1. Proceedings
  2. I3M
  3. 2018
  4. VARE
  5. 10.46354/i3m.2018.vare.024

Exploring dimensionality reduction effects in mixed reality for analyzing tinnitus patient data

  • a Burkhard Hoppenstedt  
  • bManfred Reichert  
  • cChristian Schneider  
  • d Klaus Kammerer  
  • e Winfried Schlee  
  • f Thomas Probst  
  • g Berthold Langguth  
  • h Rüdiger Pryss  
  • abdh Institute of Databases and Information System, Ulm University
  • cegDepartment of Psychiatry and Psychotherapy, University of Regensburg
  • f Department for Psychotherapy and Biopsychosocial Health, Danube University, Krems
Cite as
B. Hoppenstedt, M. Reichert, C. Schneider, K. Kammerer, W. Schlee, T. Probst, B. Langguth, R. Pryss (2018). Exploring dimensionality reduction effects in mixed reality for analyzing tinnitus patient data. Proceedings of the 4th International Conference of The Virtual And Augmented Reality In Education (VARE 2018), pp. 163-170. DOI: https://doi.org/10.46354/i3m.2018.vare.024

Abstract

In the context of big data analytics, gaining insights into high-dimensional data sets can be properly achieved, inter alia, by the use of visual analytics. Current developments in the field of immersive analytics, mainly driven by the improvements of smart glasses and virtual reality headsets, are one enabler to enhance user-friendly and interactive ways for data analytics Along this trend, more and more fields in the medical domain crave for this type of technology to analyze medical data in a new way. In this work, a mixed-reality prototype is presented that shall help tinnitus researchers and clinicians to analyze patient data more efficiently. In particular, the prototype simplifies the analysis on a high-dimensional real-world tinnitus patient data set by the use of dimensionality reduction effects. The latter is represented by resulting clusters, which are identified through the density of particles, while information loss is denoted as the remaining covered variance. Technically, the graphical interface of the prototype includes a correlation coefficient graph, a plot for the information loss, and a 3D particle system. Furthermore, the prototype provides a voice recognition feature to select or deselect relevant data variables by its users. Moreover, based on a machine learning library, the prototype aims at reducing the computational resources on the used smart glasses. Finally, in practical sessions, we demonstrated the prototype to clinicians and they reported that such a tool may be very helpful to analyze patient data on one hand. On the other, such system is welcome to educate unexperienced clinicians in a better way. Altogether, the presented tool may constitute a promising direction for the medical as well as other domains.

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