Title
Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing
Abstract
Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the -wearable system's energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy.
Year
DOI
Venue
2005
10.1109/ISWC.2005.52
ISWC
Keywords
Field
DocType
notification platform,prediction accuracy,context-aware wearable computing,power consumption,selective sampling strategy,wearable sensor,wearable computing platform,optimized sampling scheme,wearable system,power consumption technique,context-aware mobile computing,mobile computing,data collection,sensors,wearable computer,mobile computer,wearable computers,low frequency
Mobile computing,Software deployment,Computer science,Wearable computer,Accelerometer,Real-time computing,Sampling (statistics),Continuous sensing,Power consumption,Embedded system,Computation
Conference
ISBN
Citations 
PageRank 
0-7695-2419-2
67
4.59
References 
Authors
8
9
Name
Order
Citations
PageRank
Andreas Krause15822368.37
Matthias Ihmig2785.64
Edward Rankin3674.59
Derek Leong416110.86
Smriti Gupta51258.13
Dan Siewiorek646421165.50
Asim Smailagic71740345.37
Michael Deisher861444.23
Uttam Sengupta9998.07