A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson’s Disease via Wearable Sensors

Parkinson’s disease (PD) is the second most common neurodegenerative disorder and the heterogeneity of early PD leads to inter-rater and intra-rater variability in observation-based clinical assessment. Thus, objective monitoring of PD-induced motor abnormalities has attracted significant attention to manage disease progression. Here, we proposed a hierarchical architecture to reliably detect abnormal characteristics and comprehensively quantify the multi-symptom severity in patients with PD. A novel wearable device was designed to measure motor features in fifteen PD patients and fifteen age-matched healthy subjects, while performing five types of motor tasks. The abnormality classes of multi-modal measurements were recognized by hidden Markov models (HMMs) in the first layer of the proposed architecture, aiming at motivating the evaluation of specific motor manifestations. Subsequently, in the second layer, three single-symptom models differentiated PD motor characteristics from normal motion patterns and quantified the severity of cardinal PD symptoms in parallel. In order to further analyze the disease status, the multi-level severity quantification was fused in the third layer, where machine learning algorithms were adopted to develop a multi-symptom severity score. Experimental results demonstrated that the quantification of three cardinal symptoms were highly accurate to distinguish PD patients from healthy controls. Furthermore, strong correlations were observed between the Unified Parkinson’s Disease Rating Scale (UPDRS) scores and the predicted sub-scores for tremor (R=0.75, P=1.40e-3), bradykinesia (R=0.71, P=2.80e-3) and coordination impairments (R=0.69, P=4.20e-3), and the correlation coefficient can be enhanced to 0.88 (P=1.26e-5) based on the fusion schemes. In conclusion, the proposed assessment architecture holds great promise to push forward the in-home monitoring of clinical manifestations, thus enabling the self-assessment of disease progression.