June 10, 2018

Poster: On-Wearable AI to Model Human Interruptibility

  • Acer U.
  • Forlivesi C.
  • Kawsar F.
  • Van Den Broeck M.

Understanding human interruptibility has a profound implication on computational user experience in a mobile setting. Many studies have explored this challenging topic and more recently in the context of mobile notification management. We aim to further augment this rich body of literature by presenting a multi-modal wearable sensing system to model personal and social interruptibility. Our system runs as a background service of a wearable OS and is built on two key techniques: i) online learning to recognize interruptible situation at a personal scale and ii) runtime inference of opportune moments for an interruption.

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