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

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Author(s):
Moretti, Caio Benatti ; Joaquim, Ricardo C. ; Terranova, Thais T. ; Battistella, Linamara R. ; Mazzoleni, Stefano ; Caurin, Glauco A. P. ; IEEE
Total Authors: 7
Document type: Journal article
Source: 2016 6TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB); v. N/A, p. 6-pg., 2016-01-01.
Abstract

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)

FAPESP's process: 13/07276-1 - CEPOF - Optics and Photonic Research Center
Grantee:Vanderlei Salvador Bagnato
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC