Descriptive Symbolic Models of Gaits from Parkinson's Disease Patients
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Hughes:2019:CIBCB,
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author = "James Alexander Hughes and Sheridan Houghten and
Joseph Alexander Brown",
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title = "Descriptive Symbolic Models of Gaits from
{Parkinson's} Disease Patients",
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booktitle = "2019 IEEE Conference on Computational Intelligence in
Bioinformatics and Computational Biology (CIBCB)",
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year = "2019",
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abstract = "Parkinson's disease (PD) is a degenerative disorder of
the central nervous system that has many debilitating
symptoms which affect the patient's motor system and
can cause significant changes in their gait. By using
genetic programming, we aim to develop descriptive
symbolic nonlinear models of PD patient gait from time
series data recorded from pressure sensors under
subjects' feet. When compared to popular types of
linear regression (OLS and LASSO), the nonlinear models
fit their data better and generalize to unseen data
significantly better. It was found that models
developed for healthy control subjects generalized to
other control subjects well, however the models trained
on subjects with PD did not generalize well to other PD
patients, which complicates the issue of being able to
detect the progression of the disease. It is suspected
that health care professionals can have difficulty
classifying PD due to a lack of accurate data from
patient reports; having individually trained models for
active monitoring of patients would help in effectively
diagnosing PD.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CIBCB.2019.8791459",
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month = jul,
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notes = "Also known as \cite{8791459}",
- }
Genetic Programming entries for
James Alexander Hughes
Sheridan Houghten
Joseph Alexander Brown
Citations