• State-of-the-art for biomedical simulation and its enabling technologies: haptics, tissue modeling, and simulation;
• Emerging tools for clinical diagnosis and therapy: imaging tools, data visualization and fusion techniques, and robotics;
• Intelligence networks for medical decision-making and patient care.

During the Behavioral Health & Rehabilitation session - scheduled on Friday, 9 February 2007 - Prof. Giuseppe Riva, Ph.D., and Dr. Andrea Gaggioli, Ph.D., will have a presentation entitled: "NeuroVR: An Open-Source Virtual Reality Tool for Research and Therapy".

Main goal of the presentation is to describe the main features of the system and to detail the clinical areas in which the NeuroVR software is actually tested in controlled clinical trials.

All the participants to the session will receive the NeuroVR installation CD. Below is available the abstract of the presentation.

 

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Background. Although it is undisputable that VR has come of age for clinical and research applications, the majority of them are still in the laboratory or investigation stage. One of the main burden to the diffusion of this approach is the high costs required for designing and developing the VR applications: according to recent estimates, the cost of designing a clinical VR application from scratch and testing it on clinical patients using controlled trials may fall between 150,000 and 200,000 US$. A further problem is that most virtual environments in use today are not user-friendly: expensive technical support or continual maintenance are often required.

Tools and Methods.  To address these challenges, we introduce NeuroVR, a open source virtual reality software platform that allows non-expert users to adapt the content of a pre-designed virtual environment to the needs of the clinical or experimental setting. Using the NeuroVR Editor, the user can choose the appropriate psychological stimuli/stressors from a rich database of 2D and 3D objects, and easily place them into the virtual environment. In addition to static objects, it provides the ability to overlay on the 3D scene video composited with a transparent alpha channel. The edited scene can then be visualized using either immersive or non-immersive display. The NeuroVR Editor is based on Blender, an integrated suite of 3D creation tools available on all major operating systems, released under the GNU General Public License.

Results. Thanks to the incorporation of Blender, the NeuroVR Editor allows the creation of 3D interactive content, including modeling, uv-mapping, texturing, and rendering. Blender’s further key features are: a high-quality 3D architecture enabling fast and efficient creation work-flow; free support channels (via http://www.blender3D.org ); a large user community; and a small executable size, for easy distribution. The NeuroVR Editor is built using Python scripts that create a custom graphical user interface (GUI) for Blender. The Python-based GUI allows to hide all the richness and complexity of the Blender suite, so to expose only the controls needed to customize existing scenes and to create the proper files to be viewed in the player. On the player side, NeuroVR leverages two major open-source projects in the VR field: Delta3D and OpenSceneGraph. Both are building components that the NeuroVR player integrates with ad-hoc code to handle the simulations.

 


Conclusions. It is envisioned that the 250,000 people worldwide Blender user community will contribute to extend the NeuroVR library, developing new virtual environments which can be tailored by professionals for a range of clinical and experimental applications. A future goal is to include advanced features for triggering, collecting and analysing user’s responses, i.e. session logging for after-action review, run-time control module to create dynamic events (e.g. animated objects or animals, questions from the audience, interaction with virtual humans). In addition to these features, software compatibility will be provided with a range of instruments that allow collection and analysis of behavioural data, such as eye-tracking devices and sensors for physiological monitoring.

Blender project (http://www.blender.org )
Delta3D project (http://www.delta3d.org )
OpenSceneGraph project (http://www.openscenegraph.org )