More specifically, we visualize tinnitus
records as a hologram, which is
augmented by the real world. The
developed prototype particularly tackles
three challenges in the context of
analyzing the TrackYourTinnitus data set
visually. First, the detection of cor-
relations between dimensions is simp-
lified by highlighting the relations bet-
ween the diagram axes and visually
displaying the correlation coefficient.
Second, an outlier detection method
reveals striking data points and, third,
a clustering approach allows for the
recognition of related data points.
Finally, the performance of the
prototype can be controlled by
subsampling the data set in order to
receive different types of resolutions.
Therefore, the prototype is able to
handle large data sets.
Exploring Patient Data in Mixed Reality
Contact and HOLOVIEW Project Information
Institute of Databases and Information Systems
Ulm University, Germany
M. Sc. Burkhard Hoppenstedt
burkhard.hoppenstedt@uni-ulm.de
Phone +497315024136
Fax +497315024134
Dr. Rüdiger Pryss
ruediger.prys[email protected]
Phone +497315024136
Fax +497315024134
Prof. Dr. Manfred Reichert
manfred.reichert@uni-ulm.de
Phone +497315024135
Fax +497315024134
Over the last years, the
TrackYourTinnitus project collected
data from worldwide tinnitus patients
using smart mobile devices. The
gathered data set, in turn, is high-
dimensional and it is therefore challen-
ging to visualize it for analyzing pur-
poses. To remedy this drawback, a 3D
approach that applies the Microsoft
HoloLens is proposed.
All features of the tinnitus data set can be
displayed in a 3d cube
HOLOVIEW
Normalized Cube Sketch
http://2018.tri-conf.org/
Presented at TRI / TINNET Conference 2018
Burkhard Hoppenstedt [1], Christian Schneider [2], Rüdiger Pryss [1], Winfried Schlee[2], Thomas Probst [3]
Patrick Neff [2], Jorge Simoes [2], Alexander Treß [4] & Manfred Reichert [1]
[1] Ulm University, [2] University of Regensburg, [3] Danube University Krems, [4] ATR Software
Mixed Reality
The hologram can be explored in any room
and it keeps its position
Transform Axes
Each axis can be exchanged using
speech recognition
