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Detecting Eating Episodes From Daily Patterns Of Wrist Motion Using Recurrent Neural Networks, Adam Patyk

All Theses

This thesis considers the problem of detecting when a person is eating during everyday life by examining daily patterns of wrist motion with recurrent neural networks. Our novelty is analyzing an entire day of data to classify and segment meals with a model we call the “daily pattern classifier”. Previous research has only analyzed short windows on the order of seconds or minutes long that miss larger day-to-day patterns. The goal of this work is to utilize daily contextual indicators to improve eating episode detection and reduce false detections that occur throughout the day.

The wrist motion data used in …

Individualized Wrist Motion Models For Detecting Eating Episodes Using Deep Learning, Wenkang Wei

All Theses

This thesis considers the problem of detecting eating episodes such as meals and snacks, by tracking wrist motion using smartwatch device. Previous work by our group has trained a wrist motion classifier using a large data set collected from 351 people to learn general eating behaviors. We call this a group model. This thesis investigates training the classifier with the same model architecture on new data collected by 8 people, and training the individualized classifier separately for each person. We call these individual models. The main goal in this work is to determine if individual models provide higher accuracy in …