Models of Parkinson's Disease Patient Gait
| dc.contributor.author | Hughes, James Alexander | |
| dc.contributor.author | Houghten, Sheridan | |
| dc.contributor.author | Brown, Joseph Alexander | |
| dc.date.accessioned | 2022-11-03T18:00:31Z | |
| dc.date.available | 2022-11-03T18:00:31Z | |
| dc.date.issued | 2020-11 | |
| dc.identifier.citation | J. A. Hughes, S. Houghten and J. A. Brown, "Models of Parkinson's Disease Patient Gait," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 11, pp. 3103-3110, Nov. 2020, doi: 10.1109/JBHI.2019.2961808. | en_US |
| dc.identifier.issn | 2168-2194 | |
| dc.identifier.uri | http://hdl.handle.net/10464/16887 | |
| dc.description.abstract | Parkinson's Disease is a disorder with diagnostic symptoms that include a change to a walking gait. The disease is problematic to diagnose. An objective method of monitoring the gait of a patient is required to ensure the effectiveness of diagnosis and treatments. We examine the suitability of Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) Models compared to Symbolic Regression (SR) using genetic programming that was demonstrated to be successful in previous works on gait. The XGBoost and ANN models are found to out-perform SR, but the SR model is more human explainable. | en_US |
| dc.description.sponsorship | Natural Sciences and Engineering Research Council of Canada | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights.uri | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html | |
| dc.subject | Health Information Management | en_US |
| dc.subject | Electrical and Electronic Engineering | en_US |
| dc.subject | Computer Science Applications | en_US |
| dc.subject | Biotechnology | en_US |
| dc.subject | Parkinson's Disease | en_US |
| dc.title | Models of Parkinson's Disease Patient Gait | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.1109/jbhi.2019.2961808 | |
| dc.identifier.eissn | 2168-2208 | |
| dc.source.journaltitle | IEEE Journal of Biomedical and Health Informatics | |
| dc.source.volume | 24 | |
| dc.source.issue | 11 | |
| dc.source.beginpage | 3103 | |
| dc.source.endpage | 3110 | |
| refterms.dateFOA | 2022-11-03T18:00:31Z |
