Abstract

Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson’s disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP’s motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.

Details

Title
Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks
Author
Hssayeni, Murtadha D 1 ; Jimenez-Shahed Joohi 2 ; Burack, Michelle A 3 ; Ghoraani Behnaz 1 

 Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, Boca Raton, USA (GRID:grid.255951.f) (ISNI:0000 0004 0635 0263) 
 Icahn School of Medicine at Mount Sinai, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
 University of Rochester Medical Center, Department of Neurology, Rochester, USA (GRID:grid.412750.5) (ISNI:0000 0004 1936 9166) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2511567473
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.