Dietary-Aware Dining Table: Observing Dietary Behaviors over Tabletop Surface

We are what we eat. Our everyday food choices affect our long-term and short-term health. In the traditional health care, the professionals assess and weigh each individual’s dietary intake with intensive labor cost. In this paper, we have designed and implemented a dietary-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 weighting and RFID sensor surfaces. We have devised weight-RFID matching algorithm to detect and recognize multiple, concurrent person-object interactions occurring on the table. To validate our dining table, we have performed real-world experiments, including live dining scenarios (afternoon tea and Chinese-style dinner), multiple dining participants, and random concurrent activity sequences. Our experimental results have shown encouraging recognition accuracy around 80%. We believe that by monitoring the dietary behaviors of individuals would potentially contribute towards the area of dietary-aware healthcare.