4 daenet was established in 1998 Gold Certified Partner WPC Partner of the Year 2005, 2010 Regular “Inner Circle” / “Core Partner” with Microsoft Featured in joint Microsoft-Gartner newsletter and in Gartner Magic Quadrant More than 15 years experience in the “Integration” and architecture space V-TSP Integration Consulting, Coaching, Implementation, Outsourcing Scrum Master, Trainer, Coach Passionate about Innovation Community, User Group, Presentations Geos: Western Europe (Germany-HQ) Americas (USA) South-eastern Europe

A new kind of Camera, that can see in 3D A traditional video camera A 4-element microphone array A powerful SDK which can: Recognize that a person is in front of the sensor, and what position their body is in A software controllable directional microphone, and speech recognition technology And lots more

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Power Light RGB Camera IR Emitters Microphone Array

Color (1920x1080) at 30 FPS (15 FPS low light)Infrared FPSDepth FPSBody-index pixel-mask at FPSPerson detection and joint tracking, up to 6 FPSFloor plane detectionTransformation between coordinate systemsEvent driven and polling-based APIs J. Sell and P. O’Connor, “The Xbox One System on a Chip and Kinect Sensor,” IEEE Micro, 2014

Audio Color Infrared Depth BodyIndex Body

Engagement Targeting Press Panning/Zoom

Engagement for Global Commands “Xbox” Engagement for App Commands “Xbox Select” Xbox One w/ Kinect Windows 8.0 or 8.1 w/ Kinect N/A “Kinect” or … “In Experience” Commands App- defined grammar

Infrared 13 MB/s Depth 13 MB/s BodyFrameBodyIndex Color 120 MB/s Audio 32 KB/s Legend Record/Play Record Only

V2.0

Heuristic Machine Learning (ML) with G.B.

Floor normal Upper leg vector Angle between Floor normal and bone Spine less than 45 ˚ from floor normal Upper legs less than 45 ˚ from floor normal Standing Spine less than 45 ˚ from floor normal Upper legs more than 45 ˚ from floor normal Sitting Spine more than 45 ˚ from floor normal Upper legs more than 45 ˚ from floor normal Lying

Rinse and repeat Your Application Record example gestures using Kinect Studio Tag gestures using Gesture Builder Build and analyze gestures using Gesture Builder Preview gestures in Gesture Builder Call gesture detector for tracked bodies

Fusion FaceHD Face

Integrate functionalities in controlling applicationVisualize work per joint for linear and angular motionDisplay velocity and acceleration vectorsDisplay energy expenditure rate and total energy expenditureDisplay activity levelDisplay fall-detection stateDisplay detected postureCenter of massEnable/Disable “Jitter” filtersEnables logging

Sensors of the armband: Accelerometer Heat flux Near-Body Ambient temperature Galvanic Skin Response Sensor Skin Temperature  Perform physical exercise with 2 different models of a BodyMedia SenseWear armband worn at the same time  Test subject: Male 43 yrs 178 cm 72.6 kg Non-smoker right handed Idea: Compare Kinect-based EE estimation with third-party EE estimation method

Run in place Pause Intense Squats And Jumps Lighter but mixed intensity running jumps/squats Warmup running in place Cooldown

Andreas Erben +1 (206) (179) HackAndi.com

2 Create Reconstruction Pass in a set of parameters defining the environment and processing type Outputs the reconstruction 3 Other considerations Can enable color to be lifted from the scan Experimental pose finders (6DoF trackers) are also included to play with 1 Process Function Provides “six degrees of freedom” transform Can be rendered and use to begin the Fusion Fusion | Face | HD Face

Detection Alignment Orientation Expressions Outputs a bounding box around the face Can be visualized in color or IR Identifies 5 facial landmarks on the face Operation performed in color or IR Returns quaternion of the head joint with respect to the sensor Quaternion prevents gimbal lock Provides classifiers for happy, left / right eye open, engagement, mouth open and mouth moving Alignment and detection performs the operation in IR, but are converted to color using the coordinate mapper for output Fusion | Face | HD Face

Face Model Builder Once of cluster of classes that implement the capture interaction Interactive API Provides collections status and evaluation of frames you’ve collected 1 Face Model Set of 94 shape units, scale, hair color, and skin color All adjustments are set against an “average face” mesh that is deformed by the shape units acquired during the face capture 2 Use the Mesh The mesh is analogous to most animation meshes for easy application to other rigs Mesh topology is the same for all faces represented in a standard way (numTriangles, numVertices, etc.) 3 UX is a non-trivial consideration in a good implementation of the Face Model Builder. Fusion | Face | HD Face

Interval to fall down Sum of max downwards force per joint over threshold Important joint (spine, shoulders, hips) close to floor Interval to recover Important joint locations above threshold height Interval to fall down Head position moves downward more than threshold value Base of spine and head close to floor Interval to recover Head and spine not in recovery threshold distance