Associate Professor, University of Udine
Antonio Affanni received the M. S. in Electronic engineering from University of Parma, Parma, Italy in 2003. In 2007 he got the Ph. D. degree in Information Technologies from the same University. Since 2009 he joined the Electrical and Mechanical Engineering department at University of Udine, Udine, Italy. His scientific interests are in the fields of wearable sensors, industrial sensors and lab-on-chip.
Advanced sensors and machine learning techniques for biomedical signal processing
with Pamela Zontone
Stress, anxiety, drowsiness, and other emotional states are important factors that should be assessed in car drivers. These states can affect their physical and physiological well-being and, as a result, can influence their behaviour and reactions to potentially dangerous road situations.
The first part of the lecture will show an overview of commercial wearable sensors used in literature to classify and detect mental states of drivers. In particular, we will explore the possible metrological issues that arise when using commercial devices and we will show the advantages/drawbacks of using commercial sensors versus ad hoc developed prototypes. Since the sensors are the first link in the chain of machine learning and deep learning algorithms, their metrological characterization and traceability is of paramount importance in order to avoid misleading results in classification.
In the second part of the lecture, we will cover the fundamentals of biosignals and machine learning (ML) algorithms. Additionally, we will introduce some of the ML architectures we have designed over the years for stress detection in car drivers. Specifically, these systems are based on the analysis of physiological signals, such as the electrodermal activity (EDA), electrocardiogram (ECG), and electroencephalogram (EEG) signals, to detect the different emotional states experienced by subjects while driving in various simulated scenarios. We will discuss the results obtained from experiments carried out during both manual and autonomous driving scenarios, as well as while driving in a simulated urban area characterized by various traffic situations.