Our Research Areas
Applying advanced computational methods, we analyze the body movements of patients with neurological movement disorders. Goals of this work are to identify and to quantify disorder-specific or lesion-specific changes in movement patterns, including especially complex whole-body movements like gait or interactive tasks, and the diagnosis of preclinical symptoms of movement disorders. Our work addresses movement deficits associated with various neurological disorders, including cerebellar ataxia, Parkinson's disease, and apraxia. Another focus of this work is the investigation of motor adaptation and training effects in normal participants and during motor rehabilitation training for neurological patients.
We investigate the mechanisms of the perception of body movements, and their relationship with motor execution and social signals. Our work combines psychophysical experiments and the development of physiologically-inspired neural models in close collaboration with electrophysiologists at the HIH and the CIN. In addition, exploiting advanced methods from computer animation and Virtual Reality (VR), we investigate the perception of body movements (facial and body expressions) in social communication, and its deficits in psychiatric disorders, such as schizophrenia or autism spectrum disorders. A particular new focus is the study of intentional signals that are conveyed by bodily and facial expressions. For this purpose, we developed highly controlled stimulus sets, exploiting high-end methods from computer graphics. In addition, we develop physiologically-inspired neural models for neural circuits involved in the extraction of intent information from visual stimuli.
Brains control and recognize body and facial movements better than any existing technical system. We study the computational principles underlying recognition and motor control of body movements in biological systems and transfer relevant principles to technical applications. Application domains include computer graphics, computer vision, and humanoid robotics. In these fields, the modeling of movements of humans becomes increasingly important. In additon, we exploit such technical systems for movement synthesis and recognition in the context of biomedical applications, such as rehabilitation training.
The research leading to these results has received funding from, DFG GZ: KA 1258/15-1; CogIMon H2020 ICT-23-2014 /644727, HFSP RGP0036/2016, BMBF FKZ 01GQ1704, KONSENS-NH BW Stiftung NEU007/1
Short: DFG GZ: KA 1258/15-1; CogIMon H2020 ICT-644727, HFSP RGP0036/2016, BMBF FKZ 01GQ1704, KONSENS-NH BW Stiftung NEU007/1