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HEALTH
DATA ANALYTICS
INTERNET OF THINGS
WEARABLE COMPUTING
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About |
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Wearable Internet of Things (wIoT) together with deep learning is revolutionizing the smart health and wellbeing applications. Predominantly, IoT devices are good at acquiring medical data and later sending it to the cloud. Edge-based deep learning infuses intelligence in terms of processing, analysis, and inference on edge devices such as wearables. Edge-based 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 the 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 fitness 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 devices. Deep learning on edge also allows for personalization of medical solutions that enhances the user’s experience. Increasingly more wearables in health and fitness now rely on voice-based assistants. Recently, several custom chips with medical machine learning functionalities are developed to further advance edge deep learning. 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. |
Call for Papers |
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This workshop invites researchers from academia and industry to submit their current research for fostering academia-industry collaboration. The scope of this workshop includes but not limited to the following topics: ● Resource-constrained deep learning for wearable IoT ● Deep machine learning for sensing, analysis, and interpretation in IoT healthcare ● Low latency decoding on edge for smart health ● Deep learning & AI for regenerative medicine ● Knowledge transfer and model compressions of deep neural networks for smart health ● Deep learning-based health & fitness devices, systems, and services ● Recent advances in Edge, Fog and Mist computing for machine learning in healthcare & fitness application ● Context-aware pervasive health systems based on edge machine learning ● End-to-end deep learning for health and fitness applications ● Scalability, privacy, and security aspects in IoT medical big data ● Edge devices with custom hardware for medical deep learning ● Emerging applications of edge devices in fitness and smart health applications ● Deep learning for personalized health and fitness monitoring, tracking and control ● Information theoretic, semi-supervised and unsupervised machine learning for health and fitness applications ● Design and development of open-source tools for edge machine learning ● Edge-coordinated health data analysis, visualization, and interoperability ● Role of big data in edge-based machine learning for smart health & fitness applications ● Edge based machine learning for blockchain in smart health ● Edge machine learning for Neuromorphic AI and cognitive computing in smart health ● Bio-inspired machine learning for Fog computing systems in healthcare ● Data mining for wearables and mobile devices ● Data storage, retrieval and transfer between edge devices, gateways, and cloud backend ● Cloud-assisted backup and recovery for data mining in IoT ● Real-time knowledge discovery for IoT ● Knowledge graphs and knowledge representation for smart health and IoT |
Summary |
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DASH 2020 : International Workshop on Data Analytics for Smart Health will take place in Atlanta, GA, USA. It’s a 1 day event starting on Dec 10, 2020 (Thursday) and will be winded up on Dec 10, 2020 (Thursday). DASH 2020 falls under the following areas: HEALTH, DATA ANALYTICS, INTERNET OF THINGS, WEARABLE COMPUTING, etc. Submissions for this Workshop can be made by Oct 01, 2020. Authors can expect the result of submission by Nov 01, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Nov 15, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the DASH 2020
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Credits and Sources |
| [1] DASH 2020 : International Workshop on Data Analytics for Smart Health |
