Abstract
We present the design, development, and evaluation of a personalised, privacy-aware and multi-modal wearable-only system to model interruptibility. Our system runs as a background service of a wearable OS and operates on two key techniques: i) online learning to recognise interruptible situation at a personal scale and ii) runtime inference of opportune moments for an interruption. .e former is realised by a set of fast and ecient algorithms to automatically discover and learn interruptible situations as a function of meaningful places, and physical and conversational activities with active user engagement. .e la.er is substantiated with a multiphased context sensing mechanics to identify moments which are then utilised to delivery noti€cations and interactive contents at the right moment. Early experimental evaluation of our system shows a sharp 46% increase in the response rate of noti€cations in wearable se.ings at the expense of negligible 6.3% resource cost.
Original language | English |
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Title of host publication | WearSys 2018 - Proceedings of the 4th ACM Workshop on Wearable Systems and Applications |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 27-32 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-4503-5842-2 |
DOIs | |
Publication status | Published - 2018 |
Event | 4th ACM Workshop on Wearable Systems and Applications, WearSys 2018: The 4th ACM Workshop on Wearable Systems and Applications - Munich, Germany Duration: 10 Jun 2018 → 10 Jun 2018 Conference number: 4th |
Conference
Conference | 4th ACM Workshop on Wearable Systems and Applications, WearSys 2018 |
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Abbreviated title | WearSys 2018 |
Country/Territory | Germany |
City | Munich |
Period | 10/06/18 → 10/06/18 |