Cognitive Science; Human-computer Interaction; Neurosciences
- Christian Doppler Laboratory for Reconstruction of Extremity Function
My main research adsresses various areas of medical informatics. We try to improve man-machine interfaces in order to provide upper extremity amputees with a more intuitive and easy-to-learn myoelectric prosthesis control. This goes hand in hand with neuromuscular rehabilitation training. To keep patients motivated throughout the rehabilitation process, they can train their muscle strength and precision in specifically designed virtual games, which they can control the same way as their actual prosthesis. Another aspect of upper pimb amputees is the change in body image perception. With the use of digital 3D human avatars we can locate pain and cramp hotspots and visualize the patient's phantom limb appearance and position.
Techniques, methods & infrastructure
- EMG for prosthesis control and HCI
- Machine Learning pattern recognition and regression for simultaneous control of motion and transfer learning for fast recalibration of the prosthesis after electrode shift
- Matlab and C# for interface implementations
- Game Development with Unity Game Engine
- Android App Development
As a CD Laboratory we work closely together with Ottobock Healthcare GmbH.
- Prahm, C. et al., 2017. Increasing motivation, effort and performance through game-based rehabilitation for upper limb myoelectric prosthesis control. 2017 International Conference on Virtual Rehabilitation (ICVR). Available at: http://dx.doi.org/10.1109/ICVR.2017.8007517.
- Prahm, C. et al., 2016. Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis After Electrode Shift. Biosystems & Biorobotics, pp.153-157. Available at: http://dx.doi.org/10.1007/978-3-319-46669-9_28.
- Prahm, C. et al., 2017. Game-Based Rehabilitation for Myoelectric Prosthesis Control. JMIR Serious Games, 5(1), p.e3. Available at: http://dx.doi.org/10.2196/games.6026.
- Prahm, C. et al., 2016. Recommendations for Games to Increase Patient Motivation During Upper Limb Amputee Rehabilitation. Biosystems & Biorobotics, pp.1157-1161. Available at: http://dx.doi.org/10.1007/978-3-319-46669-9_188.
- Prahm, C. et al., 2016. Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control. BMC Research Notes, 9(1). Available at: http://dx.doi.org/10.1186/s13104-016-2232-y.