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Authors
Johari, Kritika https://orcid.org/0000-0002-6647-0246
Tong, Christopher Tay Zi
Bhardwaj, Rishabh
Subbaraju, Vigneshwaran
Kim, Jung-Jae
Tan, U.-Xuan
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Funding
Funding for this research was provided by:
Agency for Science, Technology and Research (AME Programmatic Funding Scheme (Project# A18A2b0046).)
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Article History
Accepted: 14 December 2023
First Online: 27 January 2024
Declarations
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: This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project # A18A2b0046). All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
: In this paper, we have developed an approach whereby we generate synthetic data, thus skipping the process of data collection for machine learning. The objective of performing the experiment with humans is to demonstrate the feasibility. As the tasks are simple (minimal risks to participants) and no personal identifier is collected, we have obtained IRB exemption approval.
