Automatic Detection of Periods of Eating Using Wrist Motion Tracking

Abstract

More than one third of adults in the United States are now classified as obese. Self monitoring of energy in take has been shown to have a positive impact on weight loss, but existing tools for logging eating activities are tedious to use and prone to bias which leads to noncompliance and underestimation. In this paper we describe preliminary results from our on going data collection from 500 free living participants, in an effort to improve our previous algorithm that detects periods of eating. We see a 75% accuracy in our data collected to date.