A Statistical Characterization of the Finger Tapping Test: Modeling, Estimation, and Applications
Published In
Biomedical and Health Informatics, IEEE Journal of
Document Type
Citation
Publication Date
3-2015
Subjects
Multilevel models (Statistics), Neuropsychological tests, Perceptual motor processes
Abstract
Sensory-motor performance is indicative of both cognitive and physical function. The Halstead-Reitan finger tapping test is a measure of sensory-motor speed commonly used to assess function as part of a neuropsychological evaluation. Despite the widespread use of this test, the underlying motor and cognitive processes driving tapping behavior during the test are not well characterized or understood. This lack of understanding may make clinical inferences from test results about health or disease state less accurate because important aspects of the task such as variability or fatigue are unmeasured. To overcome these limitations, we enhanced the tapper with a sensor that enables us to more fully characterize all the aspects of tapping. This modification enabled us to decompose the tapping performance into six component phases and represent each phase with a set of parameters having clear functional interpretation. This results in a set of 29 total parameters for each trial, including change in tapping over time, and trial-to-trial and tap-to-tap variability. These parameters can be used to more precisely link different aspects of cognition or motor function to tapping behavior. We demonstrate the benefits of this new instrument with a simple hypothesis-driven trial comparing single and dual-task tapping.
Rights
© Copyright 2016 IEEE - All rights reserved
Locate the Document
DOI
10.1109/JBHI.2014.2384911
Persistent Identifier
http://archives.pdx.edu/ds/psu/16662
Citation Details
Austin, Daniel, James McNames, Krystal Klein, Holly Jimison, and Misha Pavel. "A Statistical Characterization of the Finger Tapping Test: Modeling, Estimation, and Applications." Biomedical and Health Informatics, IEEE Journal of 19, no. 2 (2015): 501-507.
Description
Originally appeared in IEEE Journal of Biomedical and Health Informatics, vol. 19, issue 2, pages 501-507.