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Smart Home Technologies
CSE 4392 / CSE 5392
Spring 2006
Manfred [email protected]
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Intelligent Environments Environments that use technology to
assist inhabitants by automating task
components
Aimed at improving inhabitantsexperience and task performance
NOT: large number of electronicgadgets
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Objectives of
Intelligent Environments Improve Inhabitant experience:
Optimize inhabitant productivity
Minimize operating costs
Improve comfort
Simplify use of technologies
Ensure security
Enhance accessibility
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Requirements for
Intelligent EnvironmentsAcquire and apply knowledge about
tasks that occur in the environment
Automate task components thatimprove efficiency of inhabitant tasks Provide unobtrusive human-machine
interfacesAdapt to changes in the environment
and of the inhabitants Ensure privacy of the inhabitants
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Examples of
Intelligent Environments Intelligent Workspaces
Automatic note taking
Simplified information sharing
Optimized climate controls
Automated supply ordering
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Examples of
Intelligent Environments Intelligent Vehicles
Location-aware navigation systems
Task-specific navigation
Traffic-awareness
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Examples of
Intelligent Environments Smart Homes
Optimized climate and light controls
Item tracking and automated ordering for
food and general use items
Automated alarm schedules to match
inhabitants preferences
Control of media systems
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Existing ProjectsAcademic
Georgia Tech Aware Home
MIT Intelligent Room
Stanford Interactive Workspaces
UC Boulder Adaptive House
UTA MavHome Smart Home
TCU Smart Home
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Existing Projects Industry
General Electric Smart Home
Microsoft Easy Living
Philips Vision of the Future
Verizon Connected Family
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Georgia Tech Aware Home Perceive and assist occupants
Aging in Place (crisis support)
Ubiquitous sensing Scene understanding, object recognition
Multi-camera, multi-person tracking
Context-based activity Smart floor
http://www.cc.gatech.edu/fce/ahri/
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MIT Intelligent Room Support natural interaction with room
Speech-based information access
Gesture recognition
Movement tracking
Context-aware automation
http://www.ai.mit.edu/projects/aire/
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Stanford Interactive
Workspaces Large wall and tabletop interactive
displays
Scientific visualization
Mobile computing devices
Computer-supported cooperative work
Distributed system architectures
http://iwork.stanford.edu/
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UC Boulder Adaptive House Infer patterns and predict actions
Machine learning for automation
HVAC, water heater, lighting control
Goals:
Reduce occupant manual control
Improve energy efficiency
http://www.cs.colorado.edu/~mozer/house/
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UTA MavHome Smart Home Learning of inhabitant patterns
Learn optimal automation strategies
Goals
Maximize comfort and productivityMinimize cost
Ensure security
http://ranger.uta.edu/smarthome/
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TCU Smart Home Inhabitant Prediction
Smart entertainment control Smart kitchen recipe services
Household staff modeling
http://personal.tcu.edu/~lburnell/crescent/cr
escent.html
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General Electric Smart HomeAppliance control interfaces
Climate control
Energy management devices
Lighting control
Security systems Consumer Electronics Bus (CEBus)
http://www.geindustrial.com/cwc/home
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Microsoft Easy Living Camera-based person detection and tracking
Geometric world modeling for context
Multimodal sensing
Biometric authentication
Distributed systems
Ubiquitous computing
http://research.microsoft.com/easyliving/
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Philips Vision of the Future Less obtrusive technology
Technology devices Interactive wallpaper
Control wands
Intelligent garbage can
http://www.design.philips.com/vof
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Verizon Connected Family Remote monitoring of the home
Entry authentication
Integrated, pervasive communications
Centralized data management
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Challenges in
Intelligent Environments Home design and sensor layout Communication and pervasive computing
Natural interfaces Management of available data Capture and interpretation of tasks
Decision making for automation Robotic control Large-scale integration Inhabitant privacy
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Sensors How many and what type?
How to interpret sensor data?
How to interface with sensors?
Are sensors active or passive?
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Communications What medium and protocol?
How to handle bandwidth limitations?
What structure does the communication
infrastructure have?
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Data Management How to store all the data?
What data is stored?
How is data distributed to the pervasive
computing infrastructure?
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Prediction & Decision Making How to extract and represent
inhabitants task patterns?
What patterns should be maintained?
How to determine the actions toautomate?
To what level should tasks beautomated?
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Automation
How are the tasks automated?
How are actuators controlled?
How is safety ensured?
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System Integration How to achieve extensibility?
Should the system be centralized or
decentralized?
How to integrate existing technologycomponents?
How to make integration and interfaceintuitive?
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Privacy How to ensure that inhabitant
information remains private? What data should be gathered?
How should personal data bemaintained and used?
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Course Topics Sensing
Networking
Databases
Prediction and Data Mining
Decision Making Robotics
Privacy Issues
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Example Scenario Smart kitchen item tracking
Sense and monitor items in the kitchen
Predict usage patterns
Automatically generate shopping lists based
on usage patterns
Automatically retrieve replacement items