Parkinson's disease medication state management using data fusion of wearable sensors

Parkinson disease (PD) cure remains one of the greatest challenges in chronic neurological disorder therapy, motivating efforts to provide actionable information to guide self-managed therapy adjustments. In this paper, we develop a data fusion approach to combine multi-dimensional data from body-worn inertial sensors to automatically identify different medication states of patients with PD. The proposed approach employs several signal processing algorithms including time-frequency analysis and tensor decomposition to extract features that can represent spectral, temporal, and spatial behavior of body motion data. A Support Vector Machines (SVM) classifier is trained and tested using a data set of 12 patients, which resulted in an overall average accuracy of 78%.