Relevant bibliographies by topics / Internet of Medical Things (IoMT)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Internet of Medical Things (IoMT)'

Author: Grafiati
Published: 18 November 2024

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Journal articles on the topic "Internet of Medical Things (IoMT)"

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Nishad, Dipesh Kumar, and Diwakar R. Tripathi. "Internet of Medical Things (IoMT): Applications and Challenges." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (December 15, 2020): 2885–89. http://dx.doi.org/10.61841/turcomat.v11i3.14654.

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The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling remote patient monitoring, telemedicine, smart healthcare devices, and health data analytics. This paper provides a comprehensive review of the applications, benefits, challenges, and future trends of IoMT in healthcare. The applications of IoMT, including remote patient monitoring and telemedicine, have the potential to improve patient outcomes, enhance healthcare efficiency, and reduce costs. However, the implementation of IoMT is hindered by security and privacy concerns, interoperability issues, regulatory challenges, and data management complexities. Future trends of IoMT, such as the integration of artificial intelligence and machine learning, blockchain technology, IoMT ecosystem integration, and personalized medicine, promise to further enhance its capabilities and impact in healthcare. By addressing these challenges and embracing these trends, healthcare organizations can harness the full potential of IoMT to create a more connected, efficient, and patient-centric healthcare system.
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Kjwan, Alyaa Ali Hameed, and Omar Hasan Mohammad. "Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things." April-May 2024, no. 43 (April 1, 2024): 38–52. http://dx.doi.org/10.55529/ijrise.43.38.52.

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The Internet of Things refers to physical items, which are equipped with software, sensors, computing power, and other technologies, and that communicate with other electronic devices and systems over communication networks or the Internet. A collection of medical devices and software programmes known as the Internet of Medical Things (IoMT) link to healthcare networks via internet computing. Machine-to-machine communication, which is the foundation of IoMT, is made feasible by medical equipment that includes Wi-Fi. IoMT devices have the ability to analyse and store collected data by connecting to cloud services. IoMT is a different moniker for IoT in healthcare. Since data is transferred via the internet and the IoMT creates a lot of data, privacy concerns are important. The vast volume of data produced by IoMT devices calls for big data processing, and federated learning tackles privacy issues as a way to overcome these difficulties. The big data health care framework for IoMT is discussed in this article. It is built on federated learning.
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Taherdoost, Hamed. "Blockchain-Based Internet of Medical Things." Applied Sciences 13, no. 3 (January 18, 2023): 1287. http://dx.doi.org/10.3390/app13031287.

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IoMT sensor nodes, Internet of Things (IoT) wearable medical equipment, healthcare facilities, patients, and insurance firms are all increasingly being included in IoMT systems. Therefore, it is difficult to create a blockchain design for such systems, since scalability is among the most important aspects of blockchain technology. This realization prompted us to comprehensively analyze blockchain-based IoMT solutions developed in English between 2017 and 2022. This review incorporates the theoretical underpinnings of a large body of work published in highly regarded academic journals over the past decade, to standardize evaluation methods and fully capture the rapidly developing blockchain space. This study categorizes blockchain-enabled applications across various industries such as information management, privacy, healthcare, business, and supply chains according to a structured, systematic evaluation, and thematic content analysis of the literature that is already identified. The gaps in the literature on the topic have also been highlighted, with a special focus on the restrictions posed by blockchain technology and the knock-on effects that such restrictions have in other fields. Based on these results, several open research questions and potential avenues for further investigation that are likely to be useful to academics and professionals alike are pinpointed.
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Gaurav, Akshat, Konstantinos Psannis, and Dragan Peraković. "Security of Cloud-Based Medical Internet of Things (MIoTs)." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 2022): 1–16. http://dx.doi.org/10.4018/ijssci.285593.

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In this digital era expectations for medical quality have increased. As the number of patients continues to increase, conventional health care methods are having to deal with new complications. In light of these observations, researchers suggested a hybrid combination of conventional health care methods with IoT technology and develop MIoT. The goal of IoMT is to ensure that patients can respond more effectively and efficiently to their treatment. But preserving user privacy is a critical issue when it comes to collecting and handling highly sensitive personal health data. However, IoMTs have limited processing power; hence, they can only implement minimal security techniques. Consequently, throughout the health data transfer through MIoT, patient’s data is at risk of data leakage. This manuscript per the authors emphasizes the need of implementing suitable security measures to increase the IoMT's resilience to cyberattacks. Additionally, this manuscript per the authors discusses the main security and privacy issues associated with IoMT and provide an overview of existing techniques.
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Siddiq, Abdulrahman I. "A Comprehensive Review of the Internet of Medical Things in Healthcare." International Journal of Electrical and Electronic Engineering & Telecommunications 13, no. 6 (2024): 415–26. http://dx.doi.org/10.18178/ijeetc.13.6.415-426.

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The continuous and rapid development in Internet of Medical Things (IoMT) technologies has led to the expansion of their applications in the field of healthcare and to increased interest in developing their technologies for the purpose of improving their performance in light of the challenges facing their practical application. This paper aims to provide a comprehensive up-to-date review of the literature on IoMT and telemedicine. The study discusses the growing interest in the Internet of Things (IoT) in healthcare, emphasizing the IoMT. It highlights the significance of the IoMT in intelligent healthcare systems, particularly in pandemics like COVID-19, where remote monitoring becomes crucial. This study examines the utilization of the IoMT systems, emphasizing on the application of sensors in healthcare industry. Furthermore, it highlights the significance of cloud computing, communication technologies, artificial intelligence, and Blockchain in IoMT. The challenges related to accepting IoMT in practice are explored; including data security, integration of protocols, data overload, accuracy and cost. Overall, the analyzed literature confirms that IoMT-based healthcare systems demonstrate significant potential in delivering comprehensive surveillance and immediate monitoring through public catastrophes.
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Cassinadane, Ananda Vayaravel, Akshaya Sridhar, Priyanka Sekar, and Sami Ranajan Sahoo. "Internet of things in medicine and dentistry." International Journal of Clinical Biochemistry and Research 9, no. 2 (June 15, 2022): 98–105. http://dx.doi.org/10.18231/j.ijcbr.2022.020.

