BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road



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BRAINSAFEDRIVE will develop a tool as attentional detectors that detect drivers’ mental state in terms of stress, cognitive load, sleepiness in real time during simulated and/or natural driving situations. Here, the project combines two necessary state of the art expertise’s: 1) the acquisition and analysis of cerebral signals i.e. Electroencephalography (EEG) and Electrooculography (EOG); 2) the application of artificial intelligence and machine learning algorithms. The drivers’ mental state will be correlated with vehicular parameters e.g. brake, speed, acceleration, lane chnges etc and classify the driving as "normal, healthy and safe” driver. Three actors involved in this bilateral i.e. Sweden and Italy collaboration are: 1) Dept. of Molecular Medicine (DMM) at Sapienza University, Italy with the expertise of cerebral signal analysis; 2) Intelligence Systems group at
Mälardalen University (MDH), Sweden with the expertise of AI and machine learning algorithms; 3) BrainSigns (BS), Italy as SME with the expertise of the measurement system of cerebral activity during driving. MDH and BS already are collaborating through a H2020 project SimuSafe. During 3 years a) the researchers at DMM will be educated on different algorithms of AI and machine learning from MDH; b) researchers at MDH will be benefited with advance signal processing on cerebral signals from DMM; c) the BS as SME company will be benefited through neuroscience-based algorithms derived from DMM and MDH.

[Show all publications]

Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction (Jun 2023)
Waleed Reafee Sbu Jmoona, Mobyen Uddin Ahmed, Mir Riyanul Islam, Shaibal Barua, Shahina Begum, Ana Ferreira , Nicola Cavagnetto
19th International Conference on Artificial Intelligence Applications and Innovations (AIAI2023)

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions (Mar 2023)
Arnab Barua, Mobyen Uddin Ahmed, Shahina Begum
Journal of IEEE Access (IEEE-Access)

Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour: An Exploratory Analysis on Heterogeneous Data (Feb 2023)
Mir Riyanul Islam, Mobyen Uddin Ahmed, Shahina Begum
15th International Conference on Agents and Artificial Intelligence (ICAART2023)

When a CBR in Hand is Better than Twins in the Bush (Sep 2022)
Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Mir Riyanul Islam, Rosina O Weber
Fourth Workshop on XCBR: Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR-ICCBR2022)

A Systematic Review of Explainable Artificial Intelligence in terms of Different Application Domains and Tasks (Jan 2022)
Mir Riyanul Islam, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum
Applied Sciences (ApplSci)

Vision-Based Driver’s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning (Nov 2021)
Hamidur Rahman, Mobyen Uddin Ahmed, Shaibal Barua, Peter Funk, Shahina Begum
Special Issue on Deep Learning in Biomedical Informatics and Healthcare (Sensors)

Mobyen Uddin Ahmed, Professor

Room: U1-089
Phone: +46-021-107369