Knowledge Discovery strategy over patient performance data towards the extraction of hemiparesis-inherent features: A case study

Autor(es)

Moretti, Caio Benatti ; Joaquim, Ricardo C. ; Terranova, Thais T. ; Battistella, Linamara R. ; Mazzoleni, Stefano ; Caurin, Glauco A. P. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2016 6TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB); v. N/A, p. 6-pg., 2016-01-01.

Resumo

Aiming to perform an extraction of features which are strongly related to hemiparesis, this work describes a case study involving the efforts of patients in upper-limb rehabilitation, diagnosed with such pathology. Expressed as data (kinematic and dynamic measures), patients' performance were sensed and stored by a single InMotion Arm robotic device for further analysis. It was applied a Knowledge Discovery roadmap over collected data in order to preprocess, transform and perform data mining through machine learning methods. Our efforts culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having 8 features of rehabilitation performance feeding the input. Interpreting the obtained feature structure, it was observed that force-related attributes are more significant to the composition of the extracted pattern. (AU)

• Processo FAPESP: 13/07276-1 - CEPOF - Centro de Pesquisa em Óptica e Fotônica
• Beneficiário: Vanderlei Salvador Bagnato
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs