Power-aware action recognition with optimal sensor selection: An AdaBoost driven distributed template matching approach

Pasquale Panuccio, Hassan Ghasemzadeh, Giancarlo Fortino, Roozbeh Jafari

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

8 Scopus citations

Abstract

In this paper, we present a distributed action recognition framework that minimizes power consumption of the system subject to a lower bound on the classification accuracy. The system utilizes computationally simple template matching blocks that perform classifications on individual sensor nodes. A boosting approach is employed to enhance accuracy by activating only a subset of sensors optimized in terms of power consumption and can achieve a given lower bound accuracy criterion. Our experimental results on real data shows more than 85% power saving while maintaining 80% sensitivity to detected actions.

Original languageEnglish (US)
Title of host publicationmHealthSys 2011 - Proceedings of the 1st ACM Workshop on Mobile Systems, Applications, and Services for HealthCare - Co-held with ACM SenSys 2011
DOIs
StatePublished - 2011
Externally publishedYes
Event1st ACM Workshop on Mobile Systems, Applications, and Services for Healthcare, mHealthSys 2011, Co-held with ACM SenSys 2011 - Seattle, WA, United States
Duration: Nov 1 2011Nov 1 2011

Publication series

NamemHealthSys 2011 - Proceedings of the 1st ACM Workshop on Mobile Systems, Applications, and Services for HealthCare - Co-held with ACM SenSys 2011

Conference

Conference1st ACM Workshop on Mobile Systems, Applications, and Services for Healthcare, mHealthSys 2011, Co-held with ACM SenSys 2011
Country/TerritoryUnited States
CitySeattle, WA
Period11/1/1111/1/11

Keywords

  • Action recognition
  • Adaboost
  • Collaborative classification
  • Power optimization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Health Informatics
  • Health Information Management

Fingerprint

Dive into the research topics of 'Power-aware action recognition with optimal sensor selection: An AdaBoost driven distributed template matching approach'. Together they form a unique fingerprint.

Cite this