare IoT (H-IoT) networks, focusing on issues such as dynamic spectrum allocation, interference management, and prioritization of critical medical devices. To address these challenges, the paper highlights emerging solutions, including artificial intelligence-based spectrum management, edge computing integration, and advanced network architectures such as massive multiple-input multiple-output (mMIMO) and terahertz (THz) communication. We identify gaps in the existing methodologies and provide potential research directions to enhance the efficiency and reliability of eURLLC in healthcare environments. These findings offer a roadmap for future advancements in H-IoT systems and form the basis of our recommendations, emphasizing the importance of tailored solutions for spectrum management in the 6G era. Full article
(This article belongs to the Special Issue Sensors and Smart City)
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18 pages, 1863 KiB  
Article
Fuzzy Delphi and DEMATEL Approaches in Sustainable Wearable Technologies: Prioritizing User-Centric Design Indicators
by Chin-Wen Liao, Kai-Chao Yao, Ching-Hsin Wang, Hsi-Huang Hsieh, I-Chi Wang, Wei-Sho Ho, Wei-Lun Huang and Shu-Hua Huang
Appl. Sci. 2025, 15(1), 461; https://doi.org/10.3390/app15010461 - 6 Jan 2025
Viewed by 625
Abstract
The rapid advancement of intelligent technologies, including sensing devices, artificial intelligence, and the Internet of Things, has significantly accelerated the progress in industrial technology, particularly within the medical enterprise sector. Wearable innovations for health management have introduced novel approaches to physiological monitoring and [...] Read more.
The rapid advancement of intelligent technologies, including sensing devices, artificial intelligence, and the Internet of Things, has significantly accelerated the progress in industrial technology, particularly within the medical enterprise sector. Wearable innovations for health management have introduced novel approaches to physiological monitoring and early disease detection, contributing to an improved quality of life. In the context of sustainable development, wearable devices demonstrate considerable potential for supporting long-term healthcare solutions, particularly in the post-pandemic era, where the demand for smart health solutions continues to rise. This study aims to identify critical product design indicators for wearable devices that align with sustainable health management goals. Utilizing expert questionnaires and employing a combination of the Fuzzy Delphi Method and the DEMATEL-based Analytic Network Process (ANP), this research systematically evaluates the key factors influencing wearable device design. The findings highlight three primary aspects, six criteria, and 16 design indicators, with pivotal factors including “Compatibility”, “Foresight”, “Integration”, “Comfort”, “Appearance”, “Customization”, and “Intelligence”. These indicators provide a comprehensive framework for developing wearable devices that address diverse user needs while promoting individual well-being and sustainable health management. This study offers valuable insights into the design and development of wearable devices that support sustainable healthcare practices, advance social responsibility, and strengthen preventive care initiatives. Full article
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22 pages, 11189 KiB  
Article
VUF-MIWS: A Visible and User-Friendly Watermarking Scheme for Medical Images
by Chia-Chen Lin, Yen-Heng Lin, En-Ting Chu, Wei-Liang Tai and Chun-Jung Lin
Electronics 2025, 14(1), 122; https://doi.org/10.3390/electronics14010122 - 30 Dec 2024
Viewed by 510
Abstract
The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, [...] Read more.
The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications. Full article
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19 pages, 2527 KiB  
Article
The Use of Voice Control in 3D Medical Data Visualization Implementation, Legal, and Ethical Issues
by Miklos Vincze, Bela Molnar and Miklos Kozlovszky
Information 2025, 16(1), 12; https://doi.org/10.3390/info16010012 - 30 Dec 2024
Viewed by 462
Abstract
Voice-controlled devices are becoming increasingly common in our everyday lives as well as in medicine. Whether it is our smartphones, with voice assistants that make it easier to access functions, or IoT (Internet of Things) devices that let us control certain areas of [...] Read more.
