Authors
Edison Thomaz, Irfan Essa, Gregory D Abowd
Publication date
2015/9/7
Book
Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing
Pages
1029-1040
Description
Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for …
Scholar articles
E Thomaz, I Essa, GD Abowd - Proceedings of the 2015 ACM international joint …, 2015