Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/61984

TitleAnalysis of postural kinetics data using Artificial Neural Networks in Alzheimer's Disease
Author(s)Ferreira, Jaime
Gago, Miguel F.
Fernandes, Vitor
Silva, Hélder David Malheiro
Sousa, Nuno
Rocha, Luís Alexandre Machado
Bicho, Estela
KeywordsAlzheimer's disease
Artificial Neural Network
Inertia Measurement Units
Postural Stability
Issue date2014
PublisherIEEE
Abstract(s)Inertial measurement Units (IMU) (accelerometers and gyroscopes), placed in strategic parts of the human body, are a growing field on kinetic posture and imbalance study in Alzheimer’s Disease (AD). On the other hand, Artificial Neural Network (ANN) are a powerful statistical tool used on pattern recognition on big data such as IMU kinetic records. Still, on ANN research, issues like the best number of hidden layers and the best number of neurons in each hidden layer remain open. In our study we developed a software tool of Multilayer Perceptrons ANN analysis (Back Propagation and Scale Gradient Conjugate training algorithms) that automatically tests different configurations for the ANNs on the diagnosis of Alzheimer’s disease. Analysis was performed primarily on all 159 variables, biometrics and IMU records of 21 AD patients and 21 healthy subjects exposed to seven different tasks with increasing postural stress, and posteriorly on selected data derived from MannWhitney analysis. Multilayer Perceptron ANN reached a performance of 95% on the diagnosis of AD, proving to be a potential useful clinical tool.
TypeConference paper
URIhttp://hdl.handle.net/1822/61984
ISBN978-1-4799-2920-7
DOI10.1109/MeMeA.2014.6860040
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:ICVS - Livros e Capítulos de Livros

Files in This Item:
File Description SizeFormat 
10.1109_MeMeA.2014.6860040.pdf
  Restricted access
2,5 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID