Gait Model Analysis of Parkinson's Disease Patients under Cognitive Load

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Hughes:2020:CEC,

• author = "James Alexander Hughes and Sheridan Houghten and Joseph Alexander Brown",
• booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Gait Model Analysis of {Parkinson's} Disease Patients under Cognitive Load",
• year = "2020",
• editor = "Yaochu Jin",
• month = "19-24 " # jul,
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-7281-6929-3",
• DOI = "doi:10.1109/CEC48606.2020.9185621",
• 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.",
• notes = "Also known as \cite{9185621}",

}

Genetic Programming entries for James Alexander Hughes Sheridan Houghten Joseph Alexander Brown

Citations