The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface

We are what we eat. Our everyday food choices affect our long-term
and short-term health. In the traditional health care, professionals assess and
weigh each individual’s dietary intake using intensive labor at high cost. In this
paper, we design and implement a diet-aware dining table that can track what
and how much we eat. To enable automated food tracking, the dining table is
augmented with two layers of weighing and RFID sensor surfaces. We devise a
weight-RFID matching algorithm to detect and distinguish how people eat. To
validate our diet-aware dining table, we have performed experiments, including
live dining scenarios (afternoon tea and Chinese-style dinner), multiple dining
participants, and concurrent activities chosen randomly. Our experimental re-sults
have shown encouraging recognition accuracy, around 80%. We believe
monitoring the dietary behaviors of individuals potentially contribute to diet-aware
healthcare.