An online system for tracking the performance of Parkinson's patients

An objective performance measure for movement tasks is widely regarded as having utmost relevance for the therapy of movement disorders. Existing systems typically rely on human experts, which is known to produce substantial inter- and intra-rater variability. Present solutions are either based on simple features or invasive motion capture techniques. They typically work on a specific motion task only and fail to generalize to other tasks. In addition, they often require manual offline pre- and post-processing. In this paper we present a novel approach to compute a continuous and objective performance measure online during a patient session, without tedious and time-consuming pre- or post-processing steps. Our approach is able to generalize between different motion capture devices and different motion tasks. It runs on live motion data extracted with a non-invasive marker-less off-the-shelf vision-based tracking system as well as on data extracted from an inertial measurement unit suit. In the experiments we show that our approach is competitive with an offline approach as well as with the Unified Parkinson's Disease Rating Scale. Our approach is robust with respect to motion execution speed and it outperforms the offline approach regarding movement task generalization. We show promising results to track the current state of a Parkinson's subject online during a therapy session.