Smart home technologies
Download
1 / 29

- PowerPoint PPT Presentation


  • 886 Views
  • Uploaded on

Smart Home Technologies. CSE 4392 / CSE 5392 Spring 2006 Manfred Huber [email protected] Intelligent Environments. Environments that use technology to assist inhabitants by automating task components Aimed at improving inhabitants’ experience and task performance

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '' - ryanadan


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Smart home technologies l.jpg
Smart Home Technologies

CSE 4392 / CSE 5392

Spring 2006

Manfred Huber

[email protected]


Intelligent environments l.jpg
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 l.jpg
Objectives ofIntelligent Environments

  • Improve Inhabitant experience:

    • Optimize inhabitant productivity

    • Minimize operating costs

    • Improve comfort

    • Simplify use of technologies

    • Ensure security

    • Enhance accessibility


Requirements for intelligent environments l.jpg
Requirements forIntelligent 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 l.jpg
Examples of Intelligent Environments

  • Intelligent Workspaces

    • Automatic note taking

    • Simplified information sharing

    • Optimized climate controls

    • Automated supply ordering


Examples of intelligent environments6 l.jpg
Examples of Intelligent Environments

  • Intelligent Vehicles

    • Location-aware navigation systems

    • Task-specific navigation

    • Traffic-awareness


Examples of intelligent environments7 l.jpg
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 l.jpg
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 projects9 l.jpg
Existing Projects

  • Industry

    • General Electric Smart Home

    • Microsoft Easy Living

    • Philips Vision of the Future

    • Verizon Connected Family


Georgia tech aware home l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
TCU Smart Home

  • Inhabitant Prediction

  • Smart entertainment control

  • Smart kitchen recipe services

  • Household staff modeling

  • http://personal.tcu.edu/~lburnell/crescent/crescent.html


General electric smart home l.jpg
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 l.jpg
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 l.jpg
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 l.jpg
Verizon Connected Family

  • Remote monitoring of the home

  • Entry authentication

  • Integrated, pervasive communications

  • Centralized data management


Challenges in intelligent environments l.jpg
Challenges inIntelligent 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 l.jpg
Sensors

  • How many and what type?

  • How to interpret sensor data?

  • How to interface with sensors?

  • Are sensors active or passive?


Communications l.jpg
Communications

  • What medium and protocol?

  • How to handle bandwidth limitations?

  • What structure does the communication infrastructure have?


Data management l.jpg
Data Management

  • How to store all the data?

  • What data is stored?

  • How is data distributed to the pervasive computing infrastructure?


Prediction decision making l.jpg
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 l.jpg
Automation

  • How are the tasks automated?

  • How are actuators controlled?

  • How is safety ensured?


System integration l.jpg
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 l.jpg
Privacy

  • How to ensure that inhabitant information remains private?

  • What data should be gathered?

  • How should personal data be maintained and used?


Course topics l.jpg
Course Topics

  • Sensing

  • Networking

  • Databases

  • Prediction and Data Mining

  • Decision Making

  • Robotics

  • Privacy Issues


Example scenario l.jpg
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


ad