Knowledge discovery, rehabilitation robotics, and serious games: examining training data

Autor(es)

Moretti, Caio B. ; Joaquim, Ricardo C. ; Caurin, Glauco A. P. ; Krebs, Hermano I. ; Martins, Jose, Jr. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2014 5TH IEEE RAS & EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB); v. N/A, p. 6-pg., 2014-01-01.

Resumo

In this paper, we present an initial attempt to apply Knowledge Discovery techniques over real performance data from patients enrolled in robotic therapy in order to explore how to better optimize therapy. Performance data sets encompass measurements such as position, velocity and force, as well as final performance measures. We apply the Principal Component Analysis method in an attempt to reduce the dimensionality of the problem, molding subsets that were the input into a Multilayer Perceptron Artificial Neural Network which would carry out data mining with the purpose of discovering the relative significance of each field, in relation to a performance measure. It was possible to notice the impact caused by the lack of each field in terms of specific performance measures, indicating which data are more relevant to use in further experiments. (AU)

• Processo FAPESP: 13/05772-1 - Sistema web de suporte ao terapeuta para análise de desempenho de pacientes em tratamentos de reabilitação robótica
• Beneficiário: Caio Benatti Moretti
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica