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Sensors 2015, 15(9), 22616-22645; doi:10.3390/s150922616

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches

1
Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521, USA
2
Design Lab, The University of Sydney, Sydney 2006 NSW, Australia
3
Vienna University of Technology, Vienna 1040, Austria
4
Multimedia Information System Group, University of Vienna, Vienna 1090, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 12 August 2015 / Accepted: 31 August 2015 / Published: 8 September 2015
(This article belongs to the Section State-of-the-Art Sensors Technologies)
View Full-Text   |   Download PDF [819 KB, uploaded 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. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras. View Full-Text
Keywords: wearable; smartwatch; mobile sensing; prediction; energy efficiency; lifelogging; quantified self wearable; smartwatch; mobile sensing; prediction; energy efficiency; lifelogging; quantified self
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rawassizadeh, R.; Tomitsch, M.; Nourizadeh, M.; Momeni, E.; Peery, A.; Ulanova, L.; Pazzani, M. Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. Sensors 2015, 15, 22616-22645.

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