Longitudinal monitoring of patients with Parkinson's disease via wearable sensor technology in the home setting

• Published in: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 1552 - 1555
• Main Authors: Patel, S., Bor-rong Chen, Mancinelli, C., Paganoni, S., Shih, L., Welsh, M., Dy, J., Bonato, P.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2011
• Subjects: Acceleration; Accelerometers; Actigraphy - instrumentation; Biosensing Techniques - instrumentation; Equipment Design; Equipment Failure Analysis; Estimation; Feature extraction; Fluctuations; Home Care Services; Indexes; Longitudinal Studies; Monitoring; Monitoring, Ambulatory - instrumentation; Movement; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Reproducibility of Results; Sensitivity and Specificity; United States; Wearable sensors; United States
• ISBN: 9781424441211; 1424441218
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2011.6090452

Objective longitudinal monitoring of symptoms related motor fluctuations can provide valuable information for the clinical management of patients with Parkinson's disease. Current methods for long-term monitoring of motor fluctuations, such as patient diaries, are ineffective due to their time consuming and subjective nature. Researchers have shown that wearable sensors such as accelerometers can be used to gather objective information about a patient's motor symptoms. In this paper, we present preliminary results from our analysis on wearable sensor data gathered during longitudinal monitoring of 5 patients with PD. Our results indicate that it is possible to track longitudinal changes in motor symptoms by training a regression model based on Random Forests.