Visual 2015 Release Event Happening Now!!!

Ever wonder how I build some of the cool apps I’ve blogged about?

Well Visual Studio .Net is the answer and the next version of it has just been released. You can see all the cool features about it here:


I’m presenting at the MVP Virtual Conference


For those who would like to hear about using the Microsoft Kinect for Windows v2 to detect facial expressions, I will be presenting a 50 minute session online for the first ever Microsoft MVP Virtual Conference on just that.

Save the Date: May 14th @2pm – 2:50pm EST…


There will be many sessions not just mine. There will be topics on Windows 10, Microsoft Azure, Microsoft Edge (formerly project spartan), SQL Server, Power BI, Office 365, Enterprise Mobility, Surface, Powershell, Skype for Business, Hyper-V, System Center, OWIN, ASP.Net vNext, Unity 3D, AngularJS, Puppet, Microsoft Band, Xamarin and much more.

If you’re interested in this free online event, register here: 

Come support me and my fellow MVP’s!!! Troll us or simply ask us questions. I hope to hear and see you there.

Denver Kinect + Microsoft Band + Unity Hackathon


Last year I went all around Colorado drumming up support for Kinect development by talking at user groups, breakout sessions, and meetings. I’ve spoken in Fort Collins, Fort Carson, Boulder, Denver, Colorado Springs, Fountain, and many more areas. I’m glad to announce with all the support we’ve received, we are now holding a Kinect hackathon in Denver Colorado, April 24th – 25th 2015.

This event will bring the Kinect for Microsoft engineers down to an all night event, answering any questions you have about the device and how to program with it. The Microsoft Kinect team will also bring, spare Kinect devices, Microsoft Bands, Unity Pro plugins, and the Unreal 4 engine plugin, along with other surprises.

Kinect for Windows MVP’s such as myself will also be there to help, and tell you about things we’re working on, experiences, and possibly even opportunities for you to get architecture and coding help.

This event is for designers, gamers, developers, enthusiasts, students, hobbyists, idealists, medical researchers. If you have an idea you’d like to see built, or a game you want to play that is Kinect enabled this is the place to be.

Register for the upcoming #denver #hackathon today at: Space is limited! #kinect #k4wdev #k4wv2

So click on the link above to register. Don’t miss this opportunity to meet the best minds in the NUI, Kinect, Hololens world…

My Kinect told me I have Dark Olive Green Skin…

Did you know the Kinect for windows v2 has the ability to determine your Skin  pigmentation and your hair color? – Yes I’m telling you the truth. One of the many features of the Kinect device is the ability to read skin complexion and hair color of a person who is being tracked by the device.

If you ever need or require the ability to read the skin complexion of a person or determine the color of a persons hair on their head, this posting will show you how to do just that.


The steps are rather quick and simple. Determining the skin color requires you to access Kinect’s HD Face features.

Kinect has the ability to detect Facial features in 3-D. This is known as “HD Face”. It can detect depth, height, and width. The Kinect can also use it’s High Definition Camera, to detect colors such as the Red, Green, and Blue intensities that reflect back, and infer the actual skin tone of a tracked face. Along with the skin tone, the Kinect can also detect the Hair color on top of a person’s head…

So What’s Your Skin Tone? Click Here to download the source code and try it out.

If you want to include this feature inside your application, the steps you must take are:

1. Create a new WPF or Windows 8.1 WPF application

2 Inside the new application, add a reference to the Microsoft.Kinect and Microsoft.Kinect.Face assemblies.


3. Let’s also make sure we set this up for the proper processor architecture: HD Face supports both 32bit and 64 bit. I want to use 64 bit. Change your build settings to use 64 bit configuration and builds from the project properties in VS.Net:


The above step is very important. You must choose either x86 (32 bit) or x64 (64 bit) architecture and build accordingly. “Any Cpu” won’t work as is here. The reason being that the Kinect Assemblies are named exactly the same thing, however they are compiled appropriately for each architecture. You can easily get a “BadFormat” exception if you’re using version x86 with a 64 bit build and vice versa.

4. Next copy the correct version of the NuiDatabase from the Kinect Redist folder into your \bin\x64\Debug  folder path for your project. This step is also important. If you mis-match your versions, by copying the x86 NuiDatabase into a 64bit compiled application you’ll start to see weird errors during runtime, things like it can’t find your Kinect.Face assembly, and “BadFormat” errors. So make sure you choose the correct architecture.


