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

@incollection{chang-perv-2006, 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.}, author = {Chang, Keng-Hao and Liu, Shih-Yen and Chu, Hao-Hua and Hsu, Jane and Chen, Cheryl and Lin, Tung-Yun and Chen, Chieh-Yu and Huang, Polly }, citeulike-article-id = {3013396}, journal = {Pervasive Computing}, pages = {366--382}, posted-at = {2008-07-17 05:36:08}, priority = {2}, title = {The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface}, url = {http://dx.doi.org/10.1007/11748625\_23}, year = {2006} }

TY - CHAP ID - chang-perv-2006 L3 - citeulike-article-id:3013396 TI - The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface JF - Pervasive Computing SP - 366 EP - 382 N2 - 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. AU - Chang, Keng-Hao AU - Liu, Shih-Yen AU - Chu, Hao-Hua AU - Hsu, Jane AU - Chen, Cheryl AU - Lin, Tung-Yun AU - Chen, Chieh-Yu AU - Huang, Polly PY - 2006/// UR - http://dx.doi.org/10.1007/11748625_23 ER -