Functional motor impairment due to Parkinson's disease and other movement disorders are currently assessed with visual rating scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). These methods rely on the subjective judgment of a rater to assign scores representing the extent of impairment while subjects perform prescribed activities. We describe a new model-based framework that uses statistical video processing to automatically track movement during prescribed activities. This approach has many advantages over traditional clinical rating scales. It can completely characterize movement during prescribed tasks over time objectively and precisely using hardware that is inexpensive and readily available. We demonstrate the potential of this framework with a simple statistical model applied to a paced finger tapping test. This technology could be deployed in a natural home environment for frequent assessments. This technology could ultimately improve both clinical practice and clinical trials.
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