https://sites.google.com/view/edgedl-ieee-acm-chase-2019/home
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 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 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 device. 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.
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 of edge-based machine learning
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
Workshop Paper Submission due: July 15, 2019 (Strict deadline)
Workshop Paper Acceptance: July 21, 2019
Workshop Camera-Ready Paper: July 31, 2019 (Strict deadline)
Prospective authors are invited to submit full-length papers (up to six pages plus 1 page with extra charge) for technical content including figures and references. Submitted manuscripts should be single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style - download the template). Manuscripts should be original (not submitted or published anywhere else). Papers will be accepted only by electronic submission via the IEEE/ACM CHASE 2019 EDAS Submission link:
Accepted workshop papers will be included in proceedings to be published by IEEE CPS and indexed by IEEE Explore (https://ieeexplore.ieee.org/Xplore/home.jsp)
Harishchandra Dubey, Microsoft, USA [hadubey@microsoft.com]
Kunal Mankodiya, University of Rhode Island, USA [kunalm@uri.edu]
Amir M. Rahmani, University of California Irvine, USA [a.rahmani@uci.edu]
Utsav Drolia, NEC Laboratories America Inc., USA [utsav@nec-labs.com]
Shaad Mahmud, University of Massachusetts Dartmouth, USA
Nikil Dutt, University of California, Irvine, USA
Ankesh Jain, IIT Delhi, India
Axel Jantsch, TU Wien, Austria
Abhinav Misra, Educational Testing Service (ETS), USA
Shivesh Ranjan, Apple Inc., USA
Fatemeh Saki, Qualcomm Inc., San Diego, USA
Puneet Goyal, IIT Ropar, India
Pasi Liljeberg, University of Turku, Finland
C. P. Ravikumar, Texas Instrument, India
Geng Yang, Zhejiang University, China