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The internet of Things (IoT) in medical arena, also known as internet of medical things (IoMT) is the collection of medical devices and application connecting healthcare Information Technology (IT) system by means of online computer networks. It enables virtually any medical devices as well as non digitalized things (like pills and beds) to connect process and communicate data via web. IoMT allows medical devices and health-care items to exchange data on the spot, online with anyone who has a genuine need for it. The aura of IoMT includes wireless communication technologies, cloud computing, wearable technologies, messaging protocols, security methods, development boards, microcontrollers, mobile/IoT operating systems, and programming languages, built upon numerous technologies including advanced sensors, IoT connectivity and artificial intelligence (AI). IoMT can improve healthcare quality and reduce costs too in hospitals and clinics. In places where distance is the limiting factor, Telemedicine plays a vital role in remote patient monitoring. Major applications include biomedical equipment remote monitoring, remote patient monitoring biosensors and radio frequency identification. IoT in Dentistry aims to streamline oral health care by enhancing oral health while reducing costs, promoting workflow, relieving dentists and dental workers of tedious and time-consuming activities, and igniting interest in personalized oral health care.
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Prasad, Vivek Kumar, Pronaya Bhattacharya, Darshil Maru, Sudeep Tanwar, Ashwin Verma, Arunendra Singh, Amod Kumar Tiwari, et al. "Federated Learning for the Internet-of-Medical-Things: A Survey." Mathematics 11, no. 1 (December 28, 2022): 151. http://dx.doi.org/10.3390/math11010151.

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Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations.
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Niu, Qinwang, Haoyue Li, Yu Liu, Zhibo Qin, Li-bo Zhang, Junxin Chen, and Zhihan Lyu. "Toward the Internet of Medical Things: Architecture, trends and challenges." Mathematical Biosciences and Engineering 21, no. 1 (2023): 650–78. http://dx.doi.org/10.3934/mbe.2024028.

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<abstract><p>In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.</p></abstract>
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Alsaeed, Norah, and Farrukh Nadeem. "Authentication in the Internet of Medical Things: Taxonomy, Review, and Open Issues." Applied Sciences 12, no. 15 (July 26, 2022): 7487. http://dx.doi.org/10.3390/app12157487.

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The Internet of Medical Things (IoMT) has revolutionized the world of healthcare by remotely connecting patients to healthcare providers through medical devices connected over the Internet. IoMT devices collect patients’ medical data and share them with healthcare providers, who analyze it for early control of diseases. The security of patients’ data is of prime importance in IoMT. Authentication of users and devices is the first layer of security in IoMT. However, because of diverse and resource-constrained devices, authentication in IoMT is a challenging task. Several authentication schemes for IoMT have been proposed in the literature. However, each of them has its own pros and cons. To identify, evaluate and summarize the current literature on authentication in IoMT, we conducted a systematic review of 118 articles published between 2016 and 2021. We also established a taxonomy of authentication schemes in IoMT from seven different perspectives. We observed that most of the authentication schemes use a distributed architecture and public key infrastructure. It was also observed that hybrid cryptography approaches have become popular to overcome the shortcomings of single cryptographic approaches. Authentication schemes in IoMT need to support end-to-end, cross-layer, and cross-domain authentication. Finally, we discuss some open issues and future directions.
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Alseddiqi, Mohamed, Anwar AL Mofleh, Osama Najam, Budoor AlMannaei, Leena Albalooshi, Abdulla Alheddi, and Ahmed Alshaimi. "Internet of Medical Things Application in King Hamad University Hospital." Saudi Journal of Biomedical Research 8, no. 06 (June 23, 2023): 74–82. http://dx.doi.org/10.36348/sjbr.2023.v08i06.001.

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Background: The Internet of Medical Things (IoMT) is a network of medical devices and applications that are connected to the internet, allowing healthcare providers to remotely monitor and manage patient health. King Hamad University Hospital (KHUH) is a tertiary care hospital in Bahrain that has implemented various IoMT applications to improve patient care. Methods: The aim of this study is to investigate the application of Internet of Medical Things (IoMT) in King Hamad University Hospital (KHUH) and its impact on patient care and hospital operations The study will be conducted at KHUH, which is a tertiary care hospital located in Bahrain. KHUH, accommodates 739 bed in all services. (In- patients including isolation rooms: 348, Out-patient clinics: 242, Other services: 149). That provides specialized medical services to patients from Bahrain and neighboring countries. Results: The survey results showed that the most commonly used IoMT applications were remote monitoring devices for vital signs, telemedicine platforms for virtual consultations, and electronic health records for patient data management. Healthcare providers reported that these applications were effective in improving patient outcomes, reducing hospital readmissions, and increasing efficiency in healthcare delivery. However, some challenges were identified during implementation, such as technical issues with connectivity and data security concerns. Conclusion: The implementation of IoMT applications in KHUH has shown promising results in improving patient care and healthcare delivery. However, ongoing efforts are needed to address the challenges faced during implementation to ensure the sustainability and scalability of these technologies. Further research is also needed to evaluate the long-term impact of IoMT on patient outcomes and healthcare costs.
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Dissertations / Theses on the topic "Internet of Medical Things (IoMT)"

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Sayeed, Md Abu. "Epileptic Seizure Detection and Control in the Internet of Medical Things (IoMT) Framework." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703334/.

