MFED: A System for Monitoring Family Eating Dynamics

TL;DR

MFED is a novel real-time system that uses wearable sensors, Bluetooth beacons, and smartphones to monitor family eating behaviors at home, providing valuable data for understanding and addressing obesity-related risks.

Contribution

This paper introduces MFED, the first real-time system for monitoring family eating dynamics in natural settings, with a new algorithm for detecting eating gestures from wrist accelerometer data.

Findings

System deployed in 20 homes with 74 participants

Eating gesture detection improved by 19% F1-score

Collected 4750 EMA survey responses

Abstract

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.