Home-based automated assessment of upper limb motor function in Parkinson's Disease

This work presents a non-invasive low-cost system suitable for the at home assessmentof the neurological impairment of patients affected by Parkinson's Disease(PD). The assessment is automatic and it is based on the accurate tracking of hands and fingers movements of the patient during the execution of standard upper limb tasks specified by the Unified Parkinson's Disease Rating Scale (UPDRS). The system is based on a human computer interface made by light gloves and an optical tracking RGB-Depth device. The accurate tracking and characterization of hands and fingers movements allows both the automatic and objective assessment of UPDRS tasks and the gesture-based management of the system, making it suitable for motor impaired users, as are PD patients. The assessment of UPDRS tasks is performed by a machine learning approach, whichusesthe kinematic parameters that characterize the patient movements,as input to trained classifiers,with the aim ofautomatically rating the UPDRS scores of the performance. The classifiers have been trained by an experimental campaign,where cohortsof PD patients were contemporary assessed by a neurologist and the system. Results on the accuracy of the system assessments, as compared to the neurologist's ones, are given, along with preliminary results on monitoring experiments at home. Details about the user interfaces of the system, specifically designed for home-monitoring, are provided. The clinimetric properties of the system and its usability have been evaluatedand reported. The results confirm that the system is suitable for the remote monitoring of PD patients at-home.