Interactive Media Systems, TU Vienna

A Virtual Reality Training Tool for Upper Limb Prostheses

Thesis by Michael Bressler

Supervision by Hannes Kaufmann


The technology of electromyography has become very common in being used to control hand prostheses. This technology allows the capture of controlling signals for a prosthesis by attaching electrodes to the skin, over skeletal muscles. However, before practicing with a real prosthesis, the patient has to await the healing process of the arm stump. Furthermore, the learning process can be difficult and frustrating. In this thesis, a training environment will be presented, capable of simulating the process of grasping virtual spheres with a virtual hand by using the proper amount of grip force. The virtual reality experience, is created by using the ioTracker motion tracking system, developed at the Vienna University of Technology. This system is capable of tracking the motions of the head and arm of a protagonist with six degrees of freedom (position and orientation). The created tracking data is forwarded through the OpenTracker framework into an application created with the free version of the game engine Unity3D. In this application, the tracking data is translated into a virtual 3D environment and visualized. The picture created by the virtual camera, which is mounted to the head of the protagonist, is transmitted wirelessly to a head mounted display (HMD) that the protagonist is wearing. This allows the protagonist to move around freely inside an area of 4x4 meters. As this work was done in collaboration with Otto Bock, the same technology was used for controlling the virtual hand, as it is embedded in the Michelangelo Hand prosthesis by Otto Bock. Using two electrodes, the electrical activity of skeletal muscles is measured through the skin and is further processed into controlling signals, which are then sent to the simulation. As the goal of this work was both, to create an environment for exercising and to evaluate hand prostheses, the electromyographic (EMG) controlling signals can be mapped in a flexible way to certain behaviors of the prosthesis. Furthermore, several simulation modes for creating grip force can be used, which again is indicated to the protagonist by several optical grasping aides. The virtual arm can be adjusted to best match the real circumstances. Finally, several options are provided for creating and performing various evaluation and training scenarios. Based on the final application, several of these scenarios have been created and tested with probands for evaluating the capabilities of the system.


M. Bressler: "A Virtual Reality Training Tool for Upper Limb Prostheses"; Supervisor: H. Kaufmann; Institut für Softwaretechnik und Interaktive Systeme, 2013; final examination: 11-20-2013.


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