We are invited to give a talk at the 12th BSZ-Kolloquium at the Stuttgart Media University (HdM). The talk is scheduled for September 27th, 2011. Roman Rädle will present the vision of Blended Interaction with help of Blended Library as use case. Further, the progress of the project Blended Library as well as our “Living Lab” will be presented. The talk concludes with an outlook to future work.

EDIT September 29th, 2011: We thank the BSZ Baden-Württemberg for the invitation. About 300 registered people including MWK Baden-Württemberg, library directors (university, regional, and public libraries), and industry and commerce were listening to the talk. It was a pleasure to present and report about the progress of our project Blended Library.

About Facet-Streams:

Facet-Streams is a prototype application for the Microsoft Surface tabletop computer. The application enables a group of users (e.g. a family) to simultaneously search hotels in the hotel data of HolidayCheck and to agree on a holiday destination. Users can use multitouch and glass tokens to enter their individual preferences into the application.

“Facet-Streams” was designed and implemented by Hans-Christian Jetter, Jens Gerken und Michael Zöllner at the Human-Computer Interaction Group of Prof. Dr. Harald Reiterer at the University of Konstanz in cooperation with Natasa Milic-Frayling of the Integrated Systems Group of Microsoft Research Cambridge.

Quote from the mail of the “Best of CHI” committee:

Congratulations!  On behalf of SIGCHI and the CHI 2011 Committee, we would like to formally notify you that your accepted Paper has received an “Honorable Mention” award.  The CHI 2011 Papers and Notes Committee selected only the top 5% of Papers and Notes submissions to receive the “Honorable Mention” designation.  Quite an achievement. [...] Congratulations to you all and thank you for advancing the field of Human-Computer Interaction! [...]

Now that’s a really nice way to say thank you!

In the light of this award, we are happy that our submissions to CHI 2011 were even more successful than we already knew…

NAVI (Navigational Aids for the Visually Impaired) is a student project aiming at improving indoor navigation for visually impaired by leveraging the Microsoft Kinect camera, a vibrotactile waistbelt and markers from the AR-Toolkit.While the “white cane” is a good tool to improve navigation for visually impaired, it has certain drawbacks such as a small radius or that it just detects objects that are on the ground (during typical use).We wanted to augment the visually impaired person’s impression of a room or building by providing vibro-tactile feedback that reproduces the room’s layout.

The vibrotactile waistbelt

For this, depth information from the Kinect is mapped by our software onto three pairs of Arduino LilyPad vibration motors located at the left, center and right of the waist. These pairs of vibration motors are hot glued into a fabric waist belt and connected to an Arduino 2009 board. To increase the impact of the vibration motor they were put into the cap of a plastic bottle. The Arduino in the waist belt is connected via usb to a laptop that was mounted onto a special backpack-construction, which has holes for cables and fan.

The special backpack-construction to hold the laptop

To support point-to-point navigation usually a seeing-eye dog is used. This dog however must be trained for certain routes, costs a lot of money and gets tired soon. In certain research projects GPS is used to provide this point-to-point navigation, however GPS is not applicable for indoor scenarios.

The kinect camera mounted on a socket built with Sugru (http://sugru.com) and fixed with duct tape

We wanted to utilize the rgb camera of the Kinect, so we put several markers of the AR-Toolkit on the walls and doors of our building thereby modeling a certain route from one room to another. The markers are tracked continuously all along the way and our software provides synthesized auditory navigation instructions for the person. These navigation instructions vary based on the distance of the person to the marker (which we get from Kinect’s depth camera). So for example, if you walk towards a door the output will be “Door ahead in 3”, “2”, “1”, “pull the door” where each part of the information depends on the distance to the marker on the door.

The battery pack to power our mobile kinect camera

The software is written with C#/.NET. We used the MangedOpenNI (https://github.com/kobush/ManagedOpenNI) wrapper for the Kinect and the managed wrapper of the ARToolkitPlus (http://code.google.com/p/comp134artd) for marker tracking. Voice synthesis is done using Microsoft’s Speech API (http://msdn.microsoft.com/en-us/speech/default). All input streams are glued together using Reactive Extensions for .NET (http://msdn.microsoft.com/en-us/devlabs/ee794896).

The debug view of the software helped us tune the parameters for depth processing