Note: Optionally You can also use the Kinect Nu-Get packages which will basically do the right thing for you. However you can’t mix and match. You can’t manually add references and then go back and add NuGet packages, things will quickly get out of sync:


5. Inside your code add the namespaces for Kinect and Kinect HD Face:

using Microsoft.Kinect;
using Microsoft.Kinect.Face;

6. Create some variables to hold pointers to the artefacts:

        private KinectSensor m_sensor;
        private BodyFrameReader m_bodyReader;
        private HighDefinitionFaceFrameReader m_hdFaceReader;
        private HighDefinitionFaceFrameSource m_hdFaceSource;
        private FaceModel m_faceModel;
        private FaceAlignment m_faceAlignment;
        private FaceModelBuilder m_faceBuilder;
        private ulong m_trackedBodyId;
        private bool m_faceBuilderStarted;
        private bool m_faceBuildComplete;

The m_sensor holds a pointer to the Kinect Device itself. We’ll use this to get access to the Body Frames, High Definition Face Frames, FaceModel, FaceModel Builder and tracked person. The m_bodyReader will be a frame reader for determining a body being tracked. The Kinect sends 30 frames per second. Each frame can tell us if a person is found within that frame of data. The m_hdFaceSource will be the HD Face source to keep track of bodyTrackingID’s and give us access to the 30 frames per second data of HD Face Frames. The m_hdFaceReader will be used as each HD Face Frame is processed, it allows us to get the 3-D Face information (FaceModel), and listen for events which allow us to build a complete 180 degree view of the face. The m_faceModel will be the 3-D Face measurements. The m_faceBuilder will be used to build the 180 degree HD Face model which will be stored inside the m_hdFaceModel. The m_faceBuilder provides us with the internal mechanism to build an internal matrice of 3-D Face depth values of  IR, and Color (RGB) information. This allows us to then produce the complete m_hdFaceModel with Skin Color and Hair Color respectively. The m_faceBuilder also allows us to listen for events that tell us when the tracked face needs to rotate left, right, and tilt up, to make sure the complete matrix is built. The m_trackedBodyId is a tracking id that synchronizes the tracked Body, with the HD Face Source. Without a synchronized tracked person the HD Face can not perform it’s work. Lastly, there are two flag variables that will help us keep track of when the Face Builder process has started, and when the Face Builder process has completed.

Game Plan:

Overall what the application is going to do is initialize the Kinect sensor  and variables to default values. It will then setup the BodyFrameReader to listen for body frames to come from the Kinect. Once a body frame is generated, we will determine if a body is within the frame and figure out if the body is tracked. If the body is tracked, we will get the trackingId of the body and set it to the HDFrameSource. Once the tracking Id is set on the HDFrame source, this will generate HDFaceFrame events. Once a valid HDFaceFrame is generated we will start the face builder process. We will ask the face builder to start the process of building the 180 degree face model matrix. At this point the tracked user needs to turn their head slowly left and back to center, right and back to center, up and down back to center until the face builder notify us when it’s complete building the matrix. Once complete we ask the face builder to produce the 3-d face model. The 3-D face model then gives us access to the Skin Color, Hair Color, and 3-D depth and matrices.

7.  Initialize the sensor get an instance of your Kinect sensor, initialize your bodyReader, hdFaceReader, faceModel, trackingId and faceAligment variables:

 public MainWindow()

        public void InitializeKinect()
            m_sensor = KinectSensor.GetDefault();
            m_bodyReader = m_sensor.BodyFrameSource.OpenReader();
            m_bodyReader.FrameArrived += m_bodyReader_FrameArrived;
            m_hdFaceSource = new HighDefinitionFaceFrameSource(m_sensor);
            m_hdFaceReader = m_hdFaceSource.OpenReader();
            m_hdFaceReader.FrameArrived += m_hdFaceReader_FrameArrived;
            m_faceModel = new FaceModel();
            m_faceBuilder =
                 | FaceModelBuilderAttributes.SkinColor);
            m_faceBuilder.CollectionCompleted += m_faceBuilder_CollectionCompleted;
            m_faceBuilder.CaptureStatusChanged += m_faceBuilder_CaptureStatusChanged;
            m_faceBuilder.CollectionStatusChanged += m_faceBuilder_CollectionStatusChanged;
            m_faceAlignment = new FaceAlignment();
            m_trackedBodyId = 0;
            m_faceBuilderStarted = false;
            m_faceBuildComplete = false;

