Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition

Ramin Fallahzadeh, Josue Pagan Ortiz, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages996-1001
Number of pages6
ISBN (Electronic)9783981537093
DOIs
StatePublished - May 11 2017
Externally publishedYes
Event20th Design, Automation and Test in Europe, DATE 2017 - Swisstech, Lausanne, Switzerland
Duration: Mar 27 2017Mar 31 2017

Publication series

NameProceedings of the 2017 Design, Automation and Test in Europe, DATE 2017

Other

Other20th Design, Automation and Test in Europe, DATE 2017
Country/TerritorySwitzerland
CitySwisstech, Lausanne
Period3/27/173/31/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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