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Epilepsy affects up to 1% of the world's population and approximately 2.5 million people in the United States. A considerable portion (30%) of epilepsy patients are refractory to antiepileptic drugs (AEDs), and surgery can not be an effective candidate if the focus of the seizure is on the eloquent cortex. To overcome the problems with existing solutions, a notable portion of biomedical research is focused on developing an implantable or wearable system for automated seizure detection and control. Seizure detection algorithms based on signal rejection algorithms (SRA), deep neural networks (DNN), and neighborhood component analysis (NCA) have been proposed in the IoMT framework. The algorithms proposed in this work have been validated with both scalp and intracranial electroencephalography (EEG, icEEG), and demonstrate high classification accuracy, sensitivity, and specificity. The occurrence of seizure can be controlled by direct drug injection into the epileptogenic zone, which enhances the efficacy of the AEDs. Piezoelectric and electromagnetic micropumps have been explored for the use of a drug delivery unit, as they provide accurate drug flow and reduce power consumption. The reduction in power consumption as a result of minimal circuitry employed by the drug delivery system is making it suitable for practical biomedical applications. The IoMT inclusion enables remote health activity monitoring, remote data sharing, and access, which advances the current healthcare modality for epilepsy considerably.
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Gheryani, Mostafa. "Epileptic seizure and anomaly detection in internet of medical things." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5211.

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L'objectif de ma thèse est d'analyser les caractéristiques des signaux inertiels et physiologiques qui générés par les mouvements inhabituels des patients lorsque la crise survient et de développer un algorithme pour détecter la crise. Notre approche dans le chapitre III commence par dériver la moyenne quadratique pour l'ACM et le Gyro, suivie de la normalisation de signaux entiers dans la même plage puis de l'agrégation en un seul signal. Le contrôle du graphique avec ses limites supérieure et inférieure est défini lors de la phase au repos et utilisé pour détecter les crises anormales et pour déclencher une alarme. La procédure dans le chapitre IV de détection s'exécute dans un dispositif de collecte de données portable et déclenche une alarme. Cet algorithme est basé sur la dérivation des mesures instantanées dans une plage de données glissante contenant des mesures inertielles de 3D (ACM), 3D Gyro et de EMG. La différence entre la puissance estimée et la puissance mesurée est utilisé comme entrée pour l'algorithme de détection basé sur la carte de contrôle de Shewhart. Lorsque la différence entre la puissance prévue et la puissance dérivée dépasse les limites [limite inférieure/supérieure] pour plusieurs créneaux consécutifs, une alarme est déclenchée. L'approche que nous proposons permet une bonne détection avec un FAR de 4\% et une sensibilité de 97\%. Notre modèle dans Le chapitre V commence par réduire la dimension des données collectées grâce à l'utilisation de la moyenne quadratique pour dériver un signal de 3D ACM et un signal du 3D Gyro. Avec les 3 signaux dérivés (ACM, Gyro et EMG), nous appliquons le TVP pour dériver un signal utilisé comme entrée pour le mécanisme de détection d'anomalie. La version robuste du z-score est appliquée sur le signal résultant produit pour détecter les déviations associées aux crises avant de déclencher une alarme. Nos résultats expérimentaux montrent que notre approche proposée est robuste contre les mouvements nocturnes et atteint un haut niveau de précision de détection avec un faible FAR. Ensuite, nous comparons les performances de notre approche avec la méthode des passages à zéro calculées à partir de sEMG. Notre approche montre que la précision de détection à l'aide du VTP surpasse le nombre de passages à zéro sur une plage glissante de chevauchement de 1 seconde. Dans le chapitre VI, Les appareils IoMT sont utilisés pour acquérir ACM, Gyro et EMG et pour transmettre les mesures à LPU pour traitement. Lorsque le LPU détecte des changements anormaux dans les mesures, il déclenche une alarme. Notre approche proposée utilise SVM avec option de rejet pour distinguer les crises des activités normales de la vie quotidienne. Les caractéristiques présentant des changements physiologiques de l'activité musculaire et les données inertielles ont été extraites dans LPU et sont utilisées comme entrée pour l'algorithme de détection. L'option de rejet dans SVM est utilisée pour améliorer la fiabilité du système de surveillance et pour réduire les fausses alarmes, où l'utilisateur est averti et a la possibilité de supprimer l'alarme dans son smartphone en l'absence de saisie. Les expériences menées ont prouvé que notre approche proposée peut atteindre une bonne précision pour distinguer les crises des activités normales avec seulement 4% de taux de FAR. Dans chapitre VII, nous proposons un cadre pour empêcher une MitM de perturber les opérations et interdire le déclenchement d'alarmes par le système de surveillance à distance des soins de santé. Pour réduire la consommation d'énergie lours de la transmission normale des données et préserver la confidentialité des données de santé, notre système transmet une signature de plus petite taille dérivée des données acquises avec un code d'authentification de message, où la clé est dérivée de RSSI. Nos résultats expérimentaux montrent que notre approche peut atteindre une précision de détection élevée avec un faible FAR de 3%
The goal of my PhD is to investigate the characteristics of inertial and physiological signals via IoMT systems generated by epileptic seizure and to develop an algorithm to detect the seizure. The focus of the algorithms lies in nocturnal seizures where the risk of SUDEP is high because the patients are unsupervised while sleeping. In chapter III analysis we propose an IoMT platform for seizure detection. The proposed framework approach starts by deriving the RMS for ACM and Gyro, followed by the normalization of whole signals (ACM, Gyro and EMG) in the same range, and aggregate all into one signal. The chart’s control with its upper and lower limits are derived in the training phase and used to detect abnormal seizures and to raise an alarm. In chapter IV Our proposed algorithm is based on deriving instantaneous power measurements in a sliding window containing 3D ACM or 3D Gyro or EMG. The residual between forecasted and measured power is used as input for the detection algorithm based on Shewhart Control Chart (SCC). When the difference between forecasted and derived power exceeds chart limits [lower, upper] for several consecutive slots, an alarm is raised. Our proposed approach provides low FAR (4%) and sensitivity of 97%. In Chapter V our proposed method starts by reducing the dimension of collected data using RMS to derive one signal from 3D ACM and one signal from 3D Gyro. With the derived 3 collected signals (ACM, Gyro and EMG), we apply VTP to derive one signal used as input for anomaly detection mechanism. The robust version of z-score is applied on the resulting product signal to detect deviations associated with seizures before raising an alarm. Our experimental results show that our proposed approach is robust against nocturnal movements and achieves a high level of detection accuracy with low false alarm rate. Afterward, we compare the performance of our approach with the zero-crossings method calculated from sEMG. Our approach shows that the detection accuracy using VTP outperforms zero-crossing count over an overlapping sliding window of 1 second. In chapter VI, we propose an approach using the IoMT devices to acquire EMG, ACM and Gyro data and to transmit the measurements to a LPU for processing. When the LPU detects abnormal changes in the measurements, it raises an alarm for assistant. Our proposed approach uses SVM with reject option to distinguish seizures from normal daily life activity. Features presenting physiological changes of muscular activity and inertial data were extracted in LPU and are used as input for the detection algorithm. The reject option in SVM is used to enhance the reliability of the monitoring system and to reduce FAR, where the user is notified and can discard the alarm in his smartphone in the absence of seizure. The conducted experiments proved that our proposed approach could achieve a good accuracy with only 4% of false alarm rate. Finally, since we are using IoMT sensors, which are susceptible to data security issues. We proposed a solution to prevent Man in the Middle (MitM) attack, which can identify healthcare emergencies of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this chapter, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from Received Signal Strength Indication (RSSI). Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%
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Poggi, Giovanni. "Internet of Medical Things." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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In questa tesi, si partirà con un'introduzione generale all'Internet of Things focalizzando l'attenzione sulla struttura generale dell'architettura ed il suo funzionamento di base in una rete con molti altri dispositivi. Seguirà l'analisi del trend di questa tecnologia e la sua evoluzione nel tempo, con particolare attenzione all'architettura ed al suo successo ai giorni nostri. Verrà discussa l'industrializzazione che ha portato alla creazione delle Industrie 4.0, ovvero l'Internet of Things in ambito sensoristica applicato all'industria, alla robotica, ai Big Data che si occupano dell'archiviazione, all'acquisizione e all'analisi dei dati provenienti dai vari dispositivi, ai sistemi ciberfisici, alla connessione di tutti questi oggetti tra loro per la comunicazione e lo scambio delle informazioni ed infine alla realtà aumentata per il supporto nei vari processi industriali. Questi macroargomenti saranno lo spunto per introdurre il concetto di Internet of Medical Things. Con una breve panoramica sugli ospedali al giorno d'oggi, si vuol proporre una nuova concezione di ospedale dove vengono poste al centro dell'attenzione le esigenze del paziente e del personale medico, trattando nello specifico le tecnologie impiegate, i processi chirurgici, clinici e l’erogazione delle prestazioni sanitarie. Il discorso seguirà focalizzando l'attenzione anche su ambienti della medicina come la chirurgia, introducendo un luogo in cui migliaia di dispositivi connessi alla rete comunicano tra di loro. Si vedranno anche tutte le eventuali criticità e le varie sfide che bisognerà risolvere ed intraprendere per arrivare ad un corretto ed efficiente passaggio agli odierni ospedali concepiti per essere ospedali 4.0. Si concluderà con una riflessione su tutte queste tecnologie e la rivoluzione in ambito medico che promette cambiamenti che porteranno al nuovo concetto di Ospedale 4.0 su un’ottica di Internet of Medical Things.
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Olokodana, Ibrahim Latunde. "Kriging Methods to Exploit Spatial Correlations of EEG Signals for Fast and Accurate Seizure Detection in the IoMT." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1707311/.

