Quantifying Human Mental States in Physical Human-Robot Interaction
Abstract
To support independent living among older adults, we propose the introduction of
robots as domestic service providers capable of assisting with daily activities like showering,
toileting, and transferring. These tasks often require physical interaction, necessitating
robots with efficient physical capabilities and a nuanced social understanding of humans,
such as stress levels and attitudes toward the robot (robot perception). Our objective is to
explore potential real-time (objective) quantification approaches in physical Human-Robot
Interaction (pHRI), with the ultimate aim of developing a human mental state predictor
to enhance robots’ decision-making abilities.
We identify cognitive ergonomics and belief as two key human factors associated with
cognitive aspects that can sufficiently represent the human mental state. To accurately
measure these dimensions, we recognize the need for a combination of objective and subjective approaches. However, literature examining the suitability of subjective measures in
pHRI, which is crucial for obtaining reliable objective quantification, is limited. Therefore,
our research includes an assessment of a critical subjective quantification tool for a key
human factor, i.e., robot perception, followed by an exploration of correlations between
subjective and objective measures.
Our findings reveal significant correlations between subjective and objective measures,
indicating the potential for objective indicators such as skin temperature and task performance to reliably infer cognitive ergonomics and belief in collaborative pHRI scenarios.
Furthermore, we observe promising associations between pre-gathered data, such as personality traits and demographics, and measurable dimensions of cognitive ergonomics and
belief, suggesting a supplementary approach for enhancing inference using objective measures.
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Cite this version of the work
Nourhan Abdulazeem
(2024).
Quantifying Human Mental States in Physical Human-Robot Interaction. UWSpace.
http://hdl.handle.net/10012/20476
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