Presenter: Dirk Christian Elias, President of Executive Board of Fraunhofer Portugal, PT Event: Games for Health Europe 2015 Conference Date: 03 NOV 2015 / 13:30 - 14:00 Keynote Location: Juliana Congreszaal, Jaarbeurs Utrecht

1. Fraunhofer Portugal @ Games for Health Europe Conference | 02 & 03 November 2015, Utrecht, The Netherlands
Fraunhofer Portugal – Research Center for Assistive Information and Communication Solutions Applied
Science by Fraunhofer – Made in Portugal
The IoT, Big Data and I – Food for Game?
REMARKABLE TECHNOLOGY, EASY TO USE

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1. Fraunhofer Portugal
Fraunhofer-Gesellschaft (worldwide)
66 Institutes
> 80 Research Units
~ 24.000 employees
> €2 billion R&D budget
(€1,7 billion from contract research)
7 Groups
 Information and Communication Technology
 Life Sciences
 Light & Surfaces
 Microelectronics
 Production
 Materials & Components
 Defense & Security

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1. Fraunhofer Portugal
Institutional Background
Fraunhofer Portugal is a Non-Profit Research Institution of Public Common Interest

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1. Fraunhofer Portugal AICOS
Strategic Research Agenda – Focus Areas
Key Target Groups:
Subfields:
Fall and activity monitoring
Chronic diseases &
Well-Being Management
Assistive environments
Ageing & Elderly
(including relatives, caregivers
and communities)
ICT4D
(ICT for Development)
AAL
(Ambient Assisted Living)
Populations in
Rural & Developing
Areas
mGovernment
mHealth
mAgriculture
ICT for Very Small Enterprises

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1. Fraunhofer Portugal AICOS
Strategic Research Agenda – Scientific Areas
Autonomic ComputingHuman-Computer Interaction Information
Processing
- Content Retrieval
- Context Awareness
- Multimodal Information
Fusion
- Remote Management,
Control and Configuration
- User & Social Experience
- Mobile & Future Devices
- Evaluation & Usability
Adapting interaction
to specific user
needs
From raw data …
To meaningful
information
Smarter machines:
less configuration &
maintenance

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1. Fraunhofer Portugal AICOS
Hot Topics
 Eyes of Internet of Things Competence Centre – EIT-CC
 Focuses on the development of an infrastructure to physically observe
multiple data sources, act accordingly in the environment and provide a ‘holistic’
understanding of the user.
 Companion Competence Centre – C3
 Companions are specialized frontends of ‘big machines’ (IoT/Big Data
solutions) that serve human users.
 Focuses on the creation of real working prototypes built on top of processed
data provided by the technology developed in the EIT-CC.

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1. Fraunhofer Portugal AICOS
Hot Topics
Relation between
EIT-CC and C3
Goal:
Efficiency Gains

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2. Companions
 Companions are like the long time friends that:
 Help you when you need them
 Understand you and your demands and needs
 Interact with you (like a team member in a game)
 And get things done efficiently and conveniently
 Companions are front ends of large ‘machines’ (systems that deploy the
power of the IoT and Big Data)
More than just apps

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3. Gamifying Companions
COLABORAR: Playful R&D with 400 seniors
PLAN RESEARCH
DESIGN
ADAPT
MEASURE
 COLABORAR is a user network
bringing together:
 Over 400 older adult users
 Day-care, residences, hospitals,
universities, and other institutions
 Professionals (medical doctors,
occupational therapists, physical
therapists, psychologists…)
 …for human-centred design
processes in R&D

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4. Smart Companion
A Swiss army knife
PLAN RESEARCH
DESIGN
ADAPT
MEASURE
 We have followed this process
in the human-centred design of
Smart Companion
 Smart Companion is a Swiss
army knife, a convivial tool, that
assists seniors in getting things
done

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 Emergency call
 Special unlocker
 Agenda
 Calls
 Messages
 Navigation
 Location monitoring
 Points of interest
4. Smart Companion
A Swiss army knife
 It puts together an ever growing
set of relevant tools, e.g.:
 Settings: easy
personalisation
 Contacts
 Activity level
 In case of emergency
 Health measurements
 Camera
 ‘How are you?’
 Personal finances
 Activity monitoring
 Fall detection
 Fall risk prediction
 Medication reminder
 Nutrition
 Serious games
 …

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4. Smart Companion
From R&D to the market
 The approach has enabled
Smart Companion to
become a product that truly
meets seniors’ hopes and
needs

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4. Smart Companion
Feedback loop
 The transition from R&D to
market enabled:
 The creation of business
models
 Large scale testing
 An extended feedback loop
 Therefore, new inputs from
the client helped to align
new developments in R&D
already in an early phase

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5. Currently Cooking…
Active ageing
We work to support
active ageing through
technology, but what
exactly constitutes
active ageing?

