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About this Workshop

The availability of affordable wearable Internet of Things (wIoT) and edge devices with embedded sensors has revolutionized intelligent health and wellness applications. Users often use wIoT and smartphones to collect medical data and send them to the cloud for further analysis. Edge-based solutions, where analysis and inference of such data are carried out on edge devices, have been proposed to address users' security and privacy concerns since users' sensitive data is not transferred to untrusted cloud servers for inferencing. However, resource constraints on the edge devices also pose challenges in using deep learning solutions. Research needs to be conducted to produce efficient system designs, algorithms, and deep learning models that can be deployed in edge devices. Such outcomes will enable better personalization of health-related solutions and enhance users' experience. Furthermore, thanks to the ever-improving voice recognition and synthesis schemes, many wearables and smartphone applications now rely on voice assistants to interact with users. Existing work has shown that such interactions can significantly improve users' experience but incur significant security and privacy issues. This workshop aims to fill the gap between deep learning for intelligent healthcare and power-constrained wIoT and edge and create impactful solutions to help in the well beings of users.

Important Dates

  • Submission Deadline: May 7 2021
  • Acceptance Notice: June 4 2021
  • Camera-ready Deadline: June 11 2021
  • Workshop Date: TBD

Workshop Chairs

Yan Wang

Deparment of Computer and Information Sciences
Temple University

Jerry Cheng

Department of Computer Science
New York Institute of Technology