JMIR Publications

ABSTRACT

Background:

Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge due to the lack of a mature system and large-scale, reliable training set.

Objective:

Here, we present an integrative engineering and machine learning effort and report a large-scale study in terms of monitoring hours on wearable-based eating detection.

Methods:

This prospective, longitudinal, passively collected study covering 3828 hours of records was made possible by programming a digital system that streams diary, accelerometer and gyroscope data from Apple watches to iPhones, then transferring the data to the cloud.

Results:

Based on this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an AUC of 0.825 within five minutes in the general population. Additionally, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal level aggregation).

Conclusions:

The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care.