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5. Currently Cooking…
Nutrition
Active ageing is based upon
physical activity and food
And nutrition, even if only
seen through the
perspective of the older
adult as final consumer, is a
very complex thing!
Our user research has
provided some insights…
Image source: http://www.foodinsight.org/

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5. Currently Cooking…
Nutrition: Motivation
’’
 1 in every 6 seniors reaching the emergency room are malnourished1
 Low income, less educated and elderly people suffer more often from malnutrition
 Malnutrition often leads to chronic diseases
 Chronic diseases cause significant costs to health care systems
 Good nutrition is an important factor in preventive health
Reference: Pereira, G. F., Bulik, C. M., Weaver, M. A., Holland, W. C., & Platts-Mills, T. F. (2015, January). Malnutrition among cognitively intact, noncritically
ill older adults in the emergency department. Annals of Emergency Medicine, 65(1), 85-91.

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5. Currently Cooking…
Nutrition: User research
Dealing with
errands
Cooking in
advance
Saving
strategies
Finding
something
to eat
No planning
Cooking
what you
know
Managing
one’s time
Lacking
motivation
to cook
Attitudes
towards
kitchen appl.
Don’t want
to eat alone
Following a
healthy diet
…every day. (…) I
cannot walk
heavy loaded…
People don’t think.
…I never plan
anything…
…I can save gas
and save work…
…because I feel
unwell…
I go out for lunch
because I don’t
want to be alone
I usually have
something ready
in advance
… now there’s
only the two
of us…
… basically you
do the usual…
SHOPPING ROUTINES
MEAL PLANNING
MEAL PREPARATION
EATING HABITS
“
’’

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5. Currently Cooking…
Nutrition: Co-design
Seniors were involved in
playful R&D activities to tackle
malnutrition in old age

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5. Currently Cooking…
Nutrition: Our work so far

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EFFICIENCY GAINS!
MORE
FROM LESS
FOR MORE!
6. The Future
From Demand based Production and Over Supply to
Supply based Consumption and Efficient Use
To keep and increase our standard of living also in the Future
The Top Game Changer
Reference: ‘more from less for more’, R.A. Mashelkar.

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Game Changer Levels
Efficiency
Gains
?
?
?
Society
6. The Future

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Technology Level
Efficiency
Gains
?
?
IoT with Big Data & Cloud Technology
6. The Future

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 The IoT & Big Data connect
currently isolated industrial islands.
 This leads to disruptive and rapid
changes in ‘old economies' business
models.
 From branch internal
competition to cross-sector
collaborations.
 Slow companies might not survive
because they didn’t recognize the
train that was hitting them.
Business Level
6. The Future

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 It is a new IoT & Big data enabled business model
 It is attractive for all stakeholders due to cost reduction / margin increase effects
 It is even more attractive for consumers (Health nutrition is the best prevention!)
 It is addressing a demographic problem (seniors often lack money and have
increased deficits related to cooking and meal planning)
 It is addressing a societal problem (food waste)
 It is ecological (food waste, regional, seasonal , CO2…)
 IT IS MORE FROM LESS FOR MORE!
6. 1 Example: Flat Rate Food
What is this supposed to be?

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 Retail of food products: High Turnover, Low Margin, Strong Competition
 Food Waste Worldwide: 33% of all food produced (source: Food Wastage Footprint, FAO, 2013)
 Production & Harvest: 32%
 Handling & Storage: 23%
 Processing: 11%
 Distribution: 13%
 Consumption: 21%
 Remember the malnutrition facts!
Background
6. 1 Example: Flat Rate Food

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Matching Consumers & Producers
How to Match?
 Freedom & Life Style
 Financial capability
 Activity & age
 Dietary / health requirements
 Compliance & convenience
 Cooking capabilities
VS.
 Supply based consumption
 Pretty complicated…
6. 1 Example: Flat Rate Food

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Challenge
F L AT
R AT E
F O O D
Individual
dietary
requirements Compliance/
convenience &
individual
taste
Financial
capability
Activity and
health
monitoring
Simplify
logistics
(transport CO2
foot print)Decrease food
waste (societal
goal)
Increase
margins in
retail
Seasonal &
regional
availability of
products
Increase
margins
in food
production
Environmental
protection
(protect production
resources: water,
CO2, pesticides,
etc.)
EVEN MORE
COMPLICATED!
MISSION
IMPOSSIBLE?
OR
A VERY SERIOUS
GAME!
6. 1 Example: Flat Rate Food

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P R O D U C E R D I S T R I B U T O R R E T A I L E R C O N S U M E R
4 Perspectives of the same
Consumer contracts a service! (e.g. 1 Month healthy food @ unbeatable
price!)
 No fixed quantities and products! (supply based!)
 Individual and triple feedback loop (supply, health, gusto) based Menu Planning
 Supply ‘spins’ planning and consumption
 Planned and supply based consumption reduces food waste and allows for optimized
supply chain
 A win for everybody (remember FAO numbers!)
 Step-by-Step introduction possible
6. 1 Example: Flat Rate Food

