An Energy-Efficient Computational Model for Uncertainty Management in Dynamically Changing Networked Wearables

Ramyar Saeedi, Ramin Fallahzadeh, Parastoo Alinia, Hassan Ghasemzadeh

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

12 Scopus citations

Abstract

The utility of wearables is currently limited to lab experiments and controlled environments mainly because computational algorithms embedded in wearables fail to produce accurate measurements in uncontrolled, dynamically changing, and potentially harsh environments. With the exponentially growing adoption of these systems in human-centered Internet-of-Things (IoT) applications, development of resource-efficient solutions to enhance the accuracy of this systems remains a considerable research challenge. In this paper, we introduce an energy-efficient framework for uncertainty management of networked wearables. The core components of our framework are anomaly screening units for detecting anomalies that require handling, thus resulting in one order of magnitude less energy consumption compared to the conventional frameworks. Furthermore, our screening approach achieves 98.3% accuracy in detecting anomalies based on real data collected with wearable motion sensors.

Original languageEnglish (US)
Title of host publicationISLPED 2016 - Proceedings of the 2016 International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-51
Number of pages6
ISBN (Electronic)9781450341851
DOIs
StatePublished - Aug 8 2016
Externally publishedYes
Event21st IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2016 - San Francisco, United States
Duration: Aug 8 2016Aug 10 2016

Publication series

NameProceedings of the International Symposium on Low Power Electronics and Design
ISSN (Print)1533-4678

Conference

Conference21st IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2016
Country/TerritoryUnited States
CitySan Francisco
Period8/8/168/10/16

Keywords

  • accelerometer
  • activity monitoring
  • feature selection
  • Networked wearables
  • power optimization
  • reliability
  • uncertainty management

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'An Energy-Efficient Computational Model for Uncertainty Management in Dynamically Changing Networked Wearables'. Together they form a unique fingerprint.

Cite this