Audio-Visual Stimuli Change not Only Robot’s Hug Impressions but Also Its Stress-Buffering Effects

Shiomi, Masahiro; Hagita, Norihiro

doi:10.1007/s12369-019-00530-1

Cited by 17 publications

(9 citation statements)

References 37 publications

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“…Some studies on handshake scenarios were conducted with the Meka robot [59] and HRP-2 robot [60], while other studies investigated kinesthetic teaching using a 7-DoF Franka Emika manipulator [100]. Studies conducted on hugging scenarios investigated the impact of manipulating the visual and tactile appearance using an ARMAR-IIIb robot [61], the duration and style of hugs using a CASTOR robot [62], and the impact of the perceived gender using a Metahug system [57,63].…”

Section: Mapping Scheme For Phrimentioning

confidence: 99%

Human Factors Considerations for Quantifiable Human States in Physical Human–Robot Interaction: A Literature Review

Abdulazeem,

2023

Sensors

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As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human–robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing the studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human–robot interaction (pHRI), noted the factors typically addressed together, and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on the measurable data for each human factor. We also formed a map of the common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI studies (when the physical contact is mediated via a third item) often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on the human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address the quantification approaches based on measurable data.

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Section: Mapping Scheme For Phrimentioning

confidence: 99%

Human Factors Considerations for Quantifiable Human States in Physical Human–Robot Interaction: A Literature Review

Abdulazeem,

2023

Sensors

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“…The main application of human-robot touch interaction is mental therapy for users. Several studies have investigated the merits of touch interaction with robots [7][8][9][10]34]. One famous therapy robot is a touchable seal robot (Paro) whose mental support effectiveness is well known worldwide [7].…”

Section: Touch In Human-robot Interactionmentioning

confidence: 99%

Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot

Mejía

Sumioka²,

Ishiguro

et al. 2021

Applied Sciences

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Although before-touch situations are essential to achieve natural touch interactions between people and robots, they receive less attention than after-touch situations. This study reports pre-touch reaction distance analysis results around touchable upper body parts, i.e., shoulders, elbows, and hands, based on human–human pre-touch interaction. We also analyzed the effects of gender, approach side, speed, and acclimation in modeling the pre-touch reaction distance, and found that the distance around the hands is smaller than the distance around the shoulders and elbows, and speed and acclimation affect the distance. On the other hand, gender and approach side do not significantly affect the pre-touch reaction distance. Finally, we implemented the results in a male-looking android and confirmed that it reacted toward pre-touch based on the obtained model.

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“…While work in HRI has also investigated the effects of social interactions on “stress,” this work has largely focused on how these interactions affect the “wellbeing” of the human actor (see ( Ling and Björling, 2020 ) for a review), but not the artificial agent. For instance, ( Aminuddin et al, 2016 ; Willemse et al, 2017 ; Shiomi and Hagita, 2021 ; Felnhofer et al, 2019 ), assessed numerous psychological and/or physiological measures related to “stress” during and after interactions with a robotic partner, with ( Willemse and Van Erp, 2019 ) assessing the effects after partners had established an (affective) bond with their robot partner. Despite some recent work that assessed how an embodied model of “stress” affected the (compulsive) behaviours and wellbeing of a robot, ( Lewis and Cañamero, 2019 ), more work is still required to understand how the adaptive properties associated with “stress” (and its underlying mechanisms) can affect (positively or negatively) the “wellbeing” of (virtual or physical) artificial agents.…”

Section: Introductionmentioning

confidence: 99%

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Khan

Cañamero

2022

Front. Robot. AI

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In dynamic (social) environments, an affective state of “stress” can be adaptive and promote agent wellbeing, but maladaptive if not appropriately regulated. The presence of (and interactions with) affect-based social support has been hypothesised to provide mechanisms to regulate stress (the “social buffering” hypothesis), though the precise, underlying mechanisms are still unclear. However, the hormone oxytocin has been implicated in mediating these effects in at least two ways: by improving social appraisals and reducing the short-term release of stress hormones (i.e., cortisol), and adapting an agent’s long-term stress tolerance. These effects likely facilitate an agent’s long-term adaptive ability by grounding their physiological and behavioural adaptation in the (affective) social environment, though these effects also appear to be context-dependent. In this paper, we investigate whether two of the hypothesised hormonal mechanisms that underpin the “social buffering” phenomenon affect the long-term wellbeing of (artificial) social agents who share affective social bonds, across numerous social and physical environmental contexts. Building on previous findings, we hypothesise that “social buffering” effects can improve the long-term wellbeing of agents who share affective social bonds in dynamic environments, through regular prosocial interactions with social bond partners. We model some of the effects associated with oxytocin and cortisol that underpin these hypothesised mechanisms in our biologically-inspired, socially-adaptive agent model, and conduct our investigation in a small society of artificial agents whose goal is to survive in challenging environments. Our results find that, while stress can be adaptive and regulated through affective social support, long-term behavioural and physiological adaptation is determined by the contextual perception of affective social bonds, which is influenced by early-stage interactions between affective social bond partners as well as the degree of the physical and social challenges. We also show how these low-level effects associated with oxytocin and cortisol can be used as “biomarkers” of social support and environmental stress. For socially-situated artificial agents, we suggest that these “social buffering” mechanisms can adapt the (adaptive) stress mechanisms, but that the long-term efficacy of this adaptation is related to the temporal dynamics of social interactions and the contextual perception of the affective social and physical environments.

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Audio-Visual Stimuli Change not Only Robot’s Hug Impressions but Also Its Stress-Buffering Effects

Cited by 17 publications

References 37 publications

Human Factors Considerations for Quantifiable Human States in Physical Human–Robot Interaction: A Literature Review

Human Factors Considerations for Quantifiable Human States in Physical Human–Robot Interaction: A Literature Review

Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

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