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InstantRR: Instantaneous Respiratory Rate Estimation on Context-Aware Mobile Devices

  • Md. Mahbubur RahmanEmail author
  • Ebrahim Nemati
  • Viswam Nathan
  • Jilong Kuang
Conference paper
  • 44 Downloads
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death both in the USA and worldwide. Respiratory rate is an important predictor for acute COPD exacerbation and an indicator of overall well-being for healthy individuals. Current methods to measure respiratory rate either involve uncomfortable, specialized sensors such as a chestband or are less resilient to varying real-life situations. In this paper, we present a novel context-aware framework that can reliably estimate respiratory rate using data from sensors embedded in users’ existing mobile devices such as smartphones and smartwatches. Our approach takes current contexts, such as device placement, user’s social interaction, and user’s pulmonary health condition into consideration, and finds the optimal fusion across sensor streams and algorithms. We show that our approach can handle varying user contexts (e.g., detecting device placement with an accuracy of 97%) and reliably estimate respiratory rate with errors as low as 0.85 breaths per minute.

Keywords

Mobile sensor fusion Context-awareness Mobile physiological sensing Smartwatch Smartphone Respiration Inertial measurement unit (IMU) Data analytics Acute COPD exacerbation 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Md. Mahbubur Rahman
    • 1
    Email author
  • Ebrahim Nemati
    • 1
  • Viswam Nathan
    • 1
  • Jilong Kuang
    • 1
  1. 1.Digital Health LabSamsung Research AmericaMountain ViewUSA

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