Gait Model Analysis of Parkinson's Disease Patients under Cognitive Load
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Hughes:2020:CEC,
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author = "James Alexander Hughes and Sheridan Houghten and
Joseph Alexander Brown",
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booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Gait Model Analysis of {Parkinson's} Disease Patients
under Cognitive Load",
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year = "2020",
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editor = "Yaochu Jin",
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month = "19-24 " # jul,
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185621",
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abstract = "Parkinson's disease is a neurodegenerative disease
that affects close to 10 million with various symptoms
including tremors and changes in gait. Observing
differences or changes in an individual's
manifestations of gait may provide a mechanism to
identify Parkinson's disease and understand specific
changes. In this study, time series data from both
Control subjects and Parkinson's disease patients was
modeled with symbolic regression and extreme gradient
boosting. Model effectiveness was analyzed along with
the differences in the models between modeling
strategies, between Control subjects and Parkinson's
disease patients, and between normal walking and
walking while under a cognitive load. Both modelling
strategies were found to effective. The symbolic
regression models were more easily interpreted, while
extreme gradient boosting had higher overall accuracy.
Interpretation of the models identified certain
characteristics that distinguished Control subjects
from Parkinson's disease patients and normal walking
conditions from walking while under a cognitive load.",
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notes = "Also known as \cite{9185621}",
- }
Genetic Programming entries for
James Alexander Hughes
Sheridan Houghten
Joseph Alexander Brown
Citations