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Identifying Smoking from Smartphone Sensor Data and Multivariate Hidden Markov Models

  • Yang QinEmail author
  • Weicheng Qian
  • Narjes Shojaati
  • Nathaniel Osgood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Smoking is one of the foremost public health threats listed by the World Health Organization, and surveillance is a key to informing effective policies. High smartphone penetration and mature smartphone sensor data collecting techniques make smartphone sensor data based smoking monitoring viable, yet an effective classification algorithm remains elusive. In this paper, we sought to classify smoking using multivariate Hidden Markov models (HMMs) informed by binned time-series of transformed sensor data collected with smartphone-based Wi-Fi, GPS, and accelerometer sensors. Our model is trained on smartphone sensor time series data labeled with self-reported smoking periods. Two-fold cross-validation shows \(A_{z}\) (area under receiver operating characteristic curve) for HMMs using five features = (0.52, 0.84). Comparison of univariate HMMs and multivariate HMMs, suggests a high accuracy of multivariate HMMs for smoking periods classification.

Keywords

Hidden Markov model Smartphone sensor data Tobacco Smoking monitoring 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yang Qin
    • 1
    Email author
  • Weicheng Qian
    • 1
  • Narjes Shojaati
    • 1
  • Nathaniel Osgood
    • 1
  1. 1.University of SaskatchewanSaskatoonCanada

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