A Dataset and Methodology for Self-Efficacy Feeling Prediction During Industry 4.0 VR Activity - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

A Dataset and Methodology for Self-Efficacy Feeling Prediction During Industry 4.0 VR Activity

Résumé

Virtual Reality Learning Environments (VRLE) have advantages in training contexts. However, VRLE lacks of User-adaptive system which adapt scenario to the user’s state. As there is a lack of multi-sensor dataset, this paper presents the IVRASED dataset collected in an industrial VRLE with the following sensors: electroencephalogram (EEG), eye-tracking (ET), galvanic skin response (GSR) and electrocardiogram (ECG). Classification of the user's state is performed with a deep learning architecture and the results show an accuracy of 77.8% for the best sensors combination.
Fichier non déposé

Dates et versions

hal-04080307 , version 1 (24-04-2023)

Identifiants

Citer

Thibaud Bounhar, Zaher Yamak, Vincent Havard, David Baudry. A Dataset and Methodology for Self-Efficacy Feeling Prediction During Industry 4.0 VR Activity. 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE, Mar 2022, Christchurch, New Zealand. pp.176-182, ⟨10.1109/VRW55335.2022.00045⟩. ⟨hal-04080307⟩
5 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More