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StopWatch: The preliminary evaluation of a smartwatch-based system for passive detection of cigarette smoking

Research output: Contribution to journalArticle

Original languageEnglish
Article numbernty008
Number of pages5
JournalNicotine and Tobacco Research
Early online date24 Jan 2018
DateAccepted/In press - 11 Jan 2018
DateE-pub ahead of print (current) - 24 Jan 2018


Introduction: Recent developments in smoking cessation support systems and interventions have highlighted the requirement for unobtrusive, passive ways to measure smoking behaviour. A number of systems have been developed for this that either use bespoke sensing technology, or expensive combinations of wearables and smartphones. Here we present StopWatch, a system for passive detection of cigarette smoking that runs on a low-cost smartwatch and does not require additional sensing or a connected smartphone.

Methods: Our system uses motion data from the accelerometer and gyroscope in an Android smartwatch to detect the signature hand movements of cigarette smoking. It uses machine learning techniques to transform raw motion data into motion features, and in turn into individual drags and instances of smoking. These processes run on the smartwatch, and do not require a smartphone.

Results: We conducted preliminary validations of the system in daily smokers (n=13) in laboratory and free-living conditions running on an Android LG G-Watch. In free-living conditions, over a 24-hour period, the system achieved precision of 86% and recall of 71%.

Conclusions: StopWatch is a system for passive measurement of cigarette smoking that runs entirely on a commercially available Android smartwatch. It requires no smartphone so the cost is low, and needs no bespoke sensing equipment so participant burden is also low. Performance is currently lower than other more expensive and complex systems, though adequate for some applications. Future developments will focus on enhancing performance, validation on a range of smartwatches, and detection of electronic cigarette use.

Implications: We present a low-cost, smartwatch-based system for passive detection of cigarette smoking. It uses data from the motion sensors in the watch to identify the signature hand movements of cigarette smoking. The system will provide the detailed measures of individual smoking behaviour needed for context-triggered just-in-time smoking cessation support systems, and to enable just-in-time adaptive interventions. More broadly, the system will enable researchers to obtain detailed measures of individual smoking behaviour in free-living conditions that are free from the recall errors and reporting biases associated with self-report of smoking.

    Structured keywords

  • Digital Health
  • Brain and Behaviour
  • Cognitive Science
  • Tactile Action Perception
  • Tobacco and Alcohol

    Research areas

  • smartwatch, passive smoking detection, smoking cessation support

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Oxford University Press at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 166 KB, PDF document


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