Title :
Objective measure of upper extremity motor impairment in Parkinson´s disease with inertial sensors
Author :
Hoffman, Jeffrey D. ; McNames, James
Author_Institution :
Dept. of Electr. & Comput. Eng., Portland State Univ., Portland, OR, USA
fDate :
Aug. 30 2011-Sept. 3 2011
Abstract :
Functional motor impairment caused by Parkinson´s disease and other movement disorders is currently measured with rating scales such as the Unified Parkinson´s Disease Rating Scale (UPDRS). These are typically comprised of a series of simple tasks that are visually scored by a trained rater. We developed a method to objectively quantify three upper extremity motor tasks directly with a wearable inertial sensor. Specifically, we used triaxial gyroscopes and adaptive filters to quantify how predictable and regular the signals were. We found that simply using the normalized mean squared error (NMSE) as a test statistic permitted us to distinguish between subjects with and without Parkinson´s disease who were matched for age, height, and weight. A forward linear predictor based on the Kalman filter was able to attain areas under the curve (AUC) in receiver operator characteristic (ROC) curves in the range of 0.76 to 0.83. Further studies and development are warranted. This technology has the potential to more accurately measure the motor signs of Parkinson´s disease. This may reduce statistical bias and variability of rating scales, which could lead to trials with fewer subjects, less cost, and shorter duration.
Keywords :
Kalman filters; adaptive filters; biomechanics; biomedical measurement; force sensors; gyroscopes; mean square error methods; medical disorders; medical signal processing; motion measurement; patient diagnosis; sensitivity analysis; Kalman filter; ROC curve; adaptive filter; forward linear predictor; functional motor impairment; movement disorder; normalized mean squared error; receiver operator characteristic curve; triaxial gyroscope; unified Parkinson´s disease rating scale; upper extremity motor impairment; wearable inertial sensor; Adaptation models; Biomedical measurements; Diseases; Kalman filters; Sensors; Thumb; Arm; Humans; Parkinson Disease; Signal Processing, Computer-Assisted;
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2011.6091086