  While the task of automatically detecting eating events has been examined in
prior work using various wearable devices, the use of smartphones as standalone
devices to infer eating events remains an open issue. This paper proposes a
framework that infers eating vs. non-eating events from passive smartphone
sensing and evaluates it on a dataset of 58 college students. First, we show
that time of the day and features from modalities such as screen usage,
accelerometer, app usage, and location are indicative of eating and non-eating
events. Then, we show that eating events can be inferred with an AUROC (area
under the receiver operating characteristics curve) of 0.65 using
subject-independent machine learning models, which can be further improved up
to 0.81 for subject-dependent and 0.81 for hybrid models using personalization
techniques. Moreover, we show that users have different behavioral and
contextual routines around eating episodes requiring specific feature groups to
train fully personalized models. These findings are of potential value for
future mobile food diary apps that are context-aware by enabling scalable
sensing-based eating studies using only smartphones; detecting under-reported
eating events, thus increasing data quality in self report-based studies;
providing functionality to track food consumption and generate reminders for
on-time collection of food diaries; and supporting mobile interventions towards
healthy eating practices.
