  The consumption of tobacco has reached global epidemic proportions and is
characterized as the leading cause of death and illness. Among the different
ways of consuming tobacco (e.g., smokeless, cigars), smoking cigarettes is the
most widespread. In this paper, we present a two-step, bottom-up algorithm
towards the automatic and objective monitoring of cigarette-based, smoking
behavior during the day, using the 3D acceleration and orientation velocity
measurements from a commercial smartwatch. In the first step, our algorithm
performs the detection of individual smoking gestures (i.e., puffs) using an
artificial neural network with both convolutional and recurrent layers. In the
second step, we make use of the detected puff density to achieve the temporal
localization of smoking sessions that occur throughout the day. In the
experimental section we provide extended evaluation regarding each step of the
proposed algorithm, using our publicly available, realistic Smoking Event
Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets
recorded under semi-controlled and free-living conditions, respectively. In
particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of
0.863 for the detection of puffs and an F1-score/Jaccard index equal to
0.878/0.604 towards the temporal localization of smoking sessions during the
day. Finally, to gain further insight, we also compare the puff detection part
of our algorithm with a similar approach found in the recent literature.
