Oleg V. Favorov

University of North Carolina at Chapel Hill, UNC/NCSU Joint Department of Biomedical Engineering

Papers

Total Citations

H-Index

About

Oleg V. Favorov is a researcher whose work bridges neuroscience, biomedical engineering, and tactile sensing. His primary research areas include concussion assessment methodologies, reaction time metrics, and texture classification using deep learning. Favorov has made significant contributions to improving the objectivity and accuracy of concussion diagnostics. His 2020 paper, "Methodological Problems With Online Concussion Testing" (18 citations), critically examines the reliability of consumer-grade computer systems in reaction time assessments, highlighting key flaws in widely used online tools. In a related study, "An Accurate Measure of Reaction Time can Provide Objective Metrics of Concussion" (11 citations), he demonstrates how refined reaction time metrics can offer more consistent and valid concussion evaluations. More recently, Favorov has advanced tactile sensing with his 2023 work on "Tactile Sensing with Contextually Guided CNNs" (3 citations), introducing a semisupervised deep learning approach for texture classification using accelerometers. This work has implications for robotics and surface exploration. His research is notable for its practical impact on sports medicine and its innovative fusion of neuroscience with machine learning, offering students and researchers a compelling model of interdisciplinary inquiry.