Diet-Aware Dining Table –Observing Dietary Behaviors over Tabletop

Surface

Shih-yen Liu, Cheryl Chen, Tung-yun Lin, Polly HuangNational Taiwan University

Keng-hao Chang Hao-(hua) Chu Jane Yung-jen Hsu

Diet-aware Dining Table, i-space @ NTU

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…

Diet-aware Dining Table, i-space @ NTU

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

Diet-aware Dining Table, i-space @ NTU

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?

Diet-aware Dining Table, i-space @ NTU

Target interactions

• Consume food from the “personal” containers

• Where the food comes from?

• Transferred from the share containers to personal containers

Diet-aware Dining Table, i-space @ NTU

Demonstration

Diet-aware Dining Table, i-space @ NTU

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

RFID Antenna

Load Sensor

Diet-aware Dining Table, i-space @ NTU

Weight consistency principle

• Transfer tea

• Drink tea

Weight Decrease of

Weight Increase of Weight Decrease of

Weight Increase of

Diet-aware Dining Table, i-space @ NTU

1. Transfer Tea

• Bob pours tea from the tea pot to personal cup

Pour tea?•Weight increases w2.

Pick up tea pot.• RFID tag disappears• Weight decreases w1

Put on tea pot.• RFID tag appears• Weight increases w1-w2

w1

w2

w1- w2

w2

Pour tea by match!

Diet-aware Dining Table, i-space @ NTU

w1-w2

2. Drink Tea

• Bob drinks tea

Pick up cup.• RFID tag disappears.• Weight decreases w1.

Put on cup.Drink tea • RFID tag appears.• Weight increases w2.

w1

w2

Drink tea by identify “Bob”

Diet-aware Dining Table, i-space @ NTU

3. Complex Example

• Bob pours tea & Alan cuts cake

Pour tea?Cut cake? • Weight change w

Pour tea• Weight increases w1

Cut cake• Weight decreases w2

Diet-aware Dining Table, i-space @ NTU

Method summary

• Transfer interactions– Match weight

• Eat interactions– Identify personal container

• Concurrent interactions– Divide cells

Diet-aware Dining Table, i-space @ NTU

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

Diet-aware Dining Table, i-space @ NTU

Experiment Discussion

• Causes of misses

Touching table

Eat without Transfer

Weight Ambiguity

10 g10 g

Diet-aware Dining Table, i-space @ NTU

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.

Diet-aware Dining Table, i-space @ NTU

Future work

• To improve recognition accuracy• To relax constraints• Just-in-time persuasive technology

– To encourage balanced diet

Questions & Answers

Thank you!

Keng-hao Chang khchang@csie.orgNational Taiwan University