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Epileptic seizure presents a formidable threat to the life of its sufferers, leaving them unconscious within seconds of its onset. Having a mortality rate that is at least twice that of the general population, it is a true cause for concern which has gained ample attention from various research communities. About 800 million people in the world will have at least one seizure experience in their lifespan. Injuries sustained during a seizure crisis are one of the leading causes of death in epilepsy. These can be prevented by an early detection of seizure accompanied by a timely intervention mechanism. The research presented in this dissertation explores Kriging methods to exploit spatial correlations of electroencephalogram (EEG) Signals from the brain, for fast and accurate seizure detection in the Internet of Medical Things (IoMT) using edge computing paradigms, by modeling the brain as a three-dimensional spatial object, similar to a geographical panorama. This dissertation proposes basic, hierarchical and distributed Kriging models, with a deep neural network (DNN) wrapper in some instances. Experimental results from the models are highly promising for real-time seizure detection, with excellent performance in seizure detection latency and training time, as well as accuracy, sensitivity and specificity which compare well with other notable seizure detection research projects.
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Maioli, Edoardo. "Internet of Medical Things e Sviluppo di Sistemi Interoperabili basati su Standard FHIR: Un caso di studio basato sull'integrazione di un Dispositivo EGA." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24306/.

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Ci troviamo in un mondo in costante cambiamento ed evoluzione, in particolar modo nel settore informatico. È proprio in questo continuo processo di evoluzione che spicca l’IoT ovvero Interent Of Things (Internet delle cose). Oggetti, anche di tutti i giorni, collegati ad Internet capaci di scambiarsi messaggi e di comunicare tra loro. Kevin Ashton, pioniere dell’IoT, dice che questa sarà la prossima rivoluzione tecnologica come lo è stato a suo tempo Internet. Tuttavia, come sarà discusso in questa tesi, alcuni ambiti di applicazione dell’IoT come il sistema medico ed ospedaliero non sono ancora del tutto uniformati ma anzi, presentano vari ostacoli nell’ottica di un sistema interoperabile ed intelligente. Per quanto riguarda quest’ultimo caso si parla di Internet of Medical Things(IoMT), Internet of Healthcare Things (IoHT) o ancora Medicina 4.0 (QuartaRivoluzione Industraile in ambito medico). Possiamo pensare ad un ospedale con vari dispositivi capaci di condividere dati ed informazioni tra loro e di mettere adisposizione questi dati in tempo reale al personale sanitario e al paziente anche a distanza.
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Chisanga, Fredrick. "Medical application of the Internet of Things (IoT): prototyping a telemonitoring system." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/27940.

