| KEYWORD |
Stress and mental workload detection: development of a real-time application
keywords MENTAL WORKLOAD, PHYSIOLOGICAL SIGNALS, REAL TIME, STRESS
Reference persons DANILO DEMARCHI
External reference persons Irene Buraioli (irene.buraioli@polito.it)
Research Groups MiNES (Micro&Nano Electronic Systems)
Thesis type EXPERIMENTAL
Description Stress and cognitive load alter our mental and emotional processes, hampering decision-making in many situations in our daily lives. Our body is sensitive to such conditions, and several physiological signals, such as brain oxygenation (fNIRS signal), cardiorespiratory signal, EDA signal, body temperature, and eye movements, contain important information regarding stress and cognitive load. Given the large number of data and the complexity of the problem, it is necessary to develop a real-time tool that can predict a person's stress and mental workload.
The proposed thesis work will be structured as follows:
1) The candidate will be asked to perform a state-of-the-art analysis of real-time tools in physiological signal processing.
2) Next, the candidate will have to transform our post-processing tool for signal processing into a Python-based real-time version.
3) Finally, the candidate will have to validate the functioning of his or her algorithm on real data obtained by using ad hoc tests.
This project aims to develop in the candidate both informatics and biomedical skills applied to real and concrete needs of today that are becoming increasingly important in everyday life, such as stress and mental workload detection.
Required skills Programming in Python, Biomedical signal processing, programming in Matlab
Deadline 14/12/2024
PROPONI LA TUA CANDIDATURA