Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor

Parkinson's Disease (PD) is a degenerative disease of the central nervous system with a profound effect on the motor system. Symptoms include slowness of movement, rigidity of motion and in some patients, tremor. The severity of the disease is quantified using the Unified Parkinson Disease Rating Scale (UPDRS) which is a subjective scale performed and scored by physicians. In this work, we present an automated, objective quantitative analysis of four UPDRS motor examinations of Hand Movement and Finger Taps. For this purpose, a non-invasive system for recording and analysis of fine motor skills of hands was developed. The system is based on a simple low-cost depth acquisition sensor, similar to the second generation of Microsoft's Kinect sensor, and novel recursive self-correcting hand tracking algorithm. The system allows patients to perform test tasks in a natural and unhindered manner. The evaluation of the system was carried out on PD patients and controls. Machine Learning based classification was performed on the acquired data, followed by a decision making scheme.