Diet-Aware Dining Table – Observing Dietary Behaviors over Tabletop Surface

Keng-hao Chang Hao-(hua) Chu Jane Yung-jen Hsu Diet-Aware Dining Table –Observing Dietary Behaviors over Tabletop Surface Shih-yen Liu, Cheryl Chen, Tung-yun Lin, Polly Huang National Taiwan University

A story - motivation • Video [Script]: • A man wants to control weight • Doctor asks him to report his dietary habits • Questionnaire is cumbersome, awkward • Then he uses our table, everything is so easy…

Pervasive Healthcare - We are what we eat • It’s hard • Shopping receipt scanner, Mankoff et al., Ubicomp 2002 • Analyze the purchased food items of a whole family • It cannot track individual intake • Analysis of Chewing Sounds for Dietary Monitoring, Amft et al., Ubicomp 2005 • Infer food intake by chewing sound • Ambiguity

But, we try differently • Smart object approach • Instrument everyday dining tables • Not blind to what happened above the surface • Features: • Natural interaction • Multi-users but in individual level • Observed Interactions?

Target interactions • Consume food from the “personal” containers • Where the food comes from? • Transferred from the share containers to personal containers

Demonstration

Load Sensor RFID Antenna The table design – what’s the magic? • Two sensor surfaces • RFID & Weight • RFID – what • RFID-tagged containers • Weight - how much • Weight “change” of dietary behaviors • Cell division • Concurrent person-container interactions

Weight Decrease of Weight Decrease of Weight Increase of Weight Increase of Weight consistency principle • Transfer tea • Drink tea

w2 w2 w1 w1- w2 1. Transfer Tea • Bob pours tea from the tea pot to personal cup • Put on tea pot. • RFID tag appears • Weight increases w1-w2 • Pick up tea pot. • RFID tag disappears • Weight decreases w1 • Pour tea? • Weight increases w2. Pour tea by match!

w1-w2 w1 w2 2. Drink Tea • Bob drinks tea Drink tea by identify “Bob” • Put on cup. • Drink tea • RFID tag appears. • Weight increases w2. • Pick up cup. • RFID tag disappears. • Weight decreases w1.

3. Complex Example • Bob pours tea & Alan cuts cake • Cut cake • Weight decreases w2 • Pour tea? • Cut cake? • Weight change w • Pour tea • Weight increases w1

Method summary • Transfer interactions • Match weight • Eat interactions • Identify personal container • Concurrent interactions • Divide cells

Experiments • Chinese-style dinner scenario with three users • No hands, utensils on the table • 30 min, 100 transfer events, 60 eat events • Behavior Recognition Accuracy: 83.33% • Transfer: 81.99% • Eat: 88.33% • Weight Accuracy: 82.62 % A B C

Touching table Weight Ambiguity 10 g Eat without Transfer 10 g Experiment Discussion • Causes of misses

Conclusion • Diet-aware dining table • A smart object and a smart surface • Support natural user interaction • fine-grained dietary tracking at individual level • A nice first step in such direction. • 80% accuracy. • The whole problem can be explored more deeply.

Future work • To improve recognition accuracy • To relax constraints • Just-in-time persuasive technology • To encourage balanced diet

Questions & AnswersThank you! Keng-hao Chang khchang@csie.org National Taiwan University

Diet-Aware Dining Table – Observing Dietary Behaviors over Tabletop Surface