Robjona Toska is a Postgraduate Research Fellow at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino.

Her research focuses on the analysis of inertial sensor data for the quantitative assessment of motor symptoms in Parkinson’s disease, combining biomedical signal processing and data analysis. Her work aims to develop objective, technology-based markers for clinical evaluation.

Bachelor and Master

Robjona Toska obtained her B.Sc. (2019-2022) and M.Sc. (2022–2025, cum laude) degrees in Biomedical Engineering from Politecnico di Torino, specializing in Biomedical Instrumentation. Throughout her studies, she developed a strong interest in biomedical signal processing, machine learning and neurotechnology applications. She is passionate about developing translational tools that bridge clinical and engineering research, ultimately improving patients’ quality of life.

Her Master’s thesis, “Visual Adaptation Strategies under Simulated Central Vision Loss using Eye-Tracking in Augmented Reality”, explored compensatory oculomotor mechanisms under simulated central scotoma. She designed and implemented an augmented reality application on Microsoft HoloLens 2 to induce gaze-contingent visual defects and developed experimental tasks for assessing reading and visual search performance. The study combined hardware–software integration, eye-tracking analysis, and experimental validation on 33 participants.

Working Experience and Research Activities

Robjona is currently a Postgraduate Research Fellow at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino, in collaboration with Molinette Hospital. Her research focuses on the analysis of inertial sensor signals acquired from Parkinson’s disease patients during motor tasks. The project aims to investigate correlations between clinical scales and quantitative motion features derived from accelerometers and gyroscopes, employing signal fusion and filtering techniques for robust feature extraction.

Robjona is an inducted member of the IEEE-HKN Mu Nu Chapter.