The Impact of Walking and Resting on Wrist Motion for Automated Detection of Meals

Abstract

This article considers detecting eating in free-living humans by tracking wrist motion. We are specifically interested in the effect of secondary activities that people conduct while simultaneously eating, such as walking, watching television, or working. These secondary activities cause wrist motions that obfuscate those associated with eating, increasing the difficulty of detecting periods of eating. We collected a large dataset of 4,680 hours of wrist motion from 351 participants during free living. Participants reported secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals compared to only 6.8% of the time in a cafeteria dataset, whereas walking motion was found 5.5% of the time during meals in free living compared to 0% in a cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved accuracy from 74% to 77% on our free-living dataset ( t [353] = 7.86, p < 0.001). Although eating detection could be improved using more sophisticated machine learning methods or sensor modalities, all approaches would be affected by secondary activities, as they affect the labeling of data itself. Our work suggests that future work should collect detailed ground truth on secondary activities being conducted during eating, as these activities could hold insights into when an eating activity starts or stops in the absence of video-based ground truth.