Veronica Mattioli

@article{gamadabecarafesg:2025:psy,

title = {Cardiac Autonomic Responsivity to Car Driving in a Real Context},

author = {Sara Gambetta and Veronica Mattioli and Luca Davoli and Laura Belli and Luca Carnevali and Riccardo Raheli and Gianluigi Ferrari and Andrea Sgoifo},

doi = {10.1027/0269-8803/a000345},

issn = {2151-2124},

year  = {2025},

date = {2025-06-19},

urldate = {2025-01-01},

journal = {Journal of Psychophysiology},

volume = {39},

number = {1},

pages = {36-48},

abstract = {Driving a vehicle is a complex behavior requiring an optimal psychophysiological state in order to safely accomplish the task. The identification of human factors influencing driving behavior could enable the development of technologies for monitoring drivers and improving their comfort. This study aims to monitor the driver’s cardiac autonomic activity during real road driving, assuming psychophysiological variations depending on the driving context (e.g., ring road vs. urban driving) and the induction of psychosocial stress during urban driving activity. Moreover, we investigated the extent to which perceived stress, anxiety symptoms, habitual driving behavior as well as sex, and driving experience, influenced cardiac autonomic responses while driving. Heart rate (HR) and its variability (HRV) were assessed during a real driving task in a sample of thirty-eight drivers, including twenty males and eighteen females. Drivers’ psychometric characteristics were collected using questionnaires. HRV analysis revealed a significant overall autonomic activation while driving, independent of the exposure to external stressors. Neither sex nor driving experience seemed to affect cardiac autonomic response to driving. A significant positive correlation emerged between anxiety-stress symptoms and aberrant driving behavior. In summary, our results suggest that the overall driving task produced a notable impact on cardiac autonomic neural modulation. Understanding the factors that influence driving performance and modulate the resulting physiological response could provide a springboard for practical applications, such as the development of human-vehicle interaction monitoring systems for optimal psychophysiological arousal while driving.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{madabegacasgrafe:2024:sensors,

title = {IoT-Based Assessment of a Driver’s Stress Level},

author = {Veronica Mattioli and Luca Davoli and Laura Belli and Sara Gambetta and Luca Carnevali and Andrea Sgoifo and Riccardo Raheli and Gianluigi Ferrari},

doi = {10.3390/s24175479},

issn = {1424-8220},

year  = {2024},

date = {2024-08-23},

urldate = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {17},

abstract = {Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}