A Dataset and Methodology for Self-Efficacy Feeling Prediction During Industry 4.0 VR Activity
Authors : Thibaud Bounhar (LINEACT), Zaher Yamak (IRSEEM), Vincent Havard (LINEACT), David Baudry (LINEACT)
Conférence : Communications avec actes dans un congrès international - 12/03/2022 - Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
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.