You Are Doing Great! Only One Rep Left: An Affect-Aware Social Robot for Exercising

Shao, Mingyang; Alves, Silas Franco dos Reis; Ismail, Omar; Zhang, Xinyi; Nejat, Goldie; Benhabib, B.

doi:10.1109/smc.2019.8914198

Cited by 29 publications

(28 citation statements)

References 31 publications

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“…Most of the studies that fall into this stream focus on a single country (see for details Web-Appendix 3) in Europe (e.g., Austria [265], France [40, 68,190], Germany [75,95,105,110,111,170,236], the Netherlands [98], Portugal [224], Switzerland [76], and the United Kingdom [16,46,174]), Asia (e.g., China [141,223], India [215,241], Japan [123,124,131,229,251,254,295], Korea [187], and Taiwan [102]), or America (e.g., United States [22,29,115,117,183,256] and Mexico [214]). Only four studies investigate humans' recognition of robotic emotions across multiple countries [19,55,82,215].…”

Section: Geographical Originsmentioning

confidence: 99%

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Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Stock‐Homburg

2021

Int J of Soc Robotics

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Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.

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Section: Geographical Originsmentioning

confidence: 99%

“…Disciplines. For their foundation, these studies rely on contributions from robotics [20,40,55,82,187,214,223,224,242], human-computer interaction [253,254,256,295], HRI [29, 75,105,110,123,170,183,215,229], and social robotics [16,76,179,265]. One study is rooted in neuroscience [68].…”

Section: Geographical Originsmentioning

confidence: 99%

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Stock‐Homburg

2021

Int J of Soc Robotics

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“…In Shao et al ( 2019 ), the authors employed the Softbank Pepper robot as an autonomous exercise facilitator to encourage the user during the physical activity that can autonomously adapt its emotional behaviors on the basis of user affect. In particular, valence detection was performed by analyzing EEG from a commercial-grade device.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

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A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

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“…Shao et al ( 2019 ) used a low-cost EEG (InteraXon Muse 2016) together with heart rate and motion sensors during interaction with a health coach humanoid robot. They extracted EEG frequency band powers in order to classify the emotional valence of the user during exercise with the robot.…”

Section: Bcis and Human-robot Interactionmentioning

confidence: 99%

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Alimardani

Hiraki

2020

Front. Robot. AI

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Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.

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You Are Doing Great! Only One Rep Left: An Affect-Aware Social Robot for Exercising

Cited by 29 publications

References 31 publications

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

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