Voice-controlled devices are becoming increasingly common in our everyday lives as well as in medicine. Whether it is our smartphones, with voice assistants that make it easier to access functions, or IoT (Internet of Things) devices that let us control certain areas of our home with voice commands using sensors and different communication networks, or even medical robots that can be controlled by a doctor with voice instructions. Over the last decade, systems using voice control have made great progress, both in terms of accuracy of voice processing and usability. The topic of voice control is intertwined with the application of artificial intelligence (AI), as the mapping of spoken commands into written text and their understanding is mostly conducted by some kind of trained AI model. Our research had two objectives. The first was to design and develop a system that enables doctors to evaluate medical data in 3D using voice control. The second was to describe the legal and ethical issues involved in using AI-based solutions for voice control. During our research, we created a voice control module for an existing software called PathoVR, using a model taught by Google to interpret the voice commands given by the user. Our research, presented in this paper, can be divided into two parts. In the first, we have designed and developed a system that allows the user to evaluate 3D pathological medical serial sections using voice commands. In contrast, in the second part of our research, we investigated the legal and ethical issues that may arise when using voice control in the medical field. In our research, we have identified legal and ethical barriers to the use of artificial intelligence in voice control, which need to be answered in order to make this technology part of everyday medicine. Full article
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence 2024)
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18 pages, 678 KiB  
Article
Privacy-Preserving Federated Learning-Based Intrusion Detection System for IoHT Devices
by Fatemeh Mosaiyebzadeh, Seyedamin Pouriyeh, Meng Han, Liyuan Liu, Yixin Xie, Liang Zhao and Daniel Macêdo Batista
Electronics 2025, 14(1), 67; https://doi.org/10.3390/electronics14010067 - 27 Dec 2024
Viewed by 633
Abstract
In recent years, Internet of Healthcare Things (IoHT) devices have attracted significant attention from computer scientists, healthcare professionals, and patients. These devices enable patients, especially in areas without access to hospitals, to easily record and transmit their health data to medical staff via [...] Read more.
In recent years, Internet of Healthcare Things (IoHT) devices have attracted significant attention from computer scientists, healthcare professionals, and patients. These devices enable patients, especially in areas without access to hospitals, to easily record and transmit their health data to medical staff via the Internet. However, the analysis of sensitive health information necessitates a secure environment to safeguard patient privacy. Given the sensitivity of healthcare data, ensuring security and privacy is crucial in this sector. Federated learning (FL) provides a solution by enabling collaborative model training without sharing sensitive health data with third parties. Despite FL addressing some privacy concerns, the privacy of IoHT data remains an area needing further development. In this paper, we propose a privacy-preserving federated learning framework to enhance the privacy of IoHT data. Our approach integrates federated learning with ϵ-differential privacy to design an effective and secure intrusion detection system (IDS) for identifying cyberattacks on the network traffic of IoHT devices. In our FL-based framework, SECIoHT-FL, we employ deep neural network (DNN) including convolutional neural network (CNN) models. We assess the performance of the SECIoHT-FL framework using metrics such as accuracy, precision, recall, F1-score, and privacy budget (ϵ). The results confirm the efficacy and efficiency of the framework. For instance, the proposed CNN model within SECIoHT-FL achieved an accuracy of 95.48% and a privacy budget (ϵ) of 0.34 when detecting attacks on one of the datasets used in the experiments. To facilitate the understanding of the models and the reproduction of the experiments, we provide the explainability of the results by using SHAP and share the source code of the framework publicly as free and open-source software. Full article
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21 pages, 2042 KiB  
Article
EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks
by Sakshi Patni and Joohyung Lee
Future Internet 2025, 17(1), 2; https://doi.org/10.3390/fi17010002 - 25 Dec 2024
Viewed by 459
Abstract
The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data [...] Read more.
The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare. Full article
(This article belongs to the Special Issue Edge Intelligence: Edge Computing for 5G and the Internet of Things)
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54 pages, 5089 KiB  
Review
The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection
by Tarek Berghout
J. Imaging 2025, 11(1), 2; https://doi.org/10.3390/jimaging11010002 - 24 Dec 2024
Viewed by 878
Abstract
Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The [...] Read more.
Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019–2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics. Full article
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17 pages, 662 KiB  
Article
A Self-Sovereign Identity–Blockchain-Based Model Proposal for Deep Digital Transformation in the Healthcare Sector
by Luisanna Cocco and Roberto Tonelli
Future Internet 2024, 16(12), 473; https://doi.org/10.3390/fi16120473 - 19 Dec 2024
Viewed by 711
Abstract
The acceleration of the digital transformation process imposed by the pandemic in all the countries of the European Union, and in all sectors, has given way to a revolution that up until a couple of years ago would have been impossible even to [...] Read more.
The acceleration of the digital transformation process imposed by the pandemic in all the countries of the European Union, and in all sectors, has given way to a revolution that up until a couple of years ago would have been impossible even to imagine. Digital innovation has become a factor of competitiveness in all sectors. In this new scenario that has come to be, the Blockchain technology, the Self-Sovereign Identity paradigm, Internet of Things, and, in general, the new technologies that will emerge, will constitute enhancers of competitiveness and will have to aim for interoperability. In this context, this article develops and presents a model proposal in the healthcare field that aims to highlight how the combination of the Blockchain technology and the Self-Sovereign Identity paradigm restores full control over a person’s identity and information, while ensuring the integrity of all medical reports, enabling secure communications between personal medical devices and patient/doctor applications on devices exploiting peer Decentralized Identifiers and ensuring data privacy, exploiting Zero Knowledge Proofs. The proposal relies on the Veramo platform, treating all medical reports as verifiable credentials and storing them in digital wallets owned by the patient. The article concludes by presenting a prototype designed and implemented for managing medication prescriptions, their issuance, and their exchange. Full article
(This article belongs to the Section Smart System Infrastructure and Applications)
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21 pages, 7720 KiB  
Article
HPDH-MI: A High Payload Data Hiding Technique for Medical Images Based on AMBTC
by Chia-Chen Lin, Mostafa Mirzaei, En-Ting Chu and Chen Chih Cheng
Symmetry 2024, 16(12), 1634; https://doi.org/10.3390/sym16121634 - 10 Dec 2024
Cited by 1 | Viewed by 688
Abstract
In the realm of electronic health (eHealth) services powered by the Internet of Things (IoT), vast quantities of medical images and visualized electronic health records collected by IoT devices must be transmitted daily. Given the sensitive nature of medical information, ensuring the security [...] Read more.
