Tobias Schubert

CaliMotion - Detection of fine motor disabilities of stroke patients

The objective characterization of human motion is required in a variety of fields including competitive sports, rehabilitation and the detection of motor deficits. In this project we are faced with the motion analysis of Parkinson patients. Deep brain stimulation is widely used as a treatment for advanced Parkinson's disease, but requires stimulation parameter adjustment from time to time. The goal of this project is to automatically adjust the stimulation parameter based on motion capture data. Nowadays, typically human experts evaluate the motor behavior. These evaluations are based on their individual experience which leads to a low inter- and intra-expert reliability. Standardized tests improve on the reliability but are still prone to subjective ratings and require human expert knowledge. We develop a method to characterize the motor state of Parkinson patients using full body motion capturing data. Our approach merges various metrics with a Random Forest and uses a probabilistic formulation to compute a one-dimensional measure for the performed motion. Overall we obtained an objective performance measure competitive with the Unified Parkinson’s Disease Rating Scale (UPDRS).