Developing and enabling edge analytics for Internet of Medical Things
This project seeks to investigate and develop robust and resource-based edged algorithms for applications in the Internet of Things and the development of intelligent applications of real-time data-enhanced solutions in the e-Health domains.
Machine learning (ML) and deep learning (DL) are promising tools for developing intelligent applications for various domains, especially in the e-health domain. Unfortunately, these algorithms are typically very resource-intensive and require a lot of computational resources to deploy and test. The Internet of Medical Things (IoMT) is also a resource-constrained ecosystem, despite its evolution and applications in recent times. Meanwhile, due to the ground-breaking deployment of 5G technology, the challenge of resource constraint seems to be solvable as new improved algorithms seek to improve connectivity, cloud-based storage, and extend the computability of machines while expanding the applicability of mobile IoMT (West, 2016; Patel et al., 2017). Evidently, there are emerging applications of edge IoMT devices in care delivery, as connected medicine seems to provide advanced dynamics for obtaining quality care through imaging improvement, diagnostics, and treatment of all types of complex health challenges.
Therefore, this project will be focused on building innovative solutions to design compact and lightweight machine learning and deep learning algorithms to fit, train, and deploy these algorithms on resource-constrained edge devices for tracking, early detection, and diagnosis of diseases without compromising their performance and accuracy. Considering the nature of tracking that is required for the device to perform and the eventual detection or prediction of the possible presence of diseases in the body, distributed and federated learning techniques will be used over multi-modal data sets (e.g. sensor data streams, historical datasets) to provide robust and accurate Artificial Intelligence (AI) models for application in tracking, detecting, and possible enhancement of the different chemotherapy stages for affected patients.
To deploy edged IoMT devices, such as clinical wearables and remote sensors, as well as many other devices that monitor and electronically transmit medical data, such as vital signs, physical activity, personal safety, and medication adherence, the project specifically aims to develop robust algorithms based on big data analytics, learning, and intelligence. Smart objects are now the ideal building blocks for the creation of cyber-physical smart ubiquitous frameworks thanks to the IoMT. With bright technological, economic, and societal prospects, the IoMT revolution is redefining
contemporary healthcare (Islam et al., 2015). These tools will offer diagnosis and treatment options in telemedicine that have never been seen before, all while providing high-quality care at a reasonable cost for patients.
In summary, this project seeks to explore the use of knowledge graph generation and explainable AI techniques over IoMT data for the tracking, detection, and diagnosis of diseases in the human body.