Advanced Analysis of Wearable Sensor Data to Adjust Medication Intake in Patients with Parkinson's Disease

Abstract

The objective of this pilot work is to identify characteristics and measure severity of motor fluctuations in patients with Parkinson's disease (PD) based on wearable sensor data. Improved methods of assessing longitudinal changes in PD would enable optimization of treatment and maximization of patient function. We hypothesize that motor fluctuations accompanying late-stage PD present with predictable features of accelerometer signals recorded during execution of standardized motor tasks. Six patients (age 46-75) with diagnosis of idiopathic PD and levodopa-related motor fluctuations were studied. Subjects performed motor tasks in a "practically-defined OFF" state, and then at 30 minute intervals after medication intake. At each interval, data from 8 uniaxial accelerometers on the upper and lower limbs were recorded continuously, and subjects were videotaped. Features representing motion characteristics such as intensity, rate, regularity, and coordination were derived from the sensor data, and clinical scores were assigned for each task by review of the videotapes. Cluster analysis was performed on feature sets that were expected to reflect severity of parkinsonian symptoms (e.g. bradykinesia) and motor complications (e.g. dyskinesias). Two-dimensional data projections revealed clusters corresponding to the degree of dyskinesia and bradykinesia indicated by clinical scores. These preliminary results support our hypothesis that wearable sensors are sensitive to changing patterns of movement throughout the medication intake cycle, and that automated recognition of motor states using these recordings is feasible