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The aim of the project BRAINSAFEDRIVE is the realization of a system that will be able to detect in real time the cerebral activity of a driver during simulated and/or real drive and to infer his/her “internal mental state” such as “inattentive”, “confused”, “tired”, etc.
To realize such integrated device it will be necessary to have state of the art expertise for:
1A world leader in the cerebral signal analysis at the Univ. Sapienza, Dept Molecular Medicine, Italy (DMM).
2A world leader in the use of machine-learning algorithms for the classification of cerebral signals at Mälardalen University, Sweden (MDH).
3An innovative company, (BrainSigns; BS), involved in the measurement of cerebral activity during real and simulated driving.
Importantly, the two universities and the Italian SME company already cooperated under the framework of EU-Horizon2020 (BS and MDH) and Italian projects (DMM and BS).
During the 3 years of the project, young Italian scientists of DMM will be trained to use the complex algorithms for machine learning from the Swedish group (MDH).
Swedish scientists will benefit instead from expertise in cerebral signal processing from the Italian group to increase their knowledge in such area. SME company (BS) will benefit from neuroscience-based algorithms derived from the academic partners (DMM and MDH) to develop a sw&hw system for monitoring brain states during drive.
In summary, BRAINSAFEDRIVE will realize a tool for the comprehension and characterization of the cerebral states during visuo-motor decision-making in drive that will be able to raise the interest from scientists of academia as well as from the automotive industries of both countries.
Cognitive Neuroscience results suggest that cerebral activity collected in subjects that perform visuo-motor tasks is quite different when the subjects became “tired”, or “inattentive” with respect to “normal” mental states.
The aim of BRAINSAFEDRIVE is to generate a system that detects in real time dangerous mental states for the drivers in simulated and real drive.
The occurrence of such mental states will be correlated with the variation of the drive parameters (e.g. depth of the brake, average speed, etc etc), when compared to the drive parameters of the “normal, healthy and safe” drive.
Deviations from the normative database of the drive parameters will be then associated to the occurrence of the “abnormal” mental states, such as “stress”, “mental fatigue” etc etc. Thus, such drive parameters could be then used in future cars, to compare the actual performances of the driver (e.g. depth of brakes, average speed) with that of the group of “healthy drivers”. If significant deviations occurs, the system will start an appropriate action according to the detected mental state.
