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A novel physiological-based system to assess drivers’ stress during earth moving simulated activities


Daniele Bibbo , Moses Mariajoseph, Barbara Gallina, Marco Carli

Publication Type:

Journal article


Artificial Intelligence (Section of Electronics)






Earth-moving vehicles (EMVs) are vital in numerous industries, including construction, forestry, mining, cleaning, and agriculture. The changing nature of the off-road environment in which they operate makes situational awareness for readiness and, consequently, mental stress crucial for drivers and requires a high level of controllability. Therefore, the monitoring of drivers’ acute stress patterns may be used as an input in identifying various levels of attentiveness. This research presents an experimental evaluation of a physiological-based system that can be useful to evaluate the readiness of a driver in different conditions. For the experimental validation, physiological signals such as electrocardiogram (ECG), galvanic skin response (GSR) and speech data were collected from nine participants throughout driving experiments of increasing complexity on a specific simulator. The experimental results show that the identified parameters derived from the acquired physiological signals can help us understand the driver status when performing different tasks, the engagement of which is related to different road environments. This multi-parameter approach can provide more reliable information compared to single parameter approaches (e.g., eye monitoring with a camera) and identify driver status variations, from relaxed to stressed or drowsy. The use of these signals allows for the development of a smart driving cockpit, which could communicate to the vehicle the driver’s status, to set up an innovative protection system aiming to increase road safety.


author = {Daniele Bibbo and Moses Mariajoseph and Barbara Gallina and Marco Carli},
title = {A novel physiological-based system to assess drivers’ stress during earth moving simulated activities},
volume = {11},
number = {24},
pages = {1--20},
month = {December},
year = {2022},
journal = {Artificial Intelligence (Section of Electronics)},
publisher = {MDPI},
url = {}