Human-Machine Collaborative Systems
The mediation of human activity through devices means that it is possible to gather data and develop human activity models with a scale and complexity that was heretofore not possible. For example, there are now nearly 500,000 robotic surgeries performed per year with the da Vinci surgical robot. Prior work of Professor Gregory Hager’s group at The Johns Hopkins University (JHU) and by Professor Nassir Navab’s group at TUM suggest that, in principle, it is possible to record and process each of these to assess both what was done and how well it was done. This anticipates a day when patient outcomes can be quantitatively related back to surgical performance and individualized surgical training can be optimized to improve the practice of surgery.
It is precisely the convergence of intelligent machines, humans, and quantitative performance data in medicine that we intend to focus on during Professor Hager’s stay at TUM-IAS. With the support of a TUM-IAS Hans Fischer Senior Fellowship, we propose to explore the modeling of data on human task performance as a path toward creating intelligent assistants that will enhance human abilities through individualized training and augmentation. We will focus our work on applications in surgery and related interventional care that demand high technical and cognitive skills. TUM and JHU are unique in that they are two of very few research institutions in the world that contains the concentration of expertise and resources to advance these ideas. Our long-term goal is to use the Hans Senior Fischer Fellowship as “seed funding” to develop a sustainable international collaborative center of excellence devoted to the advancement of medical technologies, and specifically medical devices and systems.
TUM-IAS funded doctoral candidate:
Christian Rupprecht, Computer Aided Medical Procedures & Augmented Reality