• Workshop proposal submission due: April 21, 2019
  • Notification of workshop approval: May 1, 2019
  • Workshop Paper Submission: It may vary depending on the workshop.


    MCPS : Medical Cyber Physical Systems in Internet of Medical Things Workshop

    Medical Cyber-Physical Systems (CPS) encompass a new generation of smart medical systems that integrate human, cyber, and physical elements in closed-loop control. They aim to improve patient care by enabling the delivery of advanced therapies and complex surgeries. An example is the artificial pancreas that allows people with diabetes to better manage their condition. Designing safe and effective Medical CPS involves the work of a multi-disciplinary team of engineers, medical domain experts, and human factors specialists. This work needs to be supported by rigorous development processes and tools, as substantial evidence needs to be documented and integrated to justify design choices and ease the review process mandated by regulation. The objectives of MedCPS workshop 2019 are to provide opportunities for researchers, industrial practitioners, caregivers, and government agencies to demonstrate innovative development methods and tools, present experience reports, discuss open challenges, and explore ideas for future development of Medical CPS as it relates to the Internet of Medical Things. Contributions are welcome on all aspects of system development, including specification, design, analysis, implementation, documentation, and certification of Medical CPS. Demonstrations of existing tools for design and analysis of Medical CPS are also encouraged. More details can be found on workshop webpage: MCPS

    SCCH : Secure and Cloud Connected Health

    The use of cloud computing and BigData in health has been very prominent recently, as several features in the cloud are being used to deploy health related applications. On the other hand, the security of ehealth system has become one of the main concerns. For instance, health monitoring, health data storage, health data collection, mobile health, pervasive health, Healthcare monitoring, telemedicine, context-aware computing, ubiquitous computing, Internet of Things (IoT), blockchain, processing health data, securing health data in the cloud, are areas of interest that are being addressed using cloud computing techniques. On the other hand, several challenging issues have raised due to this. This include the quality of health data, the ability to retrieve information and use it in health context, privacy and security issues of health data while being processed in the cloud, availability of health data, models of context and tele-monitoring of contextual applications. This workshop solicits unpublished research work related to the latest challenges, technologies, solutions and techniques pertaining to connected health within the secure and cloud datacenter and to the efficient and efficient cloud solution and hosting of the various emerging applications and services in health domain such as pervasive, health, mobile health, electronic health records, BigData health applications, health networking, and health connectivity within cloud systems. More details can be found on workshop webpage: SCCH

    EdgeDL2019 : Deep Learning on Edge for Smart Health and Wellbeing Applications

    We live in exciting times when wearables and deep learning are growing in parallel and together creating tremendous impact on smart health & fitness devices, systems and services. Wearable Internet of Things (wIoT) together with deep learning is revolutionizing the smart health and fitness applications. Predominantly IoT devices are good at acquiring medical data and later sending it cloud. Edge-based deep learning infuse intelligence in terms of processing, analysis and inference on edge near wearables. Edge deep learning not only offload the cloud but also ensure high-throughput, low- latency solutions. With edge deep learning, the data is processed on edge leading to improved privacy and security as now the data is not transferred to cloud for inference. Resource constraints on the edge and endpoint IoT devices pose challenges in adopting deep learning solutions. Systems and algorithms deployed in health and wellbeing devices require research on efficient approaches for signal sensing, analysis and prediction. Recently, deep learning models are increasingly deployed on wearable and edge devices for neural prediction and inference. Modern smartwatches and smart textiles are health as well as fitness device. Deep learning on edge also allows for personalization of medical solutions that enhances the user’s experience. EdgeDL 2019 workshop invites researchers from academia and industry to submit their current research for fostering academia-industry collaboration. More details can be found on workshop webpage: EdgeDL2019

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