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Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
Örebro University, School of Science and Technology. (Machine Perception Interaction Lab)ORCID iD: 0000-0001-6168-0706
School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
2022 (English)In: International journal of human-computer studies, ISSN 1071-5819, E-ISSN 1095-9300, Vol. 158, article id 102744Article in journal (Refereed) Published
Abstract [en]

Safety in human-robot interaction can be divided into physical safety and perceived safety, where the later is still under-addressed in the literature. Investigating perceived safety in human-robot interaction requires a multidisciplinary perspective. Indeed, perceived safety is often considered as being associated with several common factors studied in other disciplines, i.e., comfort, predictability, sense of control, and trust. In this paper, we investigated the relationship between these factors and perceived safety in human-robot interaction using subjective and objective measures. We conducted a two-by-five mixed-subjects design experiment. There were two between-subjects conditions: the faulty robot was experienced at the beginning or the end of the interaction. The five within-subjects conditions correspond to (1) baseline, and the manipulations of robot behaviors to stimulate: (2) discomfort, (3) decreased perceived safety, (4) decreased sense of control and (5) distrust. The idea of triggering a deprivation of these factors was motivated by the definition of safety in the literature where safety is often defined by the absence of it. Twenty-seven young adult participants took part in the experiments. Participants were asked to answer questionnaires that measure the manipulated factors after within-subjects conditions. Besides questionnaire data, we collected objective measures such as videos and physiological data. The questionnaire results show a correlation between comfort, sense of control, trust, and perceived safety. Since these factors are the main factors that influence perceived safety, they should be considered in human-robot interaction design decisions. We also discuss the effect of individual human characteristics (such as personality and gender) that they could be predictors of perceived safety. We used the physiological signal data and facial affect from videos for estimating perceived safety where participants’ subjective ratings were utilized as labels. The data from objective measures revealed that the prediction rate was higher from physiological signal data. This paper can play an important role in the goal of better understanding perceived safety in human-robot interaction.

Place, publisher, year, edition, pages
Academic Press, 2022. Vol. 158, article id 102744
Keywords [en]
Perceived safety, Human robot interaction, Comfort, Sense of control, Trust, Physiological signal data, Facial expressions, Multidisciplinary perspective
National Category
Robotics
Research subject
Human-Computer Interaction; Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-95673DOI: 10.1016/j.ijhcs.2021.102744ISI: 000782270600008Scopus ID: 2-s2.0-85119702541OAI: oai:DiVA.org:oru-95673DiVA, id: diva2:1615201
Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2024-01-16Bibliographically approved
In thesis
1. Perceived Safety in Social Human-Robot Interaction
Open this publication in new window or tab >>Perceived Safety in Social Human-Robot Interaction
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This compilation thesis contributes to a deeper understanding of perceived safety in human-robot interaction (HRI) with a particular focus on social robots. The current understanding of safety in HRI is mostly limited to physical safety, whereas perceived safety has often been neglected and underestimated. However, safe HRI requires a conceptualization of safety that goes beyond physical safety covering also perceived safety of the users. Within this context, this thesis provides a comprehensive analysis of perceived safety in HRI with social robots, considering a diverse set of human-related and robot-related factors.

Two particular challenges for providing perceived safety in HRI are 1) understanding and evaluating human safety perception through direct and indirect measures, and 2) utilizing the measured level of perceived safety for adapting the robot behaviors. The primary contribution of this dissertation is in addressing the first challenge. The thesis investigates perceived safety in HRI by alternating between conducting user studies, literature review, and testing the findings from the literature within user studies.

In this thesis, six main factors influencing perceived safety in HRI are lifted: the context of robot use, the user’s comfort, experience and familiarity with robots, trust, sense of control over the interaction, and transparent and predictable robot behaviors. These factors could provide a common understanding of perceived safety and bridge the theoretical gap in the literature. Moreover, this thesis proposes an experimental paradigm to observe and quantify perceived safety using objective and subjective measures. This contributes to bridging the methodological gap in the literature.

The six factors are reviewed in HRI literature, and the robot features that affect these factors are organized in a taxonomy. Although this taxonomy focuses on social robots, the identified characteristics are relevant to other types of robots and autonomous systems. In addition to the taxonomy, the thesis provides a set of guidelines for providing perceived safety in social HRI. As a secondary contribution, the thesis presents an overview of reinforcement learning applications in social robotics as a suitable learning mechanism for adapting the robots’ behaviors to mitigate psychological harm.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2022. p. 77
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 94
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-98102 (URN)9789175294322 (ISBN)
Public defence
2022-04-28, Örebro universitet, Långhuset, Hörsal L2, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2022-03-17 Created: 2022-03-17 Last updated: 2022-05-04Bibliographically approved

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Akalin, NezihaLoutfi, Amy

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