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Title: Posterior Probability Profiles for the Automated Assessment of the Recovery of Stroke Patients
Authors: Van Dijck, Gert
Van Vaerenbergh, Jozef
Van Hulle, Marc #
Issue Date: Jul-2007
Publisher: AAAI Press
Host Document: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence edition:19 pages:347-353
Conference: Twenty-Second AAAI Conference on Artificial Intelligence edition:22 location:Vancouver, British Columbia, Canada date:22-26 July 2007
Abstract: Assessing recovery from stroke has been so far a time
consuming procedure in which highly trained clinicians are
required. This paper proposes a mechatronic platform which
measures low forces and torques exerted by subjects. Class
posterior probabilities are used as a quantitative and
statistically sound tool to assess motor recovery from these
force and torque measurements. The performance of the
patients is expressed in terms of the posterior probability to belong to the class of normal subjects. The mechatronic
platform together with the class posterior probabilities
enables to automate motor recovery assessment without the
need for highly trained clinicians. It is shown that the class posterior probability profiles are highly correlated, r ≈ 0.8, with the well-established Fugl-Meyer scale assessment in motor recovery. These results have been obtained through careful feature subset selection procedures in order to prune the large feature set being generated. The overall approach is general and can be applied to many other health monitoring systems where different categories (diseased vs. healthy) can be identified.
ISBN: 978-1-57735-323-2
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Research Group Neurophysiology
# (joint) last author

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