PuffMarker: A multi-sensor approach for pinpointing the timing of first lapse in smoking cessation

Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa Al'Absi, Santosh Kumar

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

72 Scopus citations

Abstract

Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors - breathing pattern from respiration and arm movements from 6-Axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.

Original languageEnglish (US)
Title of host publicationUbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages999-1010
Number of pages12
ISBN (Electronic)9781450335744
DOIs
StatePublished - Sep 7 2015
Externally publishedYes
Event3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 - Osaka, Japan
Duration: Sep 7 2015Sep 11 2015

Publication series

NameUbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Other

Other3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015
CountryJapan
CityOsaka
Period9/7/159/11/15

Keywords

  • Mobile health (mHealth)
  • Smartwatch
  • Smoking cessation
  • Smoking detection
  • Wearable sensors

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