Detection of smoking events from confounding activities of daily living

Jianchao Lu*, Jiaxing Wang, Xi Zheng, Chandan Karmakar, Sutharshan Rajasegarar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

11 Citations (Scopus)


Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation such as counselling program, motivational interview and pharmacotherapy. However, these methods lack providing real time personalized intervention messages to the smoking addicted users. The challenge is to develop an automated smoking behavior detection. We address this challenge by proposing a non-invasive sensor based automated framework for smoking behavior detection. We used a wristband based accelerometer and gyroscope sensors to detect smoking activities, differentiating with the closely confounding activities. We extract several features using learning algorithms and the empirical results with our participants show good accuracy in detecting the smoking activity in terms of precision, recall, and F1-score.

Original languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450366038
Publication statusPublished - 2019
EventAustralasian Conference on Health Informatics and Knowledge Management (12th : 2019) - Macquarie University, Sydney, Australia
Duration: 29 Jan 201931 Jan 2019


ConferenceAustralasian Conference on Health Informatics and Knowledge Management (12th : 2019)
Abbreviated titleHIKM 2019


  • Activity recognition
  • Mobile and wearable computing systems and services
  • Pervasive technologies for healthcare


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