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Today's project is from Dave Voyles sharing a new possible scripting language for the Kinect coming from Microsoft Research, Prepose...
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Prepose: Security and Privacy for Gesture-Based Programming
Abstract - With the rise of sensors such as the Microsoft Kinect, Leap Motion, and hand motion sensors in phones such as the Samsung Galaxy S5, natural user interface (NUI) has become practical. NUI raises two key challenges for the developer: First, developers must create new code to recognize new gestures, which is a time consuming process. Second, to recognize these gestures, applications must have access to depth and video of the user, raising privacy problems. We address both problems with Prepose, a novel domain-specific language (DSL) for easily building gesture recognizers, combined with a system architecture that protects user privacy against untrusted applications by running Prepose code in a trusted core, and only interacting with applications via gesture events.
Prepose lowers the cost of developing new gesture recognizers by exposing a range of primitives to developers that can capture many different gestures. Further, Prepose is designed to enable static analysis using SMT solvers, allowing the system to check security and privacy properties before running a gesture recognizer. We demonstrate that Prepose is expressive by creating novel gesture recognizers for 28 gestures in three representative domains: physical therapy, tai-chi, and ballet.We further show that matching user motions against Prepose gestures is efficient, by measuring on traces obtained from Microsoft Kinect runs.
Because of the privacy-sensitive nature of always-on Kinect sensors, we have designed the Prepose language to be analyzable: we enable security and privacy assurance through precise static analysis. In Prepose, we employ a sound static analysis that uses an SMT solver (Z3), something that works well on Prepose but would be hardly possible for a general-purpose language. We demonstrate that static analysis of Prepose code is efficient, and investigate how analysis time scales with the complexity of gestures. Our Z3-based approach scales well in practice: safety checking is under 0.5 seconds per gesture; average validity checking time is only 188 ms; lastly, for 97% of the cases, the conflict detection time is below 5 seconds, with only one query taking longer than 15 seconds.
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Prepose Scripting Language
Microsoft Research has taken this one step further though, and introduced a scripting language called “Prepose”, which allows for building Kinect gesture recognizers. You can find more information Link Removed
So how does it work?
“You create Kinect gesture recognizers by scripting high-level movements such as “raise your left leg to the side” instead of using machine learning or hand-tuned code. Prepose is the work of a team in Microsoft Research, powered by the Microsoft constraint solver Z3.”
” As examples (internally to Microsoft), we (David Molnar’s team) created Prepose scripts for tai chi, ballet, and physical therapy gestures this past summer, each tens of lines of code. I’ve included an example below to show what a Prepose script looks like.”
The team is are trying to figure out what to do next with the project so any feedback is helpful. You can always reach me here or find me on Twitter, Link Removed.
Here’s an annotated example to show Prepose syntax and concepts from David Molnar:
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Microsoft Research has taken this one step further though, and introduced a scripting language called “Prepose”, which allows for building Kinect gesture recognizers. You can find more information Link Removed
So how does it work?
“You create Kinect gesture recognizers by scripting high-level movements such as “raise your left leg to the side” instead of using machine learning or hand-tuned code. Prepose is the work of a team in Microsoft Research, powered by the Microsoft constraint solver Z3.”
” As examples (internally to Microsoft), we (David Molnar’s team) created Prepose scripts for tai chi, ballet, and physical therapy gestures this past summer, each tens of lines of code. I’ve included an example below to show what a Prepose script looks like.”
The team is are trying to figure out what to do next with the project so any feedback is helpful. You can always reach me here or find me on Twitter, Link Removed.
Here’s an annotated example to show Prepose syntax and concepts from David Molnar:
Link Removed
Project Information URL: Link Removed,
Prepose: Security and Privacy for Gesture-Based Programming
Abstract - With the rise of sensors such as the Microsoft Kinect, Leap Motion, and hand motion sensors in phones such as the Samsung Galaxy S5, natural user interface (NUI) has become practical. NUI raises two key challenges for the developer: First, developers must create new code to recognize new gestures, which is a time consuming process. Second, to recognize these gestures, applications must have access to depth and video of the user, raising privacy problems. We address both problems with Prepose, a novel domain-specific language (DSL) for easily building gesture recognizers, combined with a system architecture that protects user privacy against untrusted applications by running Prepose code in a trusted core, and only interacting with applications via gesture events.
Prepose lowers the cost of developing new gesture recognizers by exposing a range of primitives to developers that can capture many different gestures. Further, Prepose is designed to enable static analysis using SMT solvers, allowing the system to check security and privacy properties before running a gesture recognizer. We demonstrate that Prepose is expressive by creating novel gesture recognizers for 28 gestures in three representative domains: physical therapy, tai-chi, and ballet.We further show that matching user motions against Prepose gestures is efficient, by measuring on traces obtained from Microsoft Kinect runs.
Because of the privacy-sensitive nature of always-on Kinect sensors, we have designed the Prepose language to be analyzable: we enable security and privacy assurance through precise static analysis. In Prepose, we employ a sound static analysis that uses an SMT solver (Z3), something that works well on Prepose but would be hardly possible for a general-purpose language. We demonstrate that static analysis of Prepose code is efficient, and investigate how analysis time scales with the complexity of gestures. Our Z3-based approach scales well in practice: safety checking is under 0.5 seconds per gesture; average validity checking time is only 188 ms; lastly, for 97% of the cases, the conflict detection time is below 5 seconds, with only one query taking longer than 15 seconds.
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Contact Information:
- Blog: http://www.davevoyles.com
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