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The Internet of Things (IoT) is a technological paradigm that can be perceived as an evolution of the internet. It is a shift from the traditional way of connecting devices to the internet, both in number and diversity of connected devices. This significant and marked growth in the number and diversity of devices connected to the internet has prompted a rethink of approaches to interconnect devices. The growth in the number of connected devices is driven by emerging applications and business models and supported by falling device costs while the growth in the diversity is driven by the reduction in the cost of manufacturing these devices. This has led to an increase in the number of users (not limited to people) of the internet. According to statistics by the ITU, by the end of 2015, about 3.2 billion people were using the Internet. Significantly, 34% of households in developing countries had Internet access, with more than 80% of households in developed countries. This indicates that it is realistic to leverage the IoT in living spaces. Appreciating this potential, many sectors of society are already positioning themselves to reap the benefits of this great promise. Hence the health sector would do well to adopt this technological paradigm to enhance service delivery. One specific area where the health sector can benefit from the adoption of the IoT is in telemonitoring and the associated early response to medical emergencies. Statistics and research show that there are areas in the medical field, that still need improvement to enhance service delivery. The Nursing Times has summed up these areas into four categories. The first one is a need to have a regular observation of patients and their vital signs. Here, health service providers (SPs) need to adopt creative and non-obtrusive methods that will encourage patients' participation in the monitoring of these vital signs. As much as possible, vital signs readings should be taken at convenient locations and times. Therefore, devices that have consistent internet access and are usually a part of daily life for most patients, such as the mobile phones would prove to be a key enabler of regular observation of vital signs. Furthermore, miniaturization of the vital signs monitoring or sensing devices would be a key step towards realizing this scenario. A lot of work is already being done to miniaturize these devices and make them as much a part of daily life as possible, as evidenced by advancements in the field of fitness and wearables. To map this use to the medical field, a system needs to be created that would allow for the aggregation of these disparate measuring and monitoring devices with medical information management systems. The second potential area of improvement is in the early recognition of deterioration of the patients. With regular observation of patients, it is possible to recognize deterioration at its early stage. Taking cognizance of the different needs of the various stakeholders is important to achieve the intended results. The third potential area of improvement is in the communication among stakeholders. This has to do with identifying the relevant data that must be delivered to the stakeholders during the monitoring and management process. Lastly, effective response to medical concerns is the other potential area of improvement. It is noted that patients do not generally get the right response at the right time because the information does not reach the rightly qualified personnel in good time. The regular and real-time capture of vital signs data coupled with added analytics can enable IoT SPs to design solutions that automate the management and transmission of medical data in a timely manner. This work addresses how the medical sector can adopt IoT-based solutions to improve service delivery, while utilizing existing resources such as smartphones, for the transmission and management of vital signs data, availing it to stakeholders and improve communication among them. It develops a telemonitoring system based on IoT design approaches. The developed system captures readings of vital signs from monitoring devices, processes and manages this data to serve the needs of the various stakeholders. Additionally, intelligence was added to enable the system to interpret the data and make decisions that will help medical practitioners and other stakeholders (patients, caregivers, etc.) to more timely, consistently and reliably provide and receive medical services/assistance. Two end user applications were developed. A cloud-based web application developed using PHP, HTML, and JavaScript and an Android mobile application developed using Java programming language in Android studio. An ETSI standards-compliant M2M middleware is used to aggregate the system using M2M applications developed in Python. This is to leverage the benefits of the standards-compliant middleware while offering flexibility in the design of applications. The developed system was evaluated to assess whether it meets the requirements and expectations of the various stakeholders. Finally, the performance of the proposed telemonitoring system was studied by analyzing the delay on the delivery of messages (local notifications, SMS, and email) to various stakeholders to assess the contribution towards reducing the overall time of the cardiac arrest chain of survival. The results obtained showed a marked improvement (over 28 seconds) on previous work. In addition to improved performance in monitoring and management of vital signs, telemonitoring systems have a potential of decongesting health institutions and saving time for all the stakeholders while bridging most of the gaps discussed above. The captured data can also provide the health researchers and physicians with most of the prerequisite data to effectively execute predictive health thereby improving service delivery in the health sector.
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Sundaravadivel, Prabha. "Application-Specific Things Architectures for IoT-Based Smart Healthcare Solutions." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157532/.

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Human body is a complex system organized at different levels such as cells, tissues and organs, which contributes to 11 important organ systems. The functional efficiency of this complex system is evaluated as health. Traditional healthcare is unable to accommodate everyone's need due to the ever-increasing population and medical costs. With advancements in technology and medical research, traditional healthcare applications are shaping into smart healthcare solutions. Smart healthcare helps in continuously monitoring our body parameters, which helps in keeping people health-aware. It provides the ability for remote assistance, which helps in utilizing the available resources to maximum potential. The backbone of smart healthcare solutions is Internet of Things (IoT) which increases the computing capacity of the real-world components by using cloud-based solutions. The basic elements of these IoT based smart healthcare solutions are called "things." Things are simple sensors or actuators, which have the capacity to wirelessly connect with each other and to the internet. The research for this dissertation aims in developing architectures for these things, focusing on IoT-based smart healthcare solutions. The core for this dissertation is to contribute to the research in smart healthcare by identifying applications which can be monitored remotely. For this, application-specific thing architectures were proposed based on monitoring a specific body parameter; monitoring physical health for family and friends; and optimizing the power budget of IoT body sensor network using human body communications. The experimental results show promising scope towards improving the quality of life, through needle-less and cost-effective smart healthcare solutions.
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Dellgren, Emelie. "A case study on how the Apple Watch can benefit medical heart research." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211493.