8. Inside the BodyReader_FrameArrived, event handler, add code to determine when the Kinect tracks a body, once Kinect finds the tracked body, set the trackingId for the hdFaceReader Source.

void m_bodyReader_FrameArrived(object sender, BodyFrameArrivedEventArgs e)
            using (var bodyFrame = e.FrameReference.AcquireFrame())
                if (null != bodyFrame)
                    Body[] bodies = new Body[bodyFrame.BodyCount];
                    foreach (var body in bodies)
                        if (body.IsTracked)
                            m_trackedBodyId = body.TrackingId;
                            m_hdFaceReader.HighDefinitionFaceFrameSource.TrackingId = m_trackedBodyId;

9. Once the trackingId is set for the HdFaceFrameReader Source, this will kick off the HD Face Frame Arrived event handler. Just check the flag and start the face builder process:

void m_hdFaceReader_FrameArrived(object sender, HighDefinitionFaceFrameArrivedEventArgs e)
            if (!m_faceBuilderStarted)


10. In the FaceBuilder_CollectionStatus, just listen for a complete status. This allows us to set our flag for letting us know all the face views have been correctly captured and we can ask for the face builder to give us the model:

 void m_faceBuilder_CollectionStatusChanged(object sender, FaceModelBuilderCollectionStatusChangedEventArgs e)
            var collectionStatus = e.PreviousCollectionStatus;
            switch (collectionStatus)
                    case FaceModelBuilderCollectionStatus.Complete:
                    lblCollectionStatus.Text = "CollectionStatus: Complete";
                    m_faceBuildComplete = true;


11. In the faceBuilder_CollectionCompleted event handler check the collection status to make sure it’s completed, check your flag to make sure it’s set, and then ask the faceBuilder to produce the FaceModel using the event argument variable. The face Model provides access to the Skin Color and Hair Color as an Unsigned Integer (UINT).  To make this an actual drawing color, we’ll need to convert the UINT to a color structure. The color structure can be created using some old skool bit shifting, see below.

private void m_faceBuilder_CollectionCompleted(object sender, FaceModelBuilderCollectionCompletedEventArgs e)
            var status = m_faceBuilder.CollectionStatus;
            //var captureStatus = m_faceBuilder.CaptureStatus;
            if (status == FaceModelBuilderCollectionStatus.Complete && m_faceBuildComplete)
                    m_faceModel = e.ModelData.ProduceFaceModel();
                catch (Exception ex)
                    lblCollectionStatus.Text = "Error: " + ex.ToString();
                    lblStatus.Text = "Restarting...";
                    m_faceBuildComplete = false;
                    m_faceBuilderStarted = false;
                    var skinColor = UIntToColor( m_faceModel.SkinColor);
                    var hairColor = UIntToColor(m_faceModel.HairColor);
                    var skinBrush = new SolidColorBrush(skinColor);

                    var hairBrush = new SolidColorBrush(hairColor);

                    skinColorCanvas.Background = skinBrush;

                    lblSkinColor.Text += " " + skinBrush.ToString();

                hairColorCanvas.Background = hairBrush;
                lblHairColor.Text += " " + hairBrush.ToString();

                    m_faceBuilderStarted = false;
        private Color UIntToColor(uint color)
            //.Net colors are presented as
            // a b g r
            //instead of
            // a r g b
            byte a = (byte)(color >> 24);
            byte b = (byte)(color >> 16);
            byte g = (byte)(color >> 8);
            byte r = (byte)(color >> 0);
            return Color.FromArgb(250, r, g, b);


12. Lastly add the WPF Labels, and Canvas elements to your app so you can actually see something:

<Window x:Class="KinectFindingSkinTone.MainWindow"
        Title="MainWindow" Height="350" Width="525">
        <SolidColorBrush x:Key="MediumGreyBrush" Color="#ff6e6e6e" />
        <SolidColorBrush x:Key="KinectPurpleBrush" Color="#ff52318f" />
        <SolidColorBrush x:Key="KinectBlueBrush" Color="#ff00BCF2" />

