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 [BibTeX] [Marc21]
Sensing Eating Events in Context: A Smartphone-Only Approach
Type of publication: Journal paper
Citation: Meegahapola_IEEEACCESS_2022
Publication status: Published
Journal: IEEE Access
Volume: 10
Year: 2022
Month: May
URL: https://ieeexplore.ieee.org/do...
DOI: 10.1109/ACCESS.2022.3179702
Abstract: 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.
Projects Idiap
Authors Meegahapola, Lakmal Buddika
Bangamuarachchi, Wageesha
Chamantha, Anju
Ruiz-Correa, Salvador
Perera, Indika
Gatica-Perez, Daniel
Added by: [UNK]
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  • Meegahapola_IEEEACCESS_2022.pdf