A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing

By Edison Thomaz, Irfan Essa, Gregory D. Abowd. Ubiquitous Computing, 2015

316 citations recorded by the source corpus.

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 everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.

Subfields: Computer Vision and Pattern Recognition, Physiology, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/336535000d33581ef3df0578914e4f33dfded826

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Gert R. G. Lanckriet
2. Katherine Ellis
3. Yonatan Vaizman

Citations in the corpus

• Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “Previous research efforts have used mobile sensors to recognize small sets of specific targeted activities, such as body movement, modes of transportation, gymnasium activities or eating [3], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], and se”