Fecha
2025-04-24
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/closedAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Springer Nature
Citas
0 citas en 
0 citas en 
Resumen
Humans can undergo rapid emotional changes and these changes can significantly affect their ability to perform tasks. Consequently, when we develop Human-Centred Symbiotic Artificial Intelligence (HCSAI) systems to support the interaction between autonomous systems and drivers, the intelligent system controlling the vehicle must adapt to the state of the user. This symbiotic relationship highlights the importance of collaboration and cooperation between humans and agents of artificial intelligence (AI). In the field of Autonomous Vehicles (AV), measurements must be made using non-invasive devices that do not interfere with the driving task. We have therefore used wristbands to measure physiological signals. This comparison is used to select the right equipment for setting up user modelling in different levels of autonomous vehicles. We compared the accuracy, precision and ease of use of three different wristbands: Fitbit Sense2, Empatica E4 and Emotibit. We tested the performance of the bands in two different driving scenarios: SAE Level 4 environment using autonomous golf carts (iCab), and a real-world SAE Level 2 driving environment in Scotland using a Toyota Prius equipped with Comma OpenPilot technology. The Fitbit Sense2 does not allow researchers to access raw data. The Emotibit and the Empatica E4 are designed for research, so they provide access to raw data, while the Empatica E4 is easier to use than the Emotibit. The comparison calls for the development of open source codes that will facilitate integration with different operating systems and other devices, as well as an easy way to use the devices in real time.
Descripción
The registered version of this conference paper, first published in "Quesada-Arencibia, A., Affenzeller, M., Moreno-Díaz, R. (eds) Computer Aided Systems Theory – EUROCAST 2024. EUROCAST 2024. Lecture Notes in Computer Science, vol 15172. Springer, Cham", is available online at the publisher's website: https://doi.org/10.1007/978-3-031-82949-9_30
Categorías UNESCO
Palabras clave
Physiological signals, Human-centric symbiotic artificial intelligence, Autonomous driving
Citación
Fernandez-Matellan, R., Puertas-Ramirez, D., Gómez, D.M., Boticario, J.G. (2025). Comparison of Physiological Data Acquisition for Modeling of Drivers in Autonomous Vehicles. In: Quesada-Arencibia, A., Affenzeller, M., Moreno-Díaz, R. (eds) Computer Aided Systems Theory – EUROCAST 2024. EUROCAST 2024. Lecture Notes in Computer Science, vol 15172. Springer, Cham. https://doi.org/10.1007/978-3-031-82949-9_30
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial