mPuff: Automated detection of cigarette smoking puffs from respiration measurements

Abstract

Smoking has been conclusively proved to be the leading cause of mortality that accounts for one in five deaths in the United States. Extensive research is conducted on developing effective smoking cessation programs. Most smoking cessation programs achieve low success rate because they are unable to intervene at the right moment. Identification of high-risk situations that may lead an abstinent smoker to relapse involve discovering the associations among various contexts that precede a smoking session or a smoking lapse. In the absence of an automated method, detection of smoking events still relies on subject self-report that is prone to failure to report and involves subject burden. Automated detection of smoking events in the natural environment can revolutionize smoking research and lead to effective intervention. In this paper, we present mPuff a novel system to automatically detect smoking puffs from respiration measurements, using which a model can be developed to automatically detect entire smoking episodes in the field. We introduce several new features from respiration that can help classify individual respiration cycles into smoking puffs or non-puffs. We then propose supervised and semi-supervised support vector models to detect smoking puffs. We train our models on data collected from 10 daily smokers and find that smoking puffs can be detected with an accuracy of 91% within a smoking session. We then consider respiration measurements during confounding events such as stress, speaking, and walking, and show that our model can still identify smoking puffs with an accuracy of 86.7%. The smoking detector presented here opens the opportunity to develop effective interventions that can be delivered on a mobile phone when and where smoking urges may occur, thereby improving the abysmal low rate of success in smoking cessation.