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The medical health industry is entering a new era and technology will play a great role in this area. Equipment in hospitals is in many cases strictly dependent on technology that works. However, technology in the medical health industry will maybe become a bigger part of our private lifestyle. This lifestyle includes digital health apps, wearables and devices that track your daily physical routines with “Internet of things”. These ways of keeping track of your health can be used for private purposes, but also to complement medical studies with clinical results. This thesis will focus on how wearables can complement a medical study where patients with severe heart failure will use the smartwatch Apple Watch. This smartwatch will collect data on patients daily physical activity pattern and thereafter analyze this data in order to find activity patterns. This thesis intends to answer the questions How can wearables such as the Apple Watch benefit medical heart research? and what makes the Apple Watch a suitable wearable for the medical study at Lund’s University Hospital? Interviews were therefore held with medical heart researchers and addressed the purpose of the medical study and their choice of wearable. Thereafter, a examination of the Apple Watch was conducted and it together with the interview indicated that the Apple Watch in fact is a suitable wearable. Finally, an exportation process where data from the Apple Watch was done where the exported data then was decoded in Microsoft Excel. The purpose of this was to examine statements that were revealed in the interview. That being said, the thesis came to the conclusion that the Apple Watch contributes a lot when mixing complementing data from wearables with clinical records. Another conclusion was that this tracking device was suitable for the medical study. In what extension the Apple Watch is suitable, is yet unclear since the medical study is in need of further patients and research where one compares wearables against each other.
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Lu, Andy. "Forensic analysis on wireless medical devices." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2541.

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The number of Internet of Things (IoT) devices is forecast to grow to over 25 billion by 2030, with the healthcare IoT market projected to grow to 25.9% of IoT devices by 2028 worldwide. However, with new and growing technologies come new types of risks. Current risk assessment and risk management methods haven’t been designed to anticipate or predict these risks. IoT risks relate to openness and lack of standardisation, linking and connectivity between the devices and the lack of skilled support for IoT devices and networks. These factors put medical IoT devices and, by extension, their users at risk from cyber threats. Additionally, the attack surface for the medical IoT has not been fully mapped, nor have the risks been fully assessed. The lack of coverage means increased risk for manufacturers, medical facilities, and potentially, patients. This project evaluates the effectiveness of how new and emerging wireless and connected medical devices can be managed and analysed through a digital forensic framework. An initial analysis of the currently available frameworks showed that they did not address the nuances of implementing a wireless or connected medical device into a healthcare organisation. Digital forensic frameworks that were deemed relevant to wireless medical devices were selected and tested against several currently available wireless medical devices. Four frameworks were tested across four devices each. The outcome was that none of the frameworks was fully able to effectively manage wireless medical devices (at least in terms of the objectives of digital forensics), with each missing elements that would aid an investigator or a hospital organisation in the case of a cyber-related incident. These results led to the synthesis and testing of a framework that addressed the missing elements. The framework emphasises forensic readiness planning and risk management. The synthesised framework was tested against a new device. The results of the test found that the synthesised framework was effective in both the proactive digital forensics approach and reactive approach. The testing found that the framework performed better than the other tested frameworks, containing additional phases and steps that were advantageous in preparing and reacting to incidents involving wireless medical devices.
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Jönsson, Mattias. "TempScanner : An application to detect fever." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-40768.

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This thesis describes how a solution can be built to detect human flu-like symptoms. Flu-like symptoms are important to detect to prevent Covid-19 [6]. As people are returning to work there is a need for a simple way of detecting flu-like symptoms to prevent the spread of Covid-19. Other than a solution, this thesis concluded how human flu-like symptoms can be detected, with cameras specifically. This is to know what symptoms are most likely to work for a prototype. The technique of cameras and thermal cameras made this project possible as well as the technique of a single-board computer. The technique of cloud-based services is also an important part of this project. This project has resulted in a novel prototype using a single-board computer, cameras, and various cloud-based services to detect and inform a person if he or she has a human flu-like symptom.
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Books on the topic "Internet of Medical Things (IoMT)"

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Gupta, Sunil, Hitesh Kumar Sharma, and Monit Kapoor. Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT). Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18896-1.

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Hemanth, D. Jude, J. Anitha, and George A. Tsihrintzis, eds. Internet of Medical Things. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63937-2.

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Mitra, Anirban, Jayanta Mondal, and Anirban Das. Medical Internet of Things. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429318078.

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Hassanien, Aboul Ella, Nilanjan Dey, and Surekha Borra, eds. Medical Big Data and Internet of Medical Things. Boca Raton : Taylor & Francis, [2019]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351030380.

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Chakraborty, Chinmay, Amit Banerjee, Lalit Garg, and Joel J. P. C. Rodrigues, eds. Internet of Medical Things for Smart Healthcare. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8097-0.

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Prasanth, A., Lakshmi D, Rajesh Kumar Dhanaraj, Sherimon P C, and Balamurugan Balusamy. Cognitive Computing for Internet of Medical Things. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003256243.

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Tanwar, Sudeep, Ashwin Verma, and Pronaya Bhattacharya. Federated Learning for Internet of Medical Things. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003303374.

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Krishnan, Saravanan, and Aboobucker Ilmudeen. Internet of Medical Things in Smart Healthcare. New York: Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003369035.

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Sapra, Luxmi, Varun Sapra, and Akashdeep Bhardwaj. Security Implementation in Internet of Medical Things. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003269168.

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Al-Turjman, Fadi, and Anand Nayyar, eds. Machine Learning for Critical Internet of Medical Things. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-80928-7.

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Book chapters on the topic "Internet of Medical Things (IoMT)"

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Sharma, Neha, Sadhana Tiwari, Md Ilyas, Rajeev Raghuvanshi, and Ashwin Verma. "IoMT Implementation." In Federated Learning for Internet of Medical Things, 65–83. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003303374-4.

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Gupta, Sunil, Hitesh Kumar Sharma, and Monit Kapoor. "Internet of Medical Things (IoMedT) vs Internet of Things (IoT)." In Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT), 27–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18896-1_3.

