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The progress in artificial intelligence and machine learning algorithms overthe past decade has enabled the development of new methods for the objectivemeasurement of eating, including both the measurement of eating episodes aswell as the measurement of in-meal eating behavior. These allow the study ofeating behavior outside the laboratory in free-living conditions, without theneed for video recordings and laborious manual annotations. In this paper, wepresent a high-level overview of our recent work on intake monitoring using asmartwatch, as well as methods using an in-ear microphone. We also presentevaluation results of these methods in challenging, real-world datasets.Furthermore, we discuss use-cases of such intake monitoring tools for advancingresearch in eating behavior, for improving dietary monitoring, as well as fordeveloping evidence-based health policies. Our goal is to inform researchersand users of intake monitoring methods regarding (i) the development of newmethods based on commercially available devices, (ii) what to expect in termsof effectiveness, and (iii) how these methods can be used in research as wellas in practical applications.

Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned