1 i-care project dietary-aware dining table: observing dietary behavior over tabletop surface...
Post on 19-Dec-2015
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i-Care ProjectDietary-Aware Dining
Table: Observing Dietary Behavior
over Tabletop Surface
Keng-hao Chang, Shih-yen Liu, Toung Lin, Hao Chu,
Jane Hsu, Polly Huang, (Cheryl Chen)i-space Laboratory
National Taiwan University
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What is it?
• A dietary-tracker built into an everyday dining table– Track what & how much you eat over tabletop
surface
• Motivation– We are what we eat– Food choices affect long-term & short-term
health
• Show a demo video
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Smart Everyday Object
• Digital-enhanced everyday objects– Provide digital services
• Support natural human interactions– Natural human interactions = inputs to digital
services
• Goals – Providing digital services without (users)
operating digital devices → better usability– Human-centric computing: technology adapting
to users rather than users adapting & learning about technology
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Outline for Reminder of Talk
• Related work• Approach• Assumptions & Limitations• Design & Implementation• Experimental Evaluation• Future work
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Related Work
• Dietary trackers– Shopping receipt scanner (GaTec
h)– Chewing Sound (ETH)– My food phone (startup)
• Intelligent surfaces– Load sensing table (Lancester)– Smart floor (GaTech, NTU)– Posture Chair (MIT)
• What’s new here?– Accuracy– Fine-grained tracking– Simultaneous concurrent interac
tions
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Contribution claims
• It is a fine-granularity (automated) dietary tracker.– It can track multiple concurrent
interactions from multiple individuals over the same tabletop surface.•People usually don’t eat alone
• It is an enhanced loading sensing table.
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General Approach
• RFID tags on food containers• Two sensor surfaces on table
– Each surface is made of cells– RFID reader surface
• Detect RFID(s) in each cell– Weighting surface (load cells)
• Measure weight change in each cell• Track the food path from container(s) → conta
iner(s) → mouth using these two sensor surfaces
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Assumptions (Limitations)
• Closed system rather than open system.– Food transfers among tabletop objects and
mouths, no external objects and food sources
• Users identified by personal containers (personal plates and cups)
• Food containers tagged with RFID tags• No cross-cell objects• No leaning their hands on the table• Not a mobile tracker
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Single Interaction Example
• Bob pours tea from the tea pot to his personal cup, and drinks it
• Detect tea transfer from one container to another container1) Identify the presence & absence of containers
• RFID tags on containers• tag-food mapping
2) Track tea transfer • Weight change detection• Weight matching algorithm
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Single Interaction Example
Pour tea?•Weight increases ∆w2.
• Bob pours tea from the tea pot to personal cup, and drinks it
Pick up tea pot.• RFID tag disappears• Weight decreases ∆w1
Put on tea pot.•RFID tag appears•Weight increases ∆w3 Pour tea!• |∆w3 - ∆w1 | ≈ ∆w2
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Single Interaction Example
• Bob pours tea from the tea pot to personal cup, and drinks it
Pick up cup.• RFID tag disappears.• Weight decreases ∆w1.
Put on cup.•RFID tag appears.•Weight increases ∆w2.Drink tea? (only if no match)• Amount | ∆w2 - ∆w1 |
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Concurrent Interactions Example
• Bob pours tea & Alice cuts cake
Pour tea?Cut cake? • Weight change ∆w
Pour tea• Weight increases ∆ w1
Cut cake• Weight decreases ∆w2
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Concurrent Interactions Example
• Multiple, concurrent person-object interactions– The larger the cell, the higher the
possibility of concurrent interactions over a cell
– Cell size = average size of container– Reduce the possibility of concurrent
interactions over one cell
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Design Architecture
Tag-object
mappings
Behavior Inference Engine
Event Interpreter
Weight Change Detector
Object Presence Detector
Weighing surface (weighing sensors)
RFID Surface (readers)
Applications (Dietary-aware Dining Table)
Common sense
semantics
Sensor Events
Intermediate Events
Dietary Behaviors
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Inference Rule
Dietary behaviors
Behavior Inference Rules
Transfer(u, w, type)
Weight-Change(Object-i1, Δw1) ∩ (Δw1< 0) ∩ Weight- Change (Object-i2, Δw2) ∩ (Δw2 > 0) ∩ Contains(Object-i1, type) ∩ Owner(Object-i2, u) ∩(|Δw1 +Δ w2 |< ε) → Transfer (u, Δw2, type)
Eat(u, w, type) Weight-Change(Object-i, Δw) ∩ (Δw<0) ∩ Contains(Object-i, type) ∩ Owner(Object-i, u)→ Eat(u, -Δw, type)
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Experimental setup
• 2 Dining settings– Afternoon tea– Chinese-style
dinner
• 2 Parameters– # of participants– Predefined vs.