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D’Souza, Rohan. "Implementation of the Internet of Medical Things (IoMT): Clinical and Policy Implications." In Internet of Things, 313–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66633-0_14.

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Prasad, Vivek Kumar, Jainil Solanki, Pronaya Bhattacharya, Ashwin Verma, and Madhuri Bhavsar. "Artificial Intelligence Applications for IoMT." In Federated Learning for Internet of Medical Things, 23–40. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003303374-2.

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Jeba Kumar, R. J. S., J. Roopa Jayasingh, and Deepika Blessy Telagathoti. "Intelligent Transit Healthcare Schema Using Internet of Medical Things (IoMT) Technology for Remote Patient Monitoring." In Internet of Things, 17–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63937-2_2.

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Pushpalatha, N., P. Anbarasu, and A. Venkatesh. "Communication Protocols for IoMT-Based Healthcare Systems." In Cognitive Computing for Internet of Medical Things, 59–75. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003256243-4.

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Patel, Jigna, Jitali Patel, Rupal Kapdi, and Shital Patel. "Early Prediction of Prevalent Diseases Using IoMT." In Federated Learning for Internet of Medical Things, 125–44. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003303374-7.

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Gupta, Sunil, Hitesh Kumar Sharma, and Monit Kapoor. "Authentication Methods for Internet of Medical Things." In Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT), 119–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18896-1_10.

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Idrees, Ali Kadhum, Balqees Talal Hasan, and Sara Kadhum Idrees. "Deep Learning for Combating COVID-19 Pandemic in Internet of Medical Things (IoMT) Networks: A Comprehensive Review." In Internet of Things, 57–82. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-28631-5_3.

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Roobini, S., M. Kavitha, M. Sujaritha, and D. Rajesh Kumar. "Cyber-Security Threats to IoMT-Enabled Healthcare Systems." In Cognitive Computing for Internet of Medical Things, 105–30. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003256243-6.

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Conference papers on the topic "Internet of Medical Things (IoMT)"

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N, Ashokkumar, Narayana Reddy Yatam, Chandra I, Anita Christaline. J., R. Bhairavi, and Thiruveni M. "Internet of Medical Things (IoMT): Opportunities and Security Challenges." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), 370–76. IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689770.

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Anusha, V. Sai, R. Kiran Kumar, G. Charan Kumar, and Palagiri Mabjan. "Comprehensive Survey on Internet of Medical Things (IoMT) - Applications and Challenges." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724150.

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Kabir, Md Rafiul, and Sandip Ray. "DT-IoMT: A Digital Twin Reference Model for Secure Internet of Medical Things." In 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 433–38. IEEE, 2024. http://dx.doi.org/10.1109/isvlsi61997.2024.00084.

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Sohail, Fatima, Muhammad Asim Mukhtar Bhatti, Muhammad Awais, and Aamna Iqtidar. "Explainable Boosting Ensemble Methods for Intrusion Detection in Internet of Medical Things (IoMT) Applications." In 2024 4th International Conference on Digital Futures and Transformative Technologies (ICoDT2), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/icodt262145.2024.10740251.

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Chandel, Sapana, Riju Bhattacharya, Manjushree Nayak, and Astha Pathak. "Integrating Eye Gaze Estimation with the Internet of Medical Things (IoMT) for Individualized and Efficient Healthcare." In 2024 2nd World Conference on Communication & Computing (WCONF), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692177.

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Ghazal, Taher M., Mohammad Kamrul Hasan, Ghassan F. Issa, Nidal A. Al-Dmour, Saif E. A. Alnawayseh, Waleed T. Al-Sit, and Rashed Aldhaheri. "Expression of Concern for: Security Threats and their Mitigations on the Operating System of Internet of Medical Things (IoMT)." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 1. IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703535.

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Santiago, S., Prasanna B T, Kashif Qureshi, and K. Subash. "Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545094.

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Vishnu, S., S. R. Jino Ramson, and R. Jegan. "Internet of Medical Things (IoMT) - An overview." In 2020 5th International Conference on Devices, Circuits and Systems (ICDCS). IEEE, 2020. http://dx.doi.org/10.1109/icdcs48716.2020.243558.

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PremaLatha, V., E. Sreedevi, and S. Sivakumar. "Contemplate on Internet of Things Transforming as Medical Devices - The Internet of Medical Things (IOMT)." In 2019 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2019. http://dx.doi.org/10.1109/iss1.2019.8908090.

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Farhad, Arshad, and Sandra Woolley. "Data Quality and the Internet of Medical Things (IoMT)." In 35th International BCS Human-Computer Interaction Conference. BCS Learning & Development, 2022. http://dx.doi.org/10.14236/ewic/hci2022.65.

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Reports on the topic "Internet of Medical Things (IoMT)"

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Ali, Mohamed. Internet of things medical devices cybersecurity. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-920.

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MacFarlane, Andrew. 2021 medical student essay prize winner - A case of grief. Society for Academic Primary Care, July 2021. http://dx.doi.org/10.37361/medstudessay.2021.1.1.

