Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

Xiao, Zheng; Shah, Syed Bilal Hussian; Ren, Xiaojun; Li, Fengqi; Nawaf, Liqaa; Chakraborty, Chinmay; Fayaz, Muhammad

doi:10.1155/2021/4410894

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…The research community has widely adopted the approach for IoT applications. Zheng et al (2021) proposed a federated transfer learning mechanism for the internet of medical things (IoMT) healthcare. Nguyen et al (2023) discussed types of federated learning frameworks for smart healthcare, benefits, requirements, federated learning applications in applications, trends, and challenges.…”

Section: Ai Impact On Rpmmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

108

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The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

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Section: Ai Impact On Rpmmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

108

View full text Add to dashboard Cite

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“…The privacy-sensitive issue of patient data must also be addressed by federated learning. As a result, FT-IoMT Health is implemented in hospitals to aid in the diagnosis and treatment of PD [189]. The patient downloads the user model to the biosensor after training it on the user side, then connects to the network to update it before the subsequent access.…”

Section: Federated Learningmentioning

confidence: 99%

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Dixit

Bohre²,

Singh

et al. 2023

Electronics

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Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.

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“…Healthcare monitoring could be used through the medical devices in the hospitals and wearable devices for in-house patients. A key application for the emerging communications standards is in healthcare for patients' medical records [36].…”

Section: Federated Learning With Ehr Datamentioning

confidence: 99%

“…Privacy protection with addition of differential privacy noise MNIST dataset, prevented a single point of failure and better protection against malicious interference [43] An algorithm termed FT-IoMT Health was proposed for data aggregation from the participating clients ensuring security and privacy by employing transfer learning [36]. The proposed model was validated using human activity detection and showed improved results compared to traditional ML models [36].…”

Section: General Datamentioning

confidence: 99%

“…A biosensing application was developed for Parkinson's disease diagnosis by combining the acceleration and gyroscope signals [36]. Each of the symptom of walking, postural normal tremor, resting tremor, arm swing, and balance were divided into five levels ranging from normal to severe [36]. The data from three hospitals was used for evaluation and the proposed model showed optimum results [36].…”

Section: Federated Learning With Multi-modal Datamentioning

confidence: 99%

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Federated Learning for Internet of Medical Things

Tanwar¹,

Verma²,

Bhattacharya³

2023

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The clinical adoption of state-of-the-art artificial intelligence (AI) applications for disease diagnosis with digital healthcare technologies will ease the burden on medical practitioners, ensure timely interventions, and aid patients' welfare. The digital innovations in medical data acquisition have made it possible to collect multi-modal data through Internet-of-Medical-Things (IoMT) but the privacy preserving nature and compliance to medical data regulations often precludes data sharing across organizational and geographical boundaries.An AI model requires high quality and large-scale training datasets collected from diverse sources to mitigate bias and aid better prediction accuracy on the unseen data. With secure and trusted federated learning, the data of an organization can stay local and yet contribute to AI models' training for building better trained models with improved accuracy and generalization. Thus, the trusted federated learning approaches facilitate sharing the trained AI models across different participating healthcare organizations by breaking down barriers, increasing trust, and preserving privacy for better disease predictions and diagnoses that can increase deployment of AI models in clinical practice. In this chapter, we provide the current state-of-the-art solutions, adoption challenges and future research directions, and a framework for trusted federated learning in healthcare applications.

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Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

Cited by 8 publications

References 18 publications

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Federated Learning for Internet of Medical Things

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