TY - GEN
T1 - EEG-Based Robot Alert System for Improving User Concentration
AU - Raja, Kiruthika
AU - Laohakangvalvit, Tipporn
AU - Sripian, Peeraya
AU - Sugaya, Midori
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Brain-computer Interfaces (BCIs) and robots hold great potential for enhancing focus in tasks and acting as educational agents. In this research, we propose to create a robot alert system that helps improve user’s concentration by alerting the user when their concentration drops. The user’s concentration is detected by their brain activity using a commercially available electroencephalograph (EEG) sensor. This paper presents our design and development of the proposed EEG-based robot alert system using voice and facial expressions. To investigate the possibility of improving user’s concentration, we evaluated the developed system by carrying out an experiment to improve user’s concentration while learning. This was evaluated by monitoring the user’s concentration using EEG while watching an educational video with and without our proposed robot alert system. The experimental results show the user’s concentration levels to increase after the alert, thereby suggesting that our EEG-based robot alert system successfully improved the user’s concentration. Our alert system contributes to improving user’s concentration in situations that require cognitive thinking such as e-learning and driving.
AB - Brain-computer Interfaces (BCIs) and robots hold great potential for enhancing focus in tasks and acting as educational agents. In this research, we propose to create a robot alert system that helps improve user’s concentration by alerting the user when their concentration drops. The user’s concentration is detected by their brain activity using a commercially available electroencephalograph (EEG) sensor. This paper presents our design and development of the proposed EEG-based robot alert system using voice and facial expressions. To investigate the possibility of improving user’s concentration, we evaluated the developed system by carrying out an experiment to improve user’s concentration while learning. This was evaluated by monitoring the user’s concentration using EEG while watching an educational video with and without our proposed robot alert system. The experimental results show the user’s concentration levels to increase after the alert, thereby suggesting that our EEG-based robot alert system successfully improved the user’s concentration. Our alert system contributes to improving user’s concentration in situations that require cognitive thinking such as e-learning and driving.
KW - Affective interaction
KW - Brain-computer interface
KW - Concentration
KW - Robot
KW - Social robot
UR - http://www.scopus.com/inward/record.url?scp=85133288383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133288383&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06388-6_27
DO - 10.1007/978-3-031-06388-6_27
M3 - Conference contribution
AN - SCOPUS:85133288383
SN - 9783031063879
T3 - Communications in Computer and Information Science
SP - 202
EP - 209
BT - HCI International 2022 Posters - 24th International Conference on Human-Computer Interaction, HCII 2022, Proceedings
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Human-Computer Interaction, HCI International, HCII 2022
Y2 - 26 June 2022 through 1 July 2022
ER -