Artificial Intelligence - Enabled IoT for Health and Wellbeing Monitoring

Thilakarathne, Navod Neranjan; Priyashan, W. D. Madhuka; Premarathna, Chanaka Prasad

doi:10.1109/icccnt51525.2021.9579792

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…As a result, the relevant stakeholders in healthcare should take appropriate actions toward the protection of this medical data so that the data can be protected. On the other hand, when designing/setting up the medical environment and designing and deployment of security solutions, the relevant stakeholders should think that security and privacy protection should be an essential and integral part of the big data life cycle, which is data generation [75][76][77][78][79], data processing and data storage where optimal protection can be guaranteed to the underlying big data. For the healthcare system to profit from the use of big data in healthcare, the following resolves should be made for the effective utilization of big data so that maximum protection can be guaranteed:…”

Section: Discussionmentioning

confidence: 99%

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

2023

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In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.

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Section: Discussionmentioning

confidence: 99%

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

2023

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“…The literature on Federated Learning (FL) and its application in healthcare, particularly in the context of chronic disease management, demonstrates that this is a field that is rapidly evolving and is characterized by ongoing innovation and investigation. In their study, Thilakarathne et al [11] highlight the critical role that FL plays in protecting individuals' privacy within the context of the Internet of Things (IoT) in the medical field. Their concentration on the utilization of methods that are both secure and respectful of individuals' privacy is in line with the growing concern that is being expressed regarding the administration of sensitive medical data.…”

Section: Iiliterature Reviewmentioning

confidence: 99%

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Dipika R. Birari

2024

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Chronic diseases, specifically Cardiovascular Disease (CVD), pose a substantial worldwide health obstacle, requiring innovative and comprehensive approaches to management. This study presents an innovative method for managing chronic diseases by combining Federated Learning (FL) and the Internet of Things (IoT). The goal is to offer tailored healthcare solutions. The study presents a new approach called Federated Transfer Learning (FTL) that incorporates Adaptive Gradient Clipping (AGC) to improve the performance of models and maintain privacy in a distributed network of healthcare devices. The primary objective of this study is to develop a holistic framework that seamlessly amalgamates data from various sources, including wearables, medical devices, and electronic health records, utilizing FL to build a centralized model while preserving data privacy at the individual level. The proposed Faster Than Light (FTL) algorithm with Automatic Gain Control (AGC) enhances the process of model convergence, guaranteeing stability and dependability in a federated learning environment. The research methodology entails implementing the suggested framework in the management of Cardiovascular Disease (CVD). The performance of the model is assessed using the Area Under the Curve (AUC) metric, resulting in an impressive AUC score of 92.4%. This highlights the efficacy of the integrated approach in precisely forecasting and controlling risks associated with cardiovascular diseases (CVD), showcasing its potential for widespread use in managing chronic diseases. The key findings emphasize the advantages of integrating IoT devices into the FL ecosystem, enabling the immediate surveillance and gathering of various health data. The decentralized nature of the learning process enables model training to occur without compromising data privacy, making it suitable for large-scale healthcare systems. Moreover, the implementation of Faster Than Light (FTL) with Automatic Gain Control (AGC) improves the resilience and effectiveness of the federated learning procedure, thereby enhancing the overall achievement of the suggested framework. In conclusion, this study presents a groundbreaking contribution to the field of chronic disease management, specifically targeting Cardiovascular Disease. The combination of Federated Learning and IoT, along with the innovative Federated Transfer Learning with Adaptive Gradient Clipping, not only achieves outstanding predictive accuracy but also guarantees the privacy and security of sensitive health data. The proposed framework holds significant promise for revolutionizing personalized healthcare solutions, paving the way for a more effective and patient-centric approach to chronic disease management.

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“…The potential of AI and IoT to create insights from massive amounts of patient data is one of the primary benefits offered by these technologies in the medical field. Data from sources such as electronic health records (EHRs), medical imaging, and wearable devices can be overwhelming and convoluted to make sense of [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring

Dr. Rahul Pulimamidi, Prabu Ravichandran

2023

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Research in Remote Patient Monitoring Systems (RPMS) is highly valued since it directly impacts people's health and wellbeing. The number of cases being treated with RPMS has increased since the beginning of the pandemic. However, there are still challenges to be met, such as mobility, heterogeneous networks, standardisation of RPMSs, automation, and Quality of Service (QoS), despite the rise in their use. The incorporation of artificial intelligence (AI) into remote patient monitoring (RPM) is causing a revolution in the healthcare industry by improving the quality of care provided to patients, boosting operational effectiveness, and making it possible to intervene earlier. RPM makes use of technology to monitor the health of patients in a remote location, hence minimising the frequency of in-person visits that are required. The addition of AI algorithms further enhances the potential of RPM by analysing huge volumes of patient data to identify patterns, abnormalities, and potential problems. In this study, we will investigate the most potentially game-changing applications of AI in remote patient monitoring in 2023 and beyond.

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Artificial Intelligence - Enabled IoT for Health and Wellbeing Monitoring

Cited by 7 publications

References 32 publications

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring

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