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)


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.


Hidden Markov model Smartphone sensor data Tobacco Smoking monitoring 


  1. 1.
    Lopez-Meyer, P., Tiffany, S., Patil, Y., Sazonov, E.: Monitoring of cigarette smoking using wearable sensors and support vector machines. IEEE Trans. Biomed. Eng. 60(7), 1867–1872 (2013)CrossRefGoogle Scholar
  2. 2.
    Ali, A., Hossain, S., Hovsepian, K., Rahman, M., Plarre, K., Kumar, S.: mPuff: Automated detection of cigarette smoking puffs from respiration measurements. In: IPSN 2012 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks, pp. 269–280 (2012)Google Scholar
  3. 3.
    Community Preventive Services Task Force: Reducing tobacco use and secondhand smoke exposure: mobile phone-based cessation interventions (2013)Google Scholar
  4. 4.
  5. 5.
    Hashemian, M., Knowles, D., Calver, J., Qian, W., Bullock, M.C., Bell, S., Mandryk, R.L., Osgood, N., Stanley, K.G.: iEpi: an end to end solution for collecting, conditioning and utilizing epidemiologically relevant data. In: Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare, pp. 3–8. ACM (2012)Google Scholar
  6. 6.
    Meredith, S.E., Robinson, A., Erb, P., Spieler, C.A., Klugman, N., Dutta, P., Dallery, J.: A mobile-phone-based breath carbon monoxide meter to detect cigarette smoking. Nicotine Tob. Res. 16(6), 766–773 (2014)CrossRefGoogle Scholar
  7. 7.
    Qian, W., Stanley, K.G., Osgood, N.D.: The impact of spatial resolution and representation on human mobility predictability. In: Liang, S.H.L., Wang, X., Claramunt, C. (eds.) W2GIS 2013. LNCS, vol. 7820, pp. 25–40. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37087-8_3 CrossRefGoogle Scholar
  8. 8.
    Raja, M.: Diagnostic methods for detection of cotinine level in tobacco users: a review. J. Clin. Diagn. Res. 10(3), 4–6 (2016)Google Scholar
  9. 9.
    Sazonov, E., Lopez-Meyer, P., Tiffany, S.: A wearable sensor system for monitoring cigarette smoking. J. Stud. Alcohol Drugs 74(6), 956–964 (2013)CrossRefGoogle Scholar
  10. 10.
    Scholl, P.M., van Laerhoven, K.: A feasibility study of wrist-worn accelerometer based detection of smoking habits. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 886–891 (2012)Google Scholar
  11. 11.
    WHO: Tobacco Factsheet (2016).

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

Personalised recommendations