Funding agencies: This publication was made possible by Grant Number EB007163 from NIBIB/NIH.
High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity
Article first published online: 20 MAR 2013
Copyright © 2013 Movement Disorder Society
Volume 28, Issue 8, pages 1080–1087, July 2013
How to Cite
Roy, S. H., Cole, B. T., Gilmore, L. D., De Luca, C. J., Thomas, C. A., Saint-Hilaire, M. M. and Nawab, S. H. (2013), High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity. Mov. Disord., 28: 1080–1087. doi: 10.1002/mds.25391
Relevant conflicts of interest/financial disclosures: Carlo De Luca is the president and founder of Delsys, Inc., which provided the sensor data acquisition system.
Full financial disclosures and author roles may be found in the Acknowledgments section online.
- Issue published online: 12 AUG 2013
- Article first published online: 20 MAR 2013
- Manuscript Accepted: 15 JAN 2013
- Manuscript Revised: 7 JAN 2013
- Manuscript Received: 16 MAR 2012
- Parkinson's disease quantification;
- motor disorder;
Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n = 11 patients) and tested (n = 8 patients; n = 4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities. © 2013 Movement Disorder Society