    <Grid Background="White" Margin="10 0 10 0">

        <StackPanel Margin="20">
            <TextBlock x:Name="lblCollectionStatus"  Text="CollectionStatus: " Foreground="{StaticResource KinectBlueBrush}" FontSize="20" />
            <TextBlock x:Name="lblStatus"  Text="FrameStatus: " Foreground="{StaticResource KinectBlueBrush}" FontSize="20" />

            <TextBlock x:Name="lblSkinColor"  Text="Skin Color: " Foreground="{StaticResource KinectBlueBrush}" FontSize="20" />
                       <Border BorderBrush="Black"><Canvas Width="300" Height="100"  x:Name="skinColorCanvas" Background="DarkGray"></Canvas></Border>
            <TextBlock x:Name="lblHairColor"  Text="Hair Color: " Foreground="{StaticResource KinectBlueBrush}" FontSize="20" />
                <Border BorderBrush="Black">
            <Canvas Width="300" Height="100" x:Name="hairColorCanvas" Background="DarkGray"></Canvas>

Once your application runs it should look similar to this (Minus the FrameStatus):


Try it out on your own.

Using Kinect HD Face to make the MicroHeadGesture Library

Currently, I am working on a medical project which requires detection of Head Nods (in agreement), Head Shakes (in disagreement), and Head Rolls (Asian/East Indian head gesture for agreement) within a computer application.

Being that I work with the Kinect for Windows device, I figured this device is perfect for this type of application.

This posting serves as explanation to how I built this library, the algorithm used, and how I used the Kinect device and Kinect for Windows SDK to implement it.

Before we get into the Guts of how this all works, let’s talk about why the Kinect is the device that is perfect for this type of application.

The Kinect v2.0 Device has many capabilities. One of which allows the device to capture a persons face in 3-D… That is 3-Dimensions:


Envision the Z-axis arrow pointing straight out towards you in one direction, and out towards the back of the monitor/screen in the other direction.

In Kinect terminology, this feature is called HD Face. In HD Face, the Kinect can track the eyes, mouth, nose, eye brows, and other specific things about the face when a person looks towards the Kinect camera.


So envision a person’s face tracked in 3-D.


We can measure height, width, and depth of a face. Not only can we measure 3-d values and coordinates on various axes, with a little math and engineering we can also measure movements and rotations over time.

Think about normal head movements for a second. We as humans twist and turn our heads for various reasons. One such reason is proper driving techniques. We twist and turn our heads when driving looking for other cars on the road. We look up at the skies on beautiful days. We look down on floors when we drop things. We even slightly nod our heads in agreement, and shake our heads in disgust.

Question: So from a technical perspective what does this movement look like?

Answer: When a person moves their head, the head rotates around a particular axis. It’s either the X, Y, Z, or even some combination of the three axis. This rotation is perceived from a point on the head. For our purposes, let’s look at the Nose as the point of perspective.


When a person Nods their head, the nose is rotated around the X-axis in small up and down manner. The Nose coordinates for Head Nod makes the Y- coordinate values of the Nose point go up and down.

When a person Shakes their head, the nose is rotated around the Y-axis in a small left and right manner. The Nose coordinates for the Head Shake makes the X-coordinate values of the Nose point go up and down.

If we were to graph Nods and Shakes over time, their Y and X graphs would look like this:


Question: So great, we have a graph of Head Nods and Head Shakes… How do we get the Y, X and rotations from the head?

Answer: Luckily for us the Kinect for Windows SDK, provides us engineers with the HD Face Coordinates in 3-D. That is we get the X, Y, and Z coordinates of a Face. Due to linear algebra, and vector math, we can also derive the Rotational Data from this as well. HD Face gives us Facial orientation, and also Head Pivot data.

Question: Now we’re getting somewhere, so exactly how do you calculate Head Nods/Shakes/Rolls with the Kinect?

Answer: Well it takes a little creativity, and some help from some researchers in Japan (Shinjiro Kawato and Jun Ohya), who figured out the mathematically formula to derive the head position deviations.

So my implementation is based in part on this paper. Instead of “Between the eyes”, I decided to use the Nose, since the Kinect readily gives me this information fairly easily.

The implementation concept is simple.

First let’s assume, from the research paper that a typical Nod/Shake/Roll lasts about 1 to 1.4 seconds.