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As a student undertaking a Longitudinal Integrated Clerkship (LIC)1 based in a GP practice in a rural community in the North of Scotland, I have been lucky to be given responsibility and my own clinic lists. Every day I conduct consultations that change my practice: the challenge of clinically applying the theory I have studied, controlling a consultation and efficiently exploring a patient's problems, empathising with and empowering them to play a part in their own care2 – and most difficult I feel – dealing with the vast amount of uncertainty that medicine, and particularly primary care, presents to both clinician and patient. I initially consulted with a lady in her 60s who attended with her husband, complaining of severe lower back pain who was very difficult to assess due to her pain level. Her husband was understandably concerned about the degree of pain she was in. After assessment and discussion with one of the GPs, we agreed some pain relief and a physio assessment in the next few days would be a practical plan. The patient had one red flag, some leg weakness and numbness, which was her ‘normal’ on account of her multiple sclerosis. At the physio assessment a few days later, the physio felt things were worse and some urgent bloods were ordered, unfortunately finding raised cancer and inflammatory markers. A CT scan of the lung found widespread cancer, a later CT of the head after some developing some acute confusion found brain metastases, and a week and a half after presenting to me, the patient sadly died in hospital. While that was all impactful enough on me, it was the follow-up appointment with the husband who attended on the last triage slot of the evening two weeks later that I found completely altered my understanding of grief and the mourning of a loved one. The husband had asked to speak to a Andrew MacFarlane Year 3 ScotGEM Medical Student 2 doctor just to talk about what had happened to his wife. The GP decided that it would be better if he came into the practice - strictly he probably should have been consulted with over the phone due to coronavirus restrictions - but he was asked what he would prefer and he opted to come in. I sat in on the consultation, I had been helping with any examinations the triage doctor needed and I recognised that this was the husband of the lady I had seen a few weeks earlier. He came in and sat down, head lowered, hands fiddling with the zip on his jacket, trying to find what to say. The GP sat, turned so that they were opposite each other with no desk between them - I was seated off to the side, an onlooker, but acknowledged by the patient with a kind nod when he entered the room. The GP asked gently, “How are you doing?” and roughly 30 seconds passed (a long time in a conversation) before the patient spoke. “I just really miss her…” he whispered with great effort, “I don’t understand how this all happened.” Over the next 45 minutes, he spoke about his wife, how much pain she had been in, the rapid deterioration he witnessed, the cancer being found, and cruelly how she had passed away after he had gone home to get some rest after being by her bedside all day in the hospital. He talked about how they had met, how much he missed her, how empty the house felt without her, and asking himself and us how he was meant to move forward with his life. He had a lot of questions for us, and for himself. Had we missed anything – had he missed anything? The GP really just listened for almost the whole consultation, speaking to him gently, reassuring him that this wasn’t his or anyone’s fault. She stated that this was an awful time for him and that what he was feeling was entirely normal and something we will all universally go through. She emphasised that while it wasn’t helpful at the moment, that things would get better over time.3 He was really glad I was there – having shared a consultation with his wife and I – he thanked me emphatically even though I felt like I hadn’t really helped at all. After some tears, frequent moments of silence and a lot of questions, he left having gotten a lot off his chest. “You just have to listen to people, be there for them as they go through things, and answer their questions as best you can” urged my GP as we discussed the case when the patient left. Almost all family caregivers contact their GP with regards to grief and this consultation really made me realise how important an aspect of my practice it will be in the future.4 It has also made me reflect on the emphasis on undergraduate teaching around ‘breaking bad news’ to patients, but nothing taught about when patients are in the process of grieving further down the line.5 The skill Andrew MacFarlane Year 3 ScotGEM Medical Student 3 required to manage a grieving patient is not one limited to general practice. Patients may grieve the loss of function from acute trauma through to chronic illness in all specialties of medicine - in addition to ‘traditional’ grief from loss of family or friends.6 There wasn’t anything ‘medical’ in the consultation, but I came away from it with a real sense of purpose as to why this career is such a privilege. We look after patients so they can spend as much quality time as they are given with their loved ones, and their loved ones are the ones we care for after they are gone. We as doctors are the constant, and we have to meet patients with compassion at their most difficult times – because it is as much a part of the job as the knowledge and the science – and it is the part of us that patients will remember long after they leave our clinic room. Word Count: 993 words References 1. ScotGEM MBChB - Subjects - University of St Andrews [Internet]. [cited 2021 Mar 27]. Available from: https://www.st-andrews.ac.uk/subjects/medicine/scotgem-mbchb/ 2. Shared decision making in realistic medicine: what works - gov.scot [Internet]. [cited 2021 Mar 27]. Available from: https://www.gov.scot/publications/works-support-promote-shared-decisionmaking-synthesis-recent-evidence/pages/1/ 3. Ghesquiere AR, Patel SR, Kaplan DB, Bruce ML. Primary care providers’ bereavement care practices: Recommendations for research directions. Int J Geriatr Psychiatry. 2014 Dec;29(12):1221–9. 4. Nielsen MK, Christensen K, Neergaard MA, Bidstrup PE, Guldin M-B. Grief symptoms and primary care use: a prospective study of family caregivers. BJGP Open [Internet]. 2020 Aug 1 [cited 2021 Mar 27];4(3). Available from: https://bjgpopen.org/content/4/3/bjgpopen20X101063 5. O’Connor M, Breen LJ. General Practitioners’ experiences of bereavement care and their educational support needs: a qualitative study. BMC Medical Education. 2014 Mar 27;14(1):59. 6. Sikstrom L, Saikaly R, Ferguson G, Mosher PJ, Bonato S, Soklaridis S. Being there: A scoping review of grief support training in medical education. PLOS ONE. 2019 Nov 27;14(11):e0224325.
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Yemen: Urban displacement in a rural society. Internal Displacement Monitoring Center (IDMC), October 2019. http://dx.doi.org/10.55363/idmc.obmq2729.

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War and displacement in Yemen are not primarily urban in nature. Despite the amount of media attention given to key urban battles such as the siege of Taiz and the battle for Hodeidah, nearly 70 per cent of conflict and displacement takes place in rural areas. This paper, part of the 'UnSettlement: Urban displacement in the 21st century' thematic series, examines the urban and rural characteristics of displacement in Yemen, including the push and pull factors in both areas. It provides an overview of historical urbanisation trends in the country, and a rural-urban disaggregation of large conflict and displacement datasets from ACLED and IOM. It examines rural and urban displacement patters and assesses host conditions and the status of basic services in urban centres. It looks specifically at the conditions in the cities of Taiz and Aden, as they both create internal displacement and shelter IDPs. It also analyses future intentions and preferences for durable solutions along urban and rural lines.
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