Sensors 2015, 15, 22616-22645; doi:10.3390/s150922616 sensorsOPEN ACCESS ISSN 1424-8220 www.mdpi.com/journal/sensors Article Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches Reza Rawassizadeh 1;*, Martin Tomitsch 2, Manouchehr Nourizadeh 3, Elaheh Momeni 4, Aaron Peery 1, Liudmila Ulanova 1 and Michael Pazzani 1 1 Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521, USA; E-Mails:
[email protected] (A.P.);
[email protected] (L.U.);
[email protected] (M.P.) 2 Design Lab, The University of Sydney, Sydney 2006 NSW, Australia; E-Mail:
[email protected] 3 Vienna University of Technology, Vienna 1040, Austria; E-Mail:
[email protected] 4 Multimedia Information System Group, University of Vienna, Vienna 1090, Austria; E-Mail:
[email protected] * Author to whom correspondence should be addressed; E-Mail:
[email protected]; Tel.: +1-951-827-2536. Academic Editor: Vittorio M. N. Passaro Received: 12 August 2015 / Accepted: 31 August 2015 / Published: 8 September 2015 Abstract: As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches.