Author(s): Pereira, Clayton R. ; Pereira, Danillo R. ; Silva, Francisco A. Da ; Hook, Christian ; Weber, Silke A.T. [UNESP] ; Pereira, Luis A.M. [UNESP] ; Papa, Joao P. [UNESP]
Date: 2018
Persistent ID: http://hdl.handle.net/11449/177541
Origin: Oasisbr
Subject(s): machine learning; movement disorders; Parkinson's disease; machine learning; machine learning; movement disorders; movement disorders; Parkinson's disease; Parkinson's disease
Made available in DSpace on 2018-12-11T17:25:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-01-01
Parkinson's disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individual's handwritten trace called Mean Relative Tremor is also presented.
Federal University, UFSCAR
Western University, UNOEST
Ostbayerische Technische Hochschule
São Paulo State University, UNESP
São Paulo State University, UNESP
