End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch

In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations r...

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Bibliographic Details
Published inConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2018; pp. 5511 - 5514
Main Authors Kyritsis, Konstantinos, Diou, Christos, Delopoulos, Anastasios
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2018
Subjects
Accelerometers
Gyroscopes
Hidden Markov models
Mouth
Sensors
Support vector machines
Training
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Summary:In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2018.8513627