  Obesity is a risk factor for many health issues, including heart disease,
diabetes, osteoarthritis, and certain cancers. One of the primary behavioral
causes, dietary intake, has proven particularly challenging to measure and
track. Current behavioral science suggests that family eating dynamics (FED)
have high potential to impact child and parent dietary intake, and ultimately
the risk of obesity. Monitoring FED requires information about when and where
eating events are occurring, the presence or absence of family members during
eating events, and some person-level states such as stress, mood, and hunger.
To date, there exists no system for real-time monitoring of FED. This paper
presents MFED, the first of its kind of system for monitoring FED in the wild
in real-time. Smart wearables and Bluetooth beacons are used to monitor and
detect eating activities and the location of the users at home. A smartphone is
used for the Ecological Momentary Assessment (EMA) of a number of behaviors,
states, and situations. While the system itself is novel, we also present a
novel and efficient algorithm for detecting eating events from wrist-worn
accelerometer data. The algorithm improves eating gesture detection F1-score by
19% with less than 20% computation compared to the state-of-the-art methods. To
date, the MFED system has been deployed in 20 homes with a total of 74
participants, and responses from 4750 EMA surveys have been collected. This
paper describes the system components, reports on the eating detection results
from the deployments, proposes two techniques for improving ground truth
collection after the system is deployed, and provides an overview of the FED
data, generated from the multi-component system, that can be used to model and
more comprehensively understand insights into the monitoring of family eating
dynamics.
