| Full text | |
| 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 |