Opportunistic hierarchical classification for power optimization in wearable movement monitoring systems

Francesco Fraternali*, Mahsan Rofouei, Nabil Alshurafa, Hassan Ghasemzadeh, Luca Benini, Majid Sarrafzadeh

*Corresponding author for this work

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

7 Scopus citations

Abstract

Patient monitoring systems are becoming increasingly important in accurately diagnosing and treating growing worldwide chronic conditions especially the obesity epidemic. The ubiquitous nature of wearable sensors, such as the readily available embedded accelerometers in smart phones, provides physicians with an opportunity to remotely monitor their patient's daily activity. There have been several developments in the area of activity recognition using wearable sensors. However, due to power constraints, resource efficient algorithms are necessary in order to perform accurate realtime activity recognition while consuming minimal energy. In this paper, we present a two-tier architecture for optimizing power consumption in such systems. While the first tier relies on a hierarchical classification approach, the second one manages the activation and deactivation of the classification system. We demonstrate this using a series of binary Support Vector Machine classifiers. The proposed approach, however, is classifier independent. Experimenting with subjects performing different daily activities such as walking, going upstairs and down-stairs, standing and sitting, our approach achieves a power savings of 87%, while maintaining 92% classification accuracy.

Original languageEnglish (US)
Title of host publication7th IEEE International Symposium on Industrial Embedded Systems, SIES 2012 - Conference Proceedings
Pages102-111
Number of pages10
DOIs
StatePublished - 2012
Event7th IEEE International Symposium on Industrial Embedded Systems, SIES 2012 - Karlsruhe, Germany
Duration: Jun 20 2012Jun 22 2012

Publication series

Name7th IEEE International Symposium on Industrial Embedded Systems, SIES 2012 - Conference Proceedings

Other

Other7th IEEE International Symposium on Industrial Embedded Systems, SIES 2012
Country/TerritoryGermany
CityKarlsruhe
Period6/20/126/22/12

Keywords

  • Accelerometer
  • Activity Monitoring
  • Hierarchical Classifier
  • Mobile Phone
  • Power Optimization
  • Support Vector Machines

ASJC Scopus subject areas

  • Control and Systems Engineering

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