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A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements

  • 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.

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Metadaten
Author:Clayton R. PereiraORCiD, Danilo R. PereiraORCiD, Francisco A. da Silva, Christian Hook, Silke A. T. WeberORCiD, Luis A. M. Pereira, João Paulo PapaORCiD
DOI:https://doi.org/10.1109/cbms.2015.34
ISBN:978-1-4673-6775-2
ISSN:2372-9198
Parent Title (English):2015 IEEE 28th International Symposium on Computer-Based Medical Systems, 22.-25.06.2015, Sao Carlos, Brazil
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2015
Release Date:2022/12/10
First Page:171
Last Page:176
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung