UE-HRI: a new dataset for the study of user engagement in spontaneous human-robot interactions

Ben-Youssef, Atef; Clavel, Chloé; Essid, Slim; Bilac, Miriam; Chamoux, Marine; Lim, Angelica

doi:10.1145/3136755.3136814

Cited by 79 publications

(27 citation statements)

References 15 publications

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“…As stated in [1, p.1]: "Engagement is a category of user experience characterized by attributes of challenge, positive affect, endurability, aesthetic and sensory appeal, attention, feedback, variety/novelty, interactivity, and perceived user control". Studies that have examined the aspect of engagement in HRI used both explicit [e.g., [2][3][4][5] and implicit measures [6][7][8][9][10][11][12][13]. Explicit measures and questionnaireswhile providing valuable hints regarding the phenomenon of interest, suffer from several limitations.…”

Section: A Measuring Engagement In Hrimentioning

confidence: 99%

Measuring engagement elicited by eye contact in Human-Robot Interaction

Kompatsiari¹,

Ciardo²,

D³

et al. 2019

Preprint

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The present study aimed at investigating how eye contact established by a humanoid robot affects engagement in human-robot interaction (HRI). To this end, we combined explicit subjective evaluations with implicit measures, i.e. reaction times and eye tracking. More specifically, we employed a gaze cueing paradigm in HRI protocol involving the iCub robot. Critically, before moving its gaze, iCub either established eye contact or not with the user. We investigated the patterns of fixations of participants’ gaze on the robot’s face, joint attention and the subjective ratings of engagement as a function of eye contact or no eye contact. We found that eye contact affected implicit measures of engagement, i.e. longer fixation times on the robot’s face during eye contact, and joint attention elicited only after the robot established eye contact. On the contrary, explicit measures of engagement with the robot did not vary across conditions. Our results highlight the value of combining explicit with implicit measures in an HRI protocol in order to unveil underlying human cognitive mechanisms, which might be at stake during the interactions. These mechanisms could be crucial for establishing an effective and engaging HRI, and could potentially provide guidelines to the robotics community with respect to better robot design.

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Section: A Measuring Engagement In Hrimentioning

confidence: 99%

Measuring engagement elicited by eye contact in Human-Robot Interaction

Kompatsiari¹,

Ciardo²,

D³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Table 1. A comparison of the MoCA dataset with existing benchmarks: HMDB 3 , Activitynet 1 , HACS 4 , Kinectics-700 2 , UCF 101 5 , MPII Cooking 2 6 , EPIC-Kitchens 11 , You Cook 2 35 , Arbitrary view 7 , IXMAS 8 , NUCLA 9 , NTU 10 , Schreiber & Moissenet 12 , Fukuchi et al 13 , UE-HRI 36 , CMU-MMAC 37 , TUM Kitchen 38 , Ego Yale 39 . The column View(s) