A probabilistic approach based on Random Forests to estimating similarity of human motion in the context of Parkinson's Disease

The objective characterization of human motion is required in a variety of fields including competitive sports, rehabilitation and the detection of motor deficits. 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. This paper presents a novel method to characterize the motor state of Parkinson patients using full body motion capturing data based on a combination of multiple metrics. Our approach merges various metrics with a Random Forest and uses a probabilistic formulation to compute a one-dimensional measure for the performed motion. We present an application of our approach to the problem of relating subject motion to different classes like healthy subjects and Parkinson disease patients with deep brain stimulation switched on or off. In the experimental session we show that our measure leads to high classification rates and high entropy values for real-world data. Besides, we show that our method discriminates between Parkinson's subjects (with and without stimulation) and healthy persons as good as the Unified Parkinson's Disease Rating Scale (UPDRS).