At-home assessment of postural stability in parkinson's disease: a vision-based approach

Postural instability is one of the most disabling symptoms of Parkinson's Disease, with important impacts on people safety and quality of life since it increases the risk of falls and injuries. Home monitoring of changes in postural stability, as a consequence of therapies and disease progression, is highly desirable for the safety of the patient and better disease management. In this context, we present a system for the automatic evaluation of postural stability that is suitable for self-managing by people with motor impairment directly at home. The system is based on an optical RGB-Depth device, which tracks the body movements both for system's interaction, thanks to a gesture-based human-machine interface, and the automated assessment of postural stability. A set of tasks, based on standard clinical scales, has been designed for the assessment. The user controls the delivery of the tasks through the system interface. A machine learning approach is adopted, and some kinematic parameters that characterize the user's performance during each task execution are estimated and used by supervised classifiers for the automatic assessment. Data collected during experimental clinical trials were used to train the classifiers. This approach supports the compliance of the classifier assessments with respect to the clinical ones. The system prototype and the preliminary results on its accuracy in the assessment of postural stability are presented and discussed.