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Progress assessment of Stroke patients in robotic rehabilitation treatments

Grant number: 18/26493-7
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): February 01, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Alexandre Cláudio Botazzo Delbem
Grantee:Caio Benatti Moretti
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
Associated scholarship(s):19/06551-5 - Machine-learning-based biomarkers towards customization of robotic rehabilitation treatments, BE.EP.DR


Coupled with sensors, robotic devices for Stroke rehabilitation describe the motor behavior of patients as kinematic and dynamic data, which are underexplored in the machine learning context, due to the time-consuming task of pursuing enough data volume. Moreover, the establishment of means for a quantitative assessment of patient progress, as well as whether data volume is large enough for solid learning guarantees remain unclear. Pondered by premises from Statistical Learning Theory, this research project proposes means for assessing the patient progress during the treatment. The uncertainty in binary classification between left and right hemiparesis are to be exploited, towards grounds for the assumption that pathological features in data attenuate over time, as an evidence of patient recovery. (AU)