Food and fluid intake monitoring are essential for reducing the risk of dehydration,
malnutrition, and obesity. The existing research has been preponderantly focused on dietary …

Background The location in which physical activity and sedentary behavior are performed
can provide valuable behavioral information, both in isolation and synergistically with other …

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 …

Many people appropriate social media and online communities in their pursuit of personal
health goals, such as healthy eating or increased physical activity. However, people struggle …

Background: A dramatic rise in health-tracking apps for mobile phones has occurred
recently. Rich user interfaces make manual logging of users' behaviors easier and more …

Nutrient-based meal recommendations have the potential to help individuals prevent or
manage conditions such as diabetes and obesity. However, learning people's food …

Food choices are among the most frequent and important health decisions in everyday life,
but remain notoriously difficult to capture. This work examines opportunities for lightweight …

We present the design, implementation, and evaluation of a multi-sensor, low-power
necklace, NeckSense, for automatically and unobtrusively capturing fine-grained information …

In this paper, we propose Auracle, a wearable earpiece that can automatically recognize
eating behavior. More specifically, in free-living conditions, we can recognize when and for …

ObjectiveTo develop an artificial intelligence (AI)-based algorithm which can automatically
detect food items from images acquired by an egocentric wearable camera for dietary …