Energy-efficient signal processing in wearable embedded systems: An optimal feature selection approach

Hassan Ghasemzadeh, Navid Amini, Majid Sarrafzadeh

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

11 Scopus citations

Abstract

Many wearable embedded systems benefit from classification algorithms where statistical features extracted from physiological signals are mapped onto different user's states such as health status of a patient or type of activity performed by a subject. Conventionally selected features lead to rapid battery depletion in these battery-operated systems, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which minimizes energy consumption of the signal processing for classification applications. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data demonstrate that our approach can significantly reduce energy consumption of the computing module resulting in more than 30$% energy savings while achieving 96.7% classification accuracy.

Original languageEnglish (US)
Title of host publicationISLPED'12 - Proceedings of the International Symposium on Low Power Electronics and Design
Pages357-362
Number of pages6
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED'12 - Redondo Beach, CA, United States
Duration: Jul 30 2012Aug 1 2012

Publication series

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

Other

Other2012 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED'12
Country/TerritoryUnited States
CityRedondo Beach, CA
Period7/30/128/1/12

Keywords

  • activity recognition
  • embedded signal processing
  • feature selection
  • power optimization
  • wearable monitoring

ASJC Scopus subject areas

  • Engineering(all)

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