Attention Detection in Elderly People-Robot Spoken Interaction

Sehili, Mohamed El Amine; Yang, Fan; Devillers, Laurence

doi:10.1145/2666499.2666502

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…We can confirm the finding by Sehili et al (2014) for a positive relationship between neuroticism and an anthropomorphic perception of the robot (r 0.22, p 0.035). In contrast to Looije et al (2010), we cannot confirm any relationship between self-reported conscientiousness of a participant and the perceived sociability of a robot.…”

Section: Population Statisticssupporting

confidence: 84%

An Immersive Investment Game to Study Human-Robot Trust

et al. 2021

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As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilize two Neuro-Inspired COmpanion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p=0.02) between self-assessed trust and trust measured from the game, and a positive impact of non-verbal communication on trust (p=0.0008) and robot perception for anthropomorphism (p=0.007) and animacy (p=0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human’s trust in robots.

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Section: Population Statisticssupporting

confidence: 84%

An Immersive Investment Game to Study Human-Robot Trust

et al. 2021

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“…The limitation in this study is that it has been done only with the available data of the students through a multimedia slide presentation. Voice examination and the head-turning system were introduced by the author in the paper [29], where the author used a mixture of when speaking among persons, where the detection of head-turning sector's success rate was 87.7%. The ROMEO2 project has been implemented to create a human-robot interaction system whose job is to work as an assistant to the people with loss of autonomy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method.

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“…Other studies have examined joint attention between human user and robot during spoken interaction [15] [16] [17]. In [18], an audio-visual social interaction corpus was collected and annotated to investigate behaviour patterns of robot users and their attention while interacting with the robot. More recently, the timing of interaction has been explored to design robots that can embody these behavioural resources.…”

Section: B User Interfacesmentioning

confidence: 99%

Temporal patterns in multi-modal social interaction between elderly users and service robot

Wang

Nuovo

Cangelosi

et al. 2019

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Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). There has been little research on user preference towards HRI interfaces. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of the participants, including their attentional activities and conversational activities, the temporal characteristics of these social behaviours, as well as questionnaires. It has been revealed that social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life.

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Attention Detection in Elderly People-Robot Spoken Interaction

Cited by 4 publications

References 10 publications

An Immersive Investment Game to Study Human-Robot Trust

An Immersive Investment Game to Study Human-Robot Trust

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Temporal patterns in multi-modal social interaction between elderly users and service robot

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