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