cse seminar topic

7/28/2019 cse seminar topic

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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

Documents

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