In the realm of electronic health (eHealth) services powered by the Internet of Things (IoT), vast quantities of medical images and visualized electronic health records collected by IoT devices must be transmitted daily. Given the sensitive nature of medical information, ensuring the security of transmitted health data is paramount. To address this critical concern, this paper introduces a novel data hiding algorithm tailored for Absolute Moment Block Truncation Coding (AMBTC) in medical images, named HPDH-MI (High Payload Data Hiding for Medical Images). The proposed method embeds secret data into the AMBTC compression code inconspicuously to avoid detection by malicious users. It achieves this by first classifying AMBTC compressed blocks into four categories—flat, smooth, complex I, and complex II—using three predetermined thresholds. A 1-bit indicator, based on the proposed grouping strategy, facilitates efficient and effective block classification. A data embedding strategy is applied to each block type, focusing on block texture and taking into account the symmetric features of the pixels within the block. This approach achieves a balance between data hiding capacity, image quality, and embedding efficiency. Experimental evaluations highlight the superior performance of HPDH-MI. When tested on medical images from the Osirix database, the method achieves an average image quality of 31.22 dB, a payload capacity of 225,911 bits, and an embedding efficiency of 41.78%. These results demonstrate that the HPDH-MI method not only significantly increases the payload for concealing secret data in AMBTC compressed medical images but also maintains high image quality and embedding efficiency. This makes it a promising solution for secure data transmission in telemedicine, addressing the challenges of limited bandwidth while enhancing steganographic capabilities in eHealth applications. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in 5G Networks)
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20 pages, 6078 KiB  
Article
A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics
by Yasamin Moghbelan, Alfonso Esposito, Ivan Zyrianoff, Giulia Spaletta, Stefano Borgo, Claudio Masolo, Fabiana Ballarin, Valeria Seidita, Roberto Toni, Fulvio Barbaro, Giusy Di Conza, Francesca Pia Quartulli and Marco Di Felice
Appl. Sci. 2024, 14(24), 11489; https://doi.org/10.3390/app142411489 - 10 Dec 2024
Viewed by 804
Abstract
The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context [...] Read more.
The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects. Full article
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25 pages, 2970 KiB  
Article
An Android-Based Internet of Medical Things Adaptive User Authentication and Authorization Model for the Elderly
by Prudence M. Mavhemwa, Marco Zennaro, Philibert Nsengiyumva and Frederic Nzanywayingoma
J. Cybersecur. Priv. 2024, 4(4), 993-1017; https://doi.org/10.3390/jcp4040046 - 2 Dec 2024
Viewed by 1053
Abstract
Globally, 77% of the elderly aged 65 and above suffer from multiple chronic ailments, according to recent research. However, several barriers within the healthcare system in the developing world hinder the adoption of home-based patient management, hence the need for the IoMT, whose [...] Read more.
Globally, 77% of the elderly aged 65 and above suffer from multiple chronic ailments, according to recent research. However, several barriers within the healthcare system in the developing world hinder the adoption of home-based patient management, hence the need for the IoMT, whose application raises security concerns, particularly in authentication. Several authentication techniques have been proposed; however, they lack a balance of security and usability. This paper proposes a Naive Bayes based adaptive user authentication app that calculates the risk associated with a login attempt on an Android device for elderly users, using their health conditions, risk score, and available authenticators. This authentication technique guided by the MAPE-KHMT framework makes use of embedded smartphone sensors. Results indicate a 100% and 98.6% accuracy in usable-security metrics, while cross-validation and normalization results also support the accuracy, efficiency, effectiveness, and usability of our model with room for scaling it up without computational costs and generalizing it beyond SSA. The post-deployment evaluation also confirms that users found the app usable and secure. A few areas need further refinement to improve the accuracy, usability, security, and acceptance but the model shows potential to improve users’ compliance with IoMT security, thereby promoting the attainment of SDG3. Full article
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