Automatic Detection of Periods of Eating Using Wrist Motion Tracking

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