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.
2Department of Computer Architecture and Automation, Complutense University of Madrid, Madrid, Spain.


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.

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