Do Audio-Visual Stimuli Change Hug Impressions?

Shiomi, Masahiro; Hagita, Norihiro

doi:10.1007/978-3-319-70022-9_34

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…Their studies include teaching [54] and playing with the robot [27,55]. Meanwhile, Shiomi et al used a female-looking Android to investigate the effects of its subtle reactions to touch [56,57] and the impact of the pre-touch reaction distance on humans [58].…”

Section: Mapping Scheme For Phrimentioning

confidence: 99%

“…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%

“…Following this, several studies explored the relationship between cognitive ergonomics and belief, such as [19,27,37,38,54,63,95,118,124]. A fewer number of studies examined cognitive ergonomics with physical ergonomics, including [22,24,95,118] and physical ergonomics with belief, such as [57,69,95,118]. Lastly, it was found that the simultaneous consideration of affect and cognitive ergonomics within the same experiment was relatively rare.…”

Section: Emotional Statementioning

confidence: 99%

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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%

Section: Mapping Scheme For Phrimentioning

confidence: 99%

Section: Emotional Statementioning

confidence: 99%

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

Abdulazeem,

2023

Sensors

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“…Finally, as described in section Touch Interaction , touch behavior brings positive or negative meaning to people even if touchers are social robots; moreover, we also need to consider the effects of behavior change effects as well as perceived impressions. Because related works in human-robot touch interaction reported positive effects of robots’ touches toward people’s behaviors, such as improved motivation for monotonous tasks ( Shiomi et al, 2017a ), persuasion ( Bevan and Stanton Fraser, 2015 ), encouraging self-disclosure and pro-social behaviors ( Shiomi et al, 2017b ; 2020b ), and stress-buffering ( Shiomi and Hagita, 2017 ), similar to human-human touch interactions. Since these studies were conducted with relatively young participants and not PwDs, additional evaluations with PwDs under Humanitude contexts are critical to apply such knowledge in care contexts.…”

Section: Technical Challenges Toward Artificial Systems That Incorporate Humanitude Techniquesmentioning

confidence: 99%

Technical Challenges for Smooth Interaction With Seniors With Dementia: Lessons From Humanitude™

et al. 2021

Self Cite

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Due to cognitive and socio-emotional decline and mental diseases, senior citizens, especially people with dementia (PwD), struggle to interact smoothly with their caregivers. Therefore, various care techniques have been proposed to develop good relationships with seniors. Among them, Humanitude is one promising technique that provides caregivers with useful interaction skills to improve their relationships with PwD, from four perspectives: face-to-face interaction, verbal communication, touch interaction, and helping care receivers stand up (physical interaction). Regardless of advances in elderly care techniques, since current social robots interact with seniors in the same manner as they do with younger adults, they lack several important functions. For example, Humanitude emphasizes the importance of interaction at a relatively intimate distance to facilitate communication with seniors. Unfortunately, few studies have developed an interaction model for clinical care communication. In this paper, we discuss the current challenges to develop a social robot that can smoothly interact with PwDs and overview the interaction skills used in Humanitude as well as the existing technologies.

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“…Touch interaction with other people provides various positive effects [1][2][3][4][5][6], but, due to the COVID-19 pandemic, such interaction is physically and socially limited in the world. In this context, using social robots as a partner of touch interaction is a promising approach to cover the lack of human-human touch interaction since past studies reported several positive effects of such interaction [7][8][9][10][11][12][13][14][15]. Even if the effects of touch interaction with such social robots fail to fulfill the needs of interaction with actual people, people's negative situations can be mitigated.…”

Section: Introductionmentioning

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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Do Audio-Visual Stimuli Change Hug Impressions?

Cited by 7 publications

References 15 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

Technical Challenges for Smooth Interaction With Seniors With Dementia: Lessons From Humanitude™

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

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