The Glass Class Lecture 7: Future Research
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The Glass Class: Lecture 7 – Future Research Feb 17 th – 21 st 2014 Mark Billinghurst, Gun Lee HIT Lab NZ University of Canterbury
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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.
Transcript of The Glass Class Lecture 7: Future Research
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
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Kopin Pupil
Eye-Glass display 428x240 resolution Voice interactivity
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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
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THE GLASS CLASS
Samsung Galaxy Gear
Watch based wearable
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Samsung Galaxy Gear
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Nike Fuelband
Activity/sleep tracking
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Device Ecosystem
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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
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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
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Natural Hand Interaction
Using bare hands to interact with AR content MS Kinect depth sensing Real time hand tracking Physics based simulation model
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Meta Gesture Interaction
Depth sensor + Stereo see-through
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Meta Video
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Gesture Based Interaction
3 Gear Systems Kinect/Primesense Sensor Two hand tracking http://www.threegear.com
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Gesture Interaction + AR
HMD AR View Viewpoint tracking
Two hand input Skeleton interaction, occlusion
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Multimodal Interaction
Combined speech and Gesture Input Free-hand gesture tracking Semantic fusion engine (speech + gesture input history)
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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.
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Contact Lens Display Babak Parviz
University Washington MEMS components
Transparent elements Micro-sensors
Challenges Miniaturization Assembly Eye-safe
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Contact Lens Prototype
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Intelligent Feedback
Actively monitors user behaviour Implicit vs. explicit interaction
Provides corrective feedback
Scaling Up
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Ego-Vision Collaboration
Google Glass camera + processing + display + connectivity
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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?
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Human Computation Architecture
Add AR front end to typical HC platform
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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
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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
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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
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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
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Sight Video Demo
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More Information Mark Billinghurst
Email: [email protected] Twitter: @marknb00
HIT Lab NZ http://www.hitlabnz.org/
