The diet-aware dining table: observing dietary behaviors over a tabletop surface

  • Authors:

  • Keng-hao Chang;Shih-yen Liu;Hao-hua Chu;Jane Yung-jen Hsu;Cheryl Chen;Tung-yun Lin;Chieh-yu Chen;Polly Huang

  • Affiliations:

  • Department of Computer Science and Information Engineering;Department of Information Management;Department of Computer Science and Information Engineering;Department of Computer Science and Information Engineering;School and Graduate Institute of Nursing;Graduate Institute of Networking and Multimedia;Department of Computer Science and Information Engineering;Department of Electrical Engineering, National Taiwan University

  • Venue:
  • PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
  • Year:
  • 2006

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Abstract

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 results have shown encouraging recognition accuracy, around 80%. We believe monitoring the dietary behaviors of individuals potentially contribute to diet-aware healthcare.