Objective Detection of Cigarette Smoking from Physiological Sensor Signals.

Cigarette smoking has severe health impacts on those who smoke and the people around them. Several wearable sensing modalities have recently been investigated to collect objective data on daily smoking, including detection of smoking episodes from breathing patterns, hand to mouth behavior, and characteristic hand gestures or cigarette lighting events. In order to provide new insight into ongoing research on the objective collection of smoking-related events, this paper proposes a novel method to identify smoking events from the associated changes in heart rate parameters specific to smoking. The proposed method also accounts for the breathing rate and body motion of the person who is smoking to better distinguish these changes from intense physical activities. In this research, a human study was first performed on 20 daily cigarette smokers to record heart rate, breathing rate, and body acceleration collected from a wearable chest sensor consisting of an ECG and bioimpedance measurement sensor and a 3D inertial sensor. Each participant spent ~2 hours in a laboratory environment (mimicking daily activities that included smoking 4 cigarettes) and ~24 hours under unconstrained free-living conditions. A support vector machine-based classifier was developed to automatically detect smoking episodes from the captured sensor signals using fifteen features selected by a forward-feature selection method. In a leave one subject out cross-validation, the proposed approach detected smoking events (187 out of total 232) with the sensitivity and F-score accuracy of 0.87 and 0.79, respectively, in the laboratory setting (known activities) and 0.77 and 0.61, respectively, under free-living conditions. These results validate the proof-of-concept that, although further research is necessary for performance improvement, characteristic changes in heart rate parameters could be a useful indicator of cigarette smoking even under free-living conditions.