Facial Anthropomorphic Trustworthiness Scale for Social Robots: A Hybrid Approach

Yao Song, Ameersing Luximon, Yan Luximon

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid deep convolution approach was employed in this study, involving a crowdsourcing platform for data collection and deep convolution and factor analysis for data processing. The goal was to develop a scale, called Facial Anthropomorphic Trustworthiness towards Social Robots (FATSR-17), to measure the trustworthiness of a robot’s facial appearance. The final measurement scale comprised four dimensions, “ethics concern”, “capability”, “positive affect”, and “anthropomorphism”, consisting of 17 items. An iterative examination and a refinement process were conducted to ensure the scale’s reliability and validity. The study contributes to the field of robot design by providing designers with a structured toolkit to create robots that appear trustworthy to users.

Original languageEnglish
Article number335
JournalBiomimetics
Volume8
Issue number4
DOIs
Publication statusPublished - Aug 2023

Keywords

  • artificial intelligence
  • face
  • scale
  • social robot

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering
  • Molecular Medicine

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