Document - Drivers stress identification in real-world driving tasks

2022

Contribution to conference Open Access

Drivers stress identification in real-world driving tasks

Bano S., Tonellotto N., Gotta A.

Cross-modal transfer Deep Learning Stress detection

In the past few years, cross-modal distillation has garnered a lot of interest due to the rapid growth of multi-modal data. In this paper, we study stress recognition of the drivers corresponding to the driving situation. Our method enables us to recognize stress from unlabeled videos. We perform cross-modal distillation based on wearable physiological sensors and videos from on-board cameras. In this cross-modal distillation, knowledge is transferred from sensor to vision modality.

Source: PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 140–141, Pisa, Italy, 21-25 March 2022

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@inproceedings{oai:it.cnr:prodotti:471816,
	title = {Drivers stress identification in real-world driving tasks},
	author = {Bano S. and Tonellotto N. and Gotta A.},
	doi = {10.1109/percomworkshops53856.2022.9767455},
	booktitle = {PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 140–141, Pisa, Italy, 21-25 March 2022},
	year = {2022}
}