The Glass Class Lecture 7: Future Research
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The Glass Class Lecture 7: Future Research

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Lecture 7 in the Glass Class course. Presented on February 21st 2014 by Mark Billinghurst. This lecture discusses directions for future research using Google Glass.

Lecture 7 in the Glass Class course. Presented on February 21st 2014 by Mark Billinghurst. This lecture discusses directions for future research using Google Glass.

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The Glass Class Lecture 7: Future Research The Glass Class Lecture 7: Future Research Presentation Transcript

  • The Glass Class: Lecture 7 – Future Research Feb 17th – 21st 2014 Mark Billinghurst, Gun Lee HIT Lab NZ University of Canterbury
  • THE GLASS CLASS
  • THE GLASS CLASS “The best way to Predict the future is to Invent it.”                Alan  Kay      Computer  Scien3st  (1940-­‐  )  
  • THE GLASS CLASS Directions for Research   New devices   Input methods   User experience   Scaling up   Social Consequences
  • New Devices
  • THE GLASS CLASS Kopin Pupil   Eye-Glass display   428x240 resolution   Voice interactivity
  • THE GLASS CLASS GlassUp - http://www.glassup.net/   Glasses form factor – 320x240 pixel resolution   Secondary mobile display
  • THE GLASS CLASS Telepathy One -http://tele-pathy.org/   Minimal display
  • THE GLASS CLASS
  • THE GLASS CLASS Samsung Galaxy Gear   Watch based wearable
  • THE GLASS CLASS Samsung Galaxy Gear
  • THE GLASS CLASS Nike Fuelband   Activity/sleep tracking
  • THE GLASS CLASS Device Ecosystem
  • THE GLASS CLASS Wearable Attributes   fafds
  • Input Techniques/User Experience
  • THE GLASS CLASS The Vision of AR
  • THE GLASS CLASS To Make the Vision Real..   Hardware/software requirements  Intelligent systems  Contact lens displays  Free space hand/body tracking  Speech/gesture recognition  Etc..   Most importantly  Usability
  • THE GLASS CLASS Environment Sensing   Create virtual mesh over real world   Update at 10 fps – can move real objects   Use by physics engine for collision detection (virtual/real)   Use by OpenScenegraph for occlusion and shadows
  • THE GLASS CLASS Natural Hand Interaction   Using bare hands to interact with AR content   MS Kinect depth sensing   Real time hand tracking   Physics based simulation model
  • THE GLASS CLASS Meta Gesture Interaction   Depth sensor + Stereo see-through
  • THE GLASS CLASS Meta Video
  • THE GLASS CLASS Gesture Based Interaction   3 Gear Systems   Kinect/Primesense Sensor   Two hand tracking   http://www.threegear.com
  • THE GLASS CLASS Gesture Interaction + AR   HMD AR View   Viewpoint tracking   Two hand input   Skeleton interaction, occlusion
  • THE GLASS CLASS Multimodal Interaction   Combined speech and Gesture Input   Free-hand gesture tracking   Semantic fusion engine (speech + gesture input history)
  • THE GLASS CLASS User Evaluation   Change object shape, colour and position   Results   MMI signif. faster (11.8s) than gesture alone (12.4s)   70% users preferred MMI (vs. 25% speech only) Billinghurst, M., & Lee, M. (2012). Multimodal Interfaces for Augmented Reality. In Expanding the Frontiers of Visual Analytics and Visualization (pp. 449-465). Springer London.
  • THE GLASS CLASS Contact Lens Display   Babak Parviz   University Washington   MEMS components   Transparent elements   Micro-sensors   Challenges   Miniaturization   Assembly   Eye-safe
  • THE GLASS CLASS Contact Lens Prototype
  • THE GLASS CLASS Intelligent Feedback   Actively monitors user behaviour   Implicit vs. explicit interaction   Provides corrective feedback
  • Scaling Up
  • THE GLASS CLASS Ego-Vision Collaboration   Google Glass   camera + processing + display + connectivity
  • THE GLASS CLASS Ego-Vision Research   System   How do you capture the user's environment?   How do you provide good quality of service?   Interface   What visual and audio cues provide best experience?   How do you interact with the remote user?   Evaluation   How do you measure the quality of collaboration?
  • THE GLASS CLASS AR + Human Computation   Human Computation   Real people solving problems difficult for computers   Web-based, non real time   Little work on AR + HC   AR attributes   Shared point of view   Real world overlay   Location sensing What does this say?
  • THE GLASS CLASS Human Computation Architecture   Add AR front end to typical HC platform
  • THE GLASS CLASS AR + HC Research Questions   System   What architecture provides best performance?   What data is needed to be shared?   Interface   What cues are needed by the human computers?   What benefits does AR provide cf. web systems?   Evaluation   How can the system be evaluated?
  • THE GLASS CLASS Scaling Up   Seeing actions of millions of users in the world   Augmentation on city/country level
  • THE GLASS CLASS AR + Smart Sensors + Social Networks   Track population at city scale (mobile networks)   Match population data to external sensor data   medical, environmental, etc   Mine data to improve social services
  • THE GLASS CLASS
  • THE GLASS CLASS
  • THE GLASS CLASS Orange Data for Development   Orange made available 2.5 billion phone records   5 months calls from Ivory Coast   > 80 sample projects using data   eg: Monitoring human mobility for disease modeling
  • THE GLASS CLASS Research Questions   System   How can you capture the data reliably?   How can you aggregate and correlate the information?   Interface   What data provides the most values?   How can you visualize the information?   Evaluation   How do you measure the accuracy of the model?
  • Social Consequences
  • THE GLASS CLASS The Future of Wearables
  • THE GLASS CLASS Sight Video Demo
  • THE GLASS CLASS More Information   Mark Billinghurst   Email: mark.billinghurst@hitlabnz.org   Twitter: @marknb00   HIT Lab NZ   http://www.hitlabnz.org/