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

Sensors

Reza Rawassizadeh²  Martin Tomitsch³  Manouchehr Nourizadeh⁴  Elaheh Momeni¹  Aaron Peery²  Liudmila Ulanova²  Michael Pazzani² 

[1] Multimedia Information System Group, University of Vienna, Vienna 1090, Austria; E-Mail:;Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521, USA; E-Mails:;Design Lab, The University of Sydney, Sydney 2006 NSW, Australia; E-Mail:;Vienna University of Technology, Vienna 1040, Austria; E-Mail:

关键词: wearable;    smartwatch;    mobile sensing;    prediction;    energy efficiency;    lifelogging;    quantified self;   

DOI  :  10.3390/s150922616

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.