Adaptive Compressed Sensing at the Fingertip of Internet-of-Things Sensors: An Ultra-Low Power Activity Recognition
Ramin Fallahzadeh1,a, Josue Pagan Ortiz2 and Hassan Ghasemzadeh1,b
1School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington.
arfallahz@eecs.wsu.edu
bhassan@eecs.wsu.edu
2Department of Computer Architecture and Automation, Complutense University of Madrid, Madrid, Spain.
jpagan@ucm.es
ABSTRACT
With the proliferation of wearable devices in the Internet-of-Things applications, designing highly power-efficient solutions for continuous operation of these technologies in life-critical settings emerges. We propose a novel ultra-low power framework for adaptive compressed sensing in activity recognition. The proposed design uses a coarse-grained activity recognition module to adaptively tune the compressed sensing module for minimized sensing/transmission costs. We pose an optimization problem to minimize activity-specific sensing rates and introduce a polynomial time approximation algorithm using a novel heuristic dynamic optimization tree. Our evaluations on real-world data shows that the proposed autonomous framework is capable of generating feedback with +80% confidence and improves power reduction performance of the state-of-the-art approach by a factor of two.
