Dynamic Computation Offloading for Low-Power Wearable Health Monitoring Systems

Haik Kalantarian*, Costas Sideris, Bobak Mortazavi, Nabil Alshurafa, Majid Sarrafzadeh

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Objective: The objective of this paper is to describe and evaluate an algorithm to reduce power usage and increase battery lifetime for wearable health-monitoring devices. Methods: We describe a novel dynamic computation offloading scheme for real-Time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy. Results: By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%. Conclusion: We demonstrate that computation offloading can be applied to real-Time monitoring systems, and yields significant power savings. Significance: Making correct offloading decisions for health monitoring devices can extend battery life and improve adherence.

Original languageEnglish (US)
Article number7471450
Pages (from-to)621-628
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number3
DOIs
StatePublished - Mar 1 2017

Keywords

  • Machine learning
  • pervasive computing
  • power optimization
  • signal processing

ASJC Scopus subject areas

  • Biomedical Engineering

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