Comparison of user models based on GMM-UBM and i-vectors for speech, handwriting, and gait assessment of Parkinson's disease patients

no code implementations • 13 Feb 2020 • J. C. Vasquez-Correa, T. Bocklet, J. R. Orozco-Arroyave, E. Nöth

Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments.

Speaker Verification

Analysis and Evaluation of Handwriting in Patients with Parkinson's Disease Using kinematic, Geometrical, and Non-linear Features

no code implementations • 13 Feb 2020 • C. D. Rios-Urrego, J. C. Vásquez-Correa, J. F. Vargas-Bonilla, E. Nöth, F. Lopera, J. R. Orozco-Arroyave

Results: Accuracies of up to $93. 1\%$ were obtained in the classification of patients and healthy control subjects.

General Classification

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages

no code implementations • 11 Feb 2020 • J. C. Vásquez-Correa, T. Arias-Vergara, C. D. Rios-Urrego, M. Schuster, J. Rusz, J. R. Orozco-Arroyave, E. Nöth

This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech.

Specificity Transfer Learning

Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease

no code implementations • 19 Mar 2019 • R. Castrillon, A. Acien, J. R. Orozco-Arroyave, A. Morales, J. F. Vargas, R. Vera-Rodrıguez, J. Fierrez, J. Ortega-Garcia, A. Villegas

Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, knearest neighbors, and a multilayer perceptron.