Challenges and Opportunities in Automated Detection of Eating Activity

Edison Thomaz, Irfan A. Essa, Gregory D. Abowd

Abstract: Motivated by applications in nutritional epidemiology and food journaling, computing researchers have proposed numerous techniques for automating dietary monitoring over the years. Although progress has been made, a truly practical system that can automatically recognize what people eat in real-world settings remains elusive. Eating detection is a foundational element of automated dietary monitoring (ADM) since automatically recognizing when a person is eating is required before identifying what and how much is being consumed. Additionally, eating detection can serve as the basis for new types of dietary self-monitoring practices such as semi-automated food journaling.This chapter discusses the problem of automated eating detection and presents a variety of practical techniques for detecting eating activities in real-world settings. These techniques center on three sensing modalities: first-person images taken with wearable cameras, ambient sounds, and on-body inertial sensors [34–37]. The chapter begins with an analysis of how first-person images reflecting everyday experiences can be used to identify eating moments using two approaches: human computation and convolutional neural networks. Next, we present an analysis showing how certain sounds associated with eating can be recognized and used to infer eating activities. Finally, we introduce a method for detecting eating moments with on-body inertial sensors placed on the wrist.