Next let’s take for fact that the Kinect device produces 30 frames per second. And as long as a person is facing the camera, the majority of these frames per second will produce a HD Face frame for us (assuming at least approx ~15-20 fps).

Therefore if I capture about 1-1.5 seconds of frames, I can determine Head Rotations, pixel coordinates (X, Y and Z), derive rotation in angles, and store this data in a state machine for each measured frame.

I can then change states for each measured frame from “Extreme” to “Stable” to “Transient” based on the algorithms provided by Kawato and Ohya.

I then use a delayed 5 frame buffer to evaluate a set of states for the last 3 of the 5 buffered frames.

Next thing I do is continue applying the algorithm from Kawato and Ohya to figure out when and precisely how to check for head nods/shakes/rolls inside my buffered frame states.

The mechanism to check is simple as well. If the current frame state changes from a non stable state to “Stable” then I go and evaluate for Nods/Shakes/Rolls.

The evaluation is also simple. During the evaluation process, if the previous frame states have more than 2 adjacent “Extreme” states, then I check to see if all the adjacent states have Nose rotation angles greater than a configurable threshold. By default my threshold is 1 degrees. Depending on which axis it is, Y – Nods, X – Shakes, Z – Rolls, I raise an event that the appropriate head action occurred.

Here’s a graphical view of the process flow:



Frame state depiction:



If you’re interested in testing out this library, please contact me here through this blog.

Here’s the library and a sample Windows 8.1 store application using the library in action. In the picture below, I have updated the HD Face Basic XAML sample for visualization. As the HD Face mesh head nods and shakes, I show the confidence of a Head Nod or Head Shake. On the left represents KinectStudio and a recorded clip of me testing the application


!Happi Kinecting!

Kinect HIG v2.0 Posted

Just a quick reminder, for those that will be developing applications for the Kinect for Windows v2, the Kinect Human Interface Guidelines v2.0 has been released back in October 30, 2014. I originally missed this posting so I’m putting here on my blog so I can find it again.

It contains 140 pages of recommendations, specifics, and best practices on how to place, use, develop and interact with Kinect enabled applications.

You can get the latest version of the guide here:

Awarded MVP 2015

This morning I awoke to an email:

Microsoft MVP Banner
Dear Dwight Goins,
Congratulations! We are pleased to present you with the 2015 Microsoft® MVP Award! This award is given to exceptional technical community leaders who actively share their high quality, real world expertise with others. We appreciate your outstanding contributions in Kinect for Windows technical communities during the past year.

2014 in review

The stats helper monkeys prepared a 2014 annual report for this blog.

Here’s an excerpt:

The concert hall at the Sydney Opera House holds 2,700 people. This blog was viewed about 23,000 times in 2014. If it were a concert at Sydney Opera House, it would take about 9 sold-out performances for that many people to see it.

Click here to see the complete report.

Kinect Hackathon Inter-view

I was fortunate enough to attend the New York City (NYC) Kinect Hackathon held on June 21, 2014 – June 22, 2014 @GrindSpaces. This posting is about my experiences and a self interview/review of the projects and experimental hardware Microsoft allowed us to play with. If you’re interested please read on…

Question: So exactly what is a Kinect Hackathon?

The Microsoft Kinect for Windows team has been touring the world trying to push and expose the new Kinect for Windows (K4W) v2 device. They have gone to places like San Francisco, Germany, and China. More recently, they stopped off in New York City. At each location, the team introduced new features, and hardware to the masses showing off capabilities and potential. In NYC, the way in which the team did this was to have a Hackathon contest.

A Hackathon is simply a gathering of technical minded people ranging from inventors, to designers, to enthusiasts, to hobbyists, to developers, architects, and just plain ole smart people who have an idea. The goal is to take this idea and see if the people in the room can make it a reality by building a Proof of Concept (POC).

The contest part of it is to see which team can come up with the best working POC, for one or more ideas within 24 hours. Food and drinks are supplied all night, and team members and architecting, designing, developing, and testing all night until that cut off time.

Question: Wow, that sounds like fun, What was it like?

It was very fun!!! Let me explain why. The day started off by Ben Lower, community manager for Kinect for Windows team, introducing us to various members of the K4W Team: Carmine, Chen, Smarth, Ragul, Kevin, David and Lori. (Please excuse name spellings and if I missed anyone I apologize) and then explained about the new experimental firmware update to make the K4W v2 device support near mode – up to a potential 1cm, although at the Hackathon the current edition was up to 10cm. Ben also talked about Kinect Ripple a new framework which allows you use the floor to map or calibrate a grid for a pseudo menu/command control system for an application, while still keeping the K4W normal functionality – body tracking, audio, etc.

