Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146003
Title: Detection of humanoid robot design preferences using EEG and eye tracker
Authors: Liu, Yisi
Li, Fan
Tang, Lin Hei
Lan, Zirui
Cui, Jian
Sourina, Olga
Chen, Chun-Hsien
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Liu, Y., Li, F., Tang, L. H., Lan, Z., Cui, J., Sourina, O., & Chen, C.-H. (2019). Detection of humanoid robot design preferences using EEG and eye tracker. Proceedings of the International Conference on Cyberworlds, 219-244. doi:10.1109/CW.2019.00044
Abstract: Currently, many modern humanoid robots have little appeal due to their simple designs and bland appearances. To provide recommendations for designers and improve the designs of humanoid robots, a study of human's perception on humanoid robot designs is conducted using Electroencephalogram (EEG), eye tracking information and questionnaires. We proposed and carried out an experiment with 20 subjects to collect the EEG and eye tracking data to study their reaction to different robot designs and the corresponding preference towards these designs. This study can possibly give us some insights on how people react to the aesthetic designs of different humanoid robot models and the important traits in a humanoid robot design, such as the perceived smartness and friendliness of the robots. Another point of interest is to investigate the most prominent feature of the robot, such as the head, facial features and the chest. The result shows that the head and facial features are the focus. It is also discovered that more attention is paid to the robots that appear to be more appealing. Lastly, it is affirmed that the first impressions of the robots generally do not change over time, which may imply that a good humanoid robot design impress the observers at first sight.
URI: https://hdl.handle.net/10356/146003
ISBN: 9781728122977
DOI: 10.1109/CW.2019.00044
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW.2019.00044
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Conference Papers

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