| Full text | |
| Author(s): |
Afonso, Luis C. S.
;
Pereira, Clayton R.
;
Weber, Silke A. T.
;
Hook, Christian
;
Papa, Joao P.
;
IEEE
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2017-01-01. |
| Abstract | |
Approximately 50, 000 to 60, 000 new cases of Parkinson's disease (PD) are diagnosed yearly. Despite being non-lethal, PD shortens life expectancy of the ones affected with such disease. As such, researchers from different fields of study have put great effort in order to develop methods aiming the identification of PD in its early stages. This work uses handwriting dynamics data acquired by a series of tasks and proposes the application of a deep-driven graph-based clustering algorithm known as Optimum-Path Forest to learn a dictionary-like representation of each individual in order to automatic identify Parkinson's disease. Experimental results have shown promising results, with results comparable to some state-of-the-art approaches in the literature. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/19403-6 - Energy-based learning models and their applications |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |