Own Project Update - Multiple Domain Measurements

My own ideas over what I want to do with my own Leap device has been shaping up over the past month of getting distracted, so here's a quick rough sketch over what I have done so far and what I plan on pushing later.

I am trying to have my prior expertise with application performance measurements on supercomputers dovetail into the Leap. As such, I've been using the Leap API to measure various aspects of hand, finger, and tool motion that are tracked by the device. Of the various performance measurement tools I am used to, the SCORE-P project (http://www.vi-hps.org/projects/score-p/) is one which implements support for user-level modules that can feed information (Generally, SCORE-P expects this to be performance-related data) to it. If I can cast Leap-related measurements and metrics in terms of performance, what this allows me to do is have a very convenient mechanism to visualize and analyze how the two domains of information are correlated. So, on top of possibly developing analytical methods for gesture tracking information (e.g., is a gesture hard to achieve for individual X? How hard?), we can also measure how well the underlying library and application responds computationally to an attempt to achieve a gesture, performance-wise. From there, we can think about various visualization and analysis approaches that are different from traditional computing performance analysis, particularly when multiple domains of information come into play.

To this goal, my actual accomplishments have been minimal. I've successfully used Xcode (I had loads of initial difficulty getting into the spirit of it, as I had been used to standard Linux approaches to shared object libraries on the console) to construct my own Cinder (http://libcinder.org/) application to interface with the Leap library, and then to use the API's listeners to start tracking useful information over time. I've been able to track various interesting metrics regarding my fingers (again, I am unsure if I am legally allowed to say what, since that kinda exposes part of the API) and generate a "performance" profile of an active session of motion tracking.

My next step would be to attempt to integrate what I have so far with the SCORE-P instrumentation interface as a module. The subtle nuance here is that SCORE-P works on a data-pull model for measuring stuff. My plan is to have SCORE-P instrument key events in the application vis-a-vis gestures, as well as important events in the Leap API. That way, whenever SCORE-P asks me for motion information, it will always been in line with some key gesture event. At the same time, I get the bonus of acquiring from SCORE-P the computational performance information for free. When successful, I will acquire output in the form of either a CUBE-4 performance profile (http://www.scalasca.org/software/cube-4.x/download.html) or a Open Trace Format (OTF) trace file (http://www.tu-dresden.de/die_tu_dresden/zentrale_einrichtungen/zih/forschung/projekte/otf). There are a multitude of visualizers and analysis engines for performance based on those two formats. With the inclusion of Leap gesture data, perhaps there will be a need to develop newer framework approaches to multi-domain visualization tools.