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Feb 22, 2014 (3 years and 7 months ago)

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Intelligent Environments


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:


Optimize inhabitant productivity


Minimize operating costs


Improve comfort


Simplify use of technologies


Ensure security


Enhance accessibility

Requirements for

Intelligent Environments


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


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


Georgia Tech Aware Home


MIT Intelligent Room


Stanford Interactive Workspaces


UC Boulder Adaptive House


UTA MavHome Smart Home


TCU Smart Home


Existing Projects


Industry


General Electric Smart Home


Microsoft Easy Living


Philips Vision of the Future


Verizon Connected Family

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/

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/

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/

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/

UTA MavHome Smart Home


Learning of inhabitant patterns


Learn optimal automation strategies


Goals


Maximize comfort and productivity
Minimize cost


Ensure security


http://ranger.uta.edu/smarthome/

TCU Smart Home


Inhabitant Prediction


Smart entertainment control


Smart kitchen recipe services


Household staff modeling


http://personal.tcu.edu/~lburnell/crescent/cr
escent.html

General Electric Smart Home


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


Camera
-
based person detection and tracking


Geometric world modeling for context


Multimodal sensing


Biometric authentication


Distributed systems


Ubiquitous computing


http://research.microsoft.com/easyliving/

Philips Vision of the Future


Less obtrusive technology


Technology devices


Interactive wallpaper


Control wands


Intelligent garbage can


http://www.design.philips.com/vof

Verizon Connected Family


Remote monitoring of the home


Entry authentication


Integrated, pervasive communications


Centralized data management


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


Sensors


How many and what type?


How to interpret sensor data?


How to interface with sensors?


Are sensors active or passive?

Communications


What medium and protocol?


How to handle bandwidth limitations?


What structure does the communication
infrastructure have?

Data Management


How to store all the data?


What data is stored?


How is data distributed to the pervasive
computing infrastructure?

Prediction & Decision Making


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


How are the tasks automated?


How are actuators controlled?


How is safety ensured?

System Integration


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?


What data should be gathered?


How should personal data be
maintained and used?

Course Topics


Sensing


Networking


Databases


Prediction and Data Mining


Decision Making


Robotics


Privacy Issues


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