Random Sequence
A
Willy
Keng-hao
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Experimental ResultsScenarios Event Statistics Results
Dining Scenario
s
# Users
Activity Sequenc
e
Time Durati
on(secon
ds)
# of Dietar
y Behavi
or
Average Activity Interval
Behavior Recogniti
on Accuracy
#1 Afternoo
n tea
1 Predefined
73 12 6.08 100%
#2 Afternoo
n tea
2 Predefined
162 24 6.75 100%
#3 Afternoo
n tea
2 Random 913 78 11.71 79.49%
#4 Chinese-
style dinner
3 Random 1811 162 11.18 85.8%
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Predefined Activity Sequence
Afternoon Tea (Single User)1. cut a piece of cake and
transfer it to the personal plate;
2. pour tea from the tea pot to the personal cup;
3. add milk to the personal cup from the creamer;
4. eat the piece of cake from the personal plate;
5. drink tea from the personal cup;
6. add sugar to the personal cup from the sugar jar.
Afternoon Tea (Multi-users)1. A cuts cake and transfers it to
A’s personal plate;2. B pours tea from the tea pot to
B’s personal cup; 3. A pours tea to A’s personal cup
while B cuts a piece of cake and transfers it to B’s personal plate;
4. A adds sugar from the sugar jar to A’s personal cup while B adds milk from the creamer to B’s personal up;
5. A eats cake and B drinks tea; 6. B eats cake from B’s personal
plate while A drinks tea from A’s personal cup;
7. A pours tea from the tea pot to both A’s and B’s personal cups.
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Activity Recognition Accuracy in Scenario
#3
Dietary Behavior
# of Actual Events
Recognition Accuracy
Transfer event
41 70.73%
Eat event 37 89.19%
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Causes of Misses in Scenario #3
Causes of misses # of misses of transfer
events
# of misses of eat events
Total
Event interference within the weighing
cell’s weight stabilization time
6 2 8
Weight matching threshold
2 0 2
Slow RFID sample rate
3 0 3
Touching table 1 2 3
Total of misses 12 4 16
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Dietary Behavior
# of time
s
Recognition Accuracy
Weight Accuracy
Transfer dish A events
19 73.68% 68.42%
Transfer dish B events
29 79.31% 78.75%
Transfer dish C events
23 82.61% 79.19%
Transfer rice events
12 83.33% 81.88%
Transfer soup events
19 84.21% 80.16%
Eat events 60 88.33% 91.23%
Activity Recognition Accuracy in Scenario
#4
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Causes of Misses in Scenario #4
Causes of misses # of misses of transfer events
# of misses of
eat events
Total
Segmented weight-change events
5 0 5
Eating before transferring food to personal containers
5 5 10
Weight matching ambiguity
7 0 7
Touching table 3 2 5
Slow RFID sample rate 3 0 3
Total of misses 23 7 30
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Conclusion
• It is a smart object and a smart surface• It supports natural user interface• It supports fine-grained dietary tracking
at individual level• It is about human-centric computing• Accuracy can be improved further
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Future Work
• Improving recognition accuracy• Removing constraints (assumptions)• Persuasive computing
– Encourage balanced diet– Encourage proper amount of diet