The next this that transpired was opening the floor for ideas, and forming a team. A little feeling slighted note… the winners of the contest were teams which were pre-planned and prepared prior to this event, but that was ok.

People took the microphone in droves… I wish I had recorded all the great ideas and thoughts people envisioned with this device, because I could just quit my day job and work on each idea, one project at a time. Each idea has the potential to make profit, and benefit humanity. The few ideas I did remember ranged from tracking animals, plants, and people, to robots avoiding obstacles, to field sobriety tests, to automated CAD designs, to virtual orchestras, playing instruments with your body, occulus rift + kinect skeleton tracking, simple funny gestures, to move the virtual egg but don’t wake the rooster farm game, to robotic hand tracking


 image, to Kinect ripple based Mine sweeper, a kinect ripple based match that shape game, and of course but not least my idea of the Windows 8 Store app: Kinect Virtual Doctor.

After the idea forming came the teams. I pitched my idea, others pitched their ideas, and we just went around forming teams if you didn’t have one. At first I was afraid my heart rate idea (based on my initial code here) would just be a copy and paste app for Windows 8 until a young college student named Mansib Rahman decided to pair up with me.

We changed the game…



We started binging (googling in reality – but binging sounds WAYYYY better) potential algorithms for various medical rates using the HD Camera, IR Camera, and the new Near Mode firmware features of the Kinect. We learned a lot. We worked all night when I re-imagined and realized the potential for a medical library for use with the K4W v2 device was huge. That’s when we decided to create the Kinect Virtual Doctor windows 8 store app.  The application could potentially be placed inside your home, and you can stand in front of your mirror, while the application could tell you your breating rate- O2 Levels, pulse, blood pressure, stress mood, alertness, and many other things. But first we needed to make sure it was plausible and doable. We took the rest of the night trying to determine which algorithms we could implement in 24 hours. It turns out the heart rate, and breathing rates were the easiest, but we only ended up with re-writing my heart rate sample for Windows 8 utilizing the algorithm posted here.

One of the funniest stories of the night in my opinion was the “Pied Piper” green T’s group, at least that’s what I call them. Kudo’s by the way to sticking it out, and passing me a working audio sample – thanks to Andras (K4W MVP). Oh and before I forget – thank you Velvart Andras and James Ashley – (K4W MVP’s) for helping me out with ideas and coding.

These “Pied Piper” guys started out with the idea playing musical instruments with your body. For example if you hit your arm, it plays a drum, if you chest it changes the octave or plays another instrument. Sitting next to these guys was painful because of the terrible sounds coming from that area of the room. Envision akward musical notes with no melody constantly sounding off around 3am in the morning… Then on the other side of me was the Roosters crowing “Cocka-doodle-doo” right afterwards. I swear I felt like Noah or something. In any case the piped piper guys realized it was a little more difficult to do the playing music with your body routine. So they started to give up. A couple of them left and had some drinks – and in my opinion came back slightly wasted. That’s when the only logical thing for them to do appeared… “Let’s make a field sobriety test with the Kinect”. The app was simple – walk a straight line and repeat a tounge twister phrase. If the Kinect tracked you walking the straight line and the you said the phrase correctly, you didn’t go to jail.

This was hilarious and straight out of the HBO movie series “Sillicon Valley” and their fake Pied Piper web site mixed with the intoxication app from Google’s based “The Internship”… Now we went from 3 in the morning bad music to rooster crows to “Ricky Rubio went around the reading rainbow wrong” or something like that – PRICELESS!!!

Question: So what was your experience with the experimental firmware for the Kinect?

I will simply say this, for my application the 10cm worked better for obtaining the face and calculating the heart rate, however not everyone had the same success for their applications during the event.

Question: What was your experience with the Kinect Ripple?

I thought this was another great implementation for the Kinect. I can see Museums, Science exhibits, Schools, Convention centers and the like all utilizing this funtionality. In case you’re wondering what exactly it does… here’s a quick image:

2014-06-22 11.14.39 and video:



Question: So would you say the Kinect Hackathon was a success?

Yes, I most definitely would!