Benefits for Diet-aware Dining Table

Moreover, it is feasible to calculate the nutritional value of a meal by this design. Through-out the entire process of cooking a meal, by recording the three situations which consist of information of the food ingredients, the nutritional value of a meal is obtainable by sum-ming up nutritional values of food ingredients added while cooking. As a result, there is a significant kitchen automation benefit: it strengthens the RFID assumption made in Section 3.1 that every food container on the diet-aware dining table is tagged with a RFID tag and there is a mapping database between a tag-id and nutritional value. The reason is that if a meal is prepared in the smart kitchen, the mapping database can be automatically build and there is no need for a cooking person to manually input the meal’s content as the meal is placed onto the dining table.

54 CHAPTER 7. APPLICATION II: SMART KITCHEN

Figure 7.3: System architecture of Smart Kitchen.

7.5 Summary

We describes the design of a smart kitchen to achieve healthy cooking. By generalizing the method used diet-aware dining table, we regard a smart counter and a smart cabi-net installed as two cells to collaboratively recognize cooking situations, such as adding food ingredients. A LCD and speaker system then give healthy-cooking advices to a cooking person. We believe those situations being aware of in this system well demon-strates the idea of smart kitchen to promote healthy cooking. Further extension the idea of situation-awareness by adding new situations to be aware could make a kitchen of the future realizable and approachable.

We are planning to prototype the smart kitchen and develop an effective user interface to promote healthy cooking. Since users are typically busy during their cooking process, the design of the interface should be simple and intuitive as not requiring high cognitive load on users. We are interested in exploring what is the appropriate amount of awareness information presented to users, and what are the best times of delivering such information.

We will invite experienced household cooks to participate in the design and evaluation of our kitchen environment.

Chapter 8

Conculsion and Future Work

We are what we eat. This paper describes the design and implementation of our diet-aware dining table. We have augmented an ordinary dining table with two layers of sensor surfaces underneath the table - the RFID surface and the weighing surface. Given certain assumptions, the diet-aware dining table automatically tracks what and how much each individual eats from the dining table over the course of a meal. We have performed several experiments, including live dining scenarios (afternoon tea and Chinese-style dinner), multiple dining participants, and random concurrent activity sequences. Our experimental results have shown reasonable recognition accuracy of around 80%, which is at least as good as the accuracy of the traditional dietary assessment methods.

Our future work will further improve the recognition accuracy, address some of the main causes of inaccuracy from our experimental results, and relax some of the assump-tions and restricassump-tions. Note that some of the restricassump-tions can be solved by making simple design changes. For examples, the current prototype does not allow hands or elbows on the table. To relax this restriction, we can add a slightly protruding frame around the edge of table, so that users can rest their elbows on the frame without affecting our system. We also believe in multi-sensor approach. For example, by deploying a video camera above

55

56 CHAPTER 8. CONCULSION AND FUTURE WORK

the table, it is possible to observe events that cannot be detected by RFID and weighing surfaces.

Furthermore, the design of diet-aware dining table is able to be further generalized as a smart surface that two more applications in the area of healthcare are investigated. Since this table can track tabletop person-food interactions in real time, it’s feasible build just-in-time persuasive feedbacks to encourage better healthy dining behaviors. As a result, we have explored the design of an interactive, persuasive game to assist adult parents to improve dietary behavior of their young children. The persuasive game is played over a smart lunch tray, extended from our diet-aware dining table. In addition, we have designed a smart kitchen which is installed with a smart counter and a smart cabinet, extended from our diet-aware dining table as well, to aware what and how much food a user is cooking.

After recognizing cooking behaviors, a LCD display and speaker system will guide the user to healthy cooking. The smart kitchen also calculates the nutritional value of a meal which is build into a RFIDtag-nutrition mapping database for diet-aware dining table.

This initiates the automation from a kitchen to a dining table.

The smart kitchen application is now in the design phase, and we will start to build it. In addition, for these two applications, further user study is required. Responses from the users could help inventing better interface to persuade healthy dining and cooking behaviors.

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Appendix A

Publication of Keng-hao Chang

Below is a list of publications that I have achieved in the study of master program:

1. Keng-hao Chang, Shih-yen Liu, Hao-hua Chu, Jane Yung-jen Hsu, Cheryl Chen, Tung-yun Lin, Polly Huang, ”Dietary-Aware Dining Table - Observing Dietary Behaviors over Tabletop Surface”, in Proceedings of the 4th International Con-ference on Pervasive Computing (Pervasive 2006), Dublin, Ireland, May 7, 2006 pages 366 - 382 (with acceptance rate 13%).

2. Shun-yuan Yeh, Chon-in Wu, Keng-hao Chang, Hao-hua Chu, Jane Yung-jen Hsu,

”The GETA Sandals: A Footprint Location Tracking System”, to appear in Springer/ACM Personal and Ubiquitous Computing (ACM PUC), 2005.

3. Keng-hao Chang, Shih-yen Liu, Jr-ben Tian, Hao-hua Chu, Cheryl Chen, ”Dietary-Aware Dining Table - Tracking What and How Much You Eat”, in Proceedings of Workshop on Smart Object Systems, in conjunction with the Seventh Interna-tional Conference on Ubiquitous Computing (ACM UbiComp 2005), Tokyo, Japan, September 11, 2005, pages 61-68

4. Kenji Okuda, Shun-yuan Yeh, Chon-in Wu, Keng-hao Chang, Hao-hua Chu, ”The 63

64 APPENDIX A. PUBLICATION OF KENG-HAO CHANG

GETA Sandals: A Footprint Location Tracking System”, Workshop on Location-and Context-Awareness (LoCa 2005), in Cooperation with Pervasive 2005 , (also published as Lecture Notes in Computer Science 3479, Location- and Context-Awareness), Munich, Germany, May 2005, pages 120-131.

5. Shun-yuan Yeh, Keng-hao Chang, Chon-in Wu, Okuda Kenji, Hao-hua Chu, ”GETA Sandals: Knowing Where You Walk To”, the demo paper (adjunct proceed-ings) of the Seventh Interna-tional Conference on Ubiquitous Computing (ACM UbiComp 2005), Tokyo, Japan, September 11, 2005.

6. Keng-hao Chang, Tsung-Han Lin, Hao-hua Chu, Polly Huang, ”Modeling and Simulation Com-parison of Two Time Synchronization Protocols”, submitted to ACM Transactions on Sensor Networks