Development of A Multimodal Trust Database in Human-Robot Collaborative Contexts
Document Type
Conference Proceeding
Publication Date
1-1-2023
Journal / Book Title
2023 IEEE 14th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2023
Abstract
Robots are gradually being incorporated into the workforce to assist with labor-intensive and repetitive tasks, especially in smart manufacturing contexts. This leads to increased human-robot collaboration, which may be an unfamiliar, distrustful, and uncomfortable situation for inexperienced people to navigate. Motivated by these issues and aiming to have a comprehensive understanding of the factors that affect people's trust in robots, we developed a new trust database by investigating the trust between human collaborators wearing four biological sensors and a robot performing collaborative tasks. Using these sensors, we collected trust-related physiological human factors from the brain (EEG), heart (ECG), forearm (EMG), and eyes during human-robot collaborative tasks. As well as a trust rating through a questionnaire, this allows for the creation of a multimodal human-robot trust database (TrustBase). TrustBase provides insightful guidance to optimize and improve the environment deployment and robot configuration in human-robot partnerships within smart manufacturing contexts.
DOI
10.1109/UEMCON59035.2023.10316014
Journal ISSN / Book ISBN
85179756158 (Scopus)
Montclair State University Digital Commons Citation
Parron, Jesse; Nguyen, Thai Thao; and Wang, Weitian, "Development of A Multimodal Trust Database in Human-Robot Collaborative Contexts" (2023). School of Computing Faculty Scholarship and Creative Works. 55.
https://digitalcommons.montclair.edu/computing-facpubs/55