Toward Active Physical Human-Robot Interaction: Quantifying the Human State During Interactions

Unanticipated physical actions from the robot on humans [active physical human-robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a phy...

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Vydáno v:IEEE transactions on human-machine systems Ročník 52; číslo 3; s. 367 - 378
Hlavní autoři: Hu, Yue, Abe, Naoko, Benallegue, Mehdi, Yamanobe, Natsuki, Venture, Gentiane, Yoshida, Eiichi
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Atmospheric measurements
Clustering
Data collection
Force
Games
human factors
Human-centered robotics
Particle measurements
physical human–robot interaction
Physiology
Robots
Safety
Statistical analysis
Task analysis
ISSN:2168-2291, 2168-2305
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Shrnutí:Unanticipated physical actions from the robot on humans [active physical human-robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a physically and psychologically safe manner. The objective of this article is to explore the possibility of quantifying the humans' physical and mental state during an active physical interaction with a robot, by means of a laboratory experiment. We hypothesize that the active robot actions could cause measurable alterations in users' data, which could be related to their perceptions and personalities. In the experiment, the user plays a visual game using the robot, which has a hidden task that results in active physical actions on the user. We collect data from physical and physiological sensors, and the perceptions and personalities via questionnaires and a semi-structured interview. Statistical analysis and clustering of the data collected from a total of 35 participants showed the relationships between participants' physical and physiological data and their age, gender, perception, and personalities. Further developments based on these exploratory outcomes can be used to implement an active pHRI controller that can account for both the physical and the mental state of users.
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ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2021.3138684