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4 Perspectives of the same – @ Home
INDIVIDUAL PLANNER &
RECOMMENDER &
CHEF@HOME
• Activity
• Simple health
parameters
• Priority products
• Promotions
• Prices
• Package size
• Nutritional info
• Personal info
• Health status
(external)
• Medicaments
• Personal preferences
• Food processors
• Fridge
• Etc.
• Product lists for longer
periods
Like / Dislike
IoT
RETAILER
Big
Data
QUANTIFIED SELF
MENU PLAN
SMART WHITE GOODS
DISHES
REGIONAL /
SEASONAL
INDIVIDUAL DEMANDS & NEEDS
“CLOUD CHEF” e.g.
IBM COOKING WITH
WATSON
Big
Data
IoT
RETAILER
Big
Data
6. 1 Example: Flat Rate Food

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4 Perspectives of the same – @ Retail
• PRICES
• PROMOTIONS
• LOGISTICS
DECISIONS
RETAIL MANAGEMENT ENGINE
• Quantities
• Validities
• Quality
STOCK / SHOP
• In shop stock
• Planograms
• Individual deliveries
center
STOCK / SHOP
• Individual deliveries
• Pick-ups
• Extras
• Guaranteed freshness
FLAT RATE FOOD CLIENTS
PRODUCTS
• Individual
Clients
• Products lists
(quantities, timing)
• Extras
FLAT RATE FOOD CLIENTDISTRIBUTOR
DISTRIBUTOR
• Demands / Prices
• Brands vs. White Labels
• Shop planning
• In-shop planning
• Individual vs. Bulk
• Client knowledge
LONG TERM
DEMANDS
• Orders
• Predictions
• Price offers
RETAIL INTELLIGENCE
SHORT TERMS
DEMANDS
IoT
IoT IoT
IoT
Big
Data
PRODUCTS
• Priority products
• Availabilities / Validities
• Predictions /
Promotions
• Brand placements
• Prices
6. 1 Example: Flat Rate Food

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4 Perspectives of the same – @
Distribution/Transport
• LOGISTICS
• NO LOSS STOCK
• PREDICTIONS &
LONG TERM
DEMANDS
DISTRIBUTOR
MANAGEMENT ENGINE
• Orders
• Predictions
• Long term demands
NATIONAL PRODUCERS
• Quantities
• Availabilities
• Qualities
• Predictions
• Lead times
INTERNATIONAL PRODUCERS
• Orders
• Long term demands
(capacity reservations)
FOOD PROCESSORS &
BRANDS
• Products
• Prices
• Validities
• Availabilities
• Promotions
• Placement offers
RETAILER
• Logistics plans
JUST IN TIME TRANSPORT
• Quantities
• Availability
• Qualities
• Predictions
• Priority products
NATIONAL PRODUCER
• Orders
• Predictions
• Offers
• White label demands
RETAILER
• Minimum stock
• Transport capacities
JUST IN TIME STOCK
• Brand placements
• Validities / quantities (products)
• Capacities (processing of e.g. white
label)
• Demands (for processing of brand
products)
BRAND / PACKAGED FOOD / PROCESSED
FOOD
IoT
Big
Data
IoT
6. 1 Example: Flat Rate Food

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4 Perspectives of the same – @ Producer
PRECISION AGRICULTURE
FARM ENGINE
• Weather
• Market developments (prices,
international demand)
• Energy / utility prices &
availabilities
• Pesticide & fertilizer prices
EXTERNAL FACTORS
• orders
• Predictions
• Long term demands
DISTRIBUTOR
• Fertilizers
• Pesticides
• Tools
• Maintenance
FARM EQUIPMENT SUPPLIER
• Field data
(humidity, plant
moisture, pH, EC, etc.)
FIELD / STABLE
• Work plans
• Maintenance plans
• …
HR & MACHINES
• Products & Prices
• predictions / future
availabilities
• Long term capacities
DISTRIBUTION / TRANSPORT
• Irrigation
• Fertilizer / food
• Medicaments
• Pesticides
FIELD / STABLE
• Fruit sequences
• Diseases
• …
FARMING KNOWLEDGE
IoT
Big
Data
IoT IoT
6. 1 Example: Flat Rate Food

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6. The Future
User Level
Efficiency
Gains
Smart Companions
Cross-sector
Product Developments &
New Business Models
IoT with Big Data & Cloud
Users

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Smart Companions
6. The Future

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Are IoT & Big Data Game Changers?
 IoT & Big Data are no Technological Game Changers!
 Networked sensors and actuators
 Mobile devices and wearables
 Cloud services
 Data Analytics
 The large scale integration of all will become a reality
 New IoT & Big Data driven Business Models and inherent Efficiency Gains are
Game Changers!
 Mind Ray Kurzweil!
7. Conclusion

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8. Remark
Mind and Ensure the Difference!
Deus ex Machina
(Greek origin)
(24/7, Global, Real Time)
References: ‘Star Trek’ (1966–1969) from director Gene
Roddenberry; ‘Metropolis’ (1927) from Director Fritz
Lang.
The Ghost in the Machine
(Gilbert Ryle; Arthur Koestler)
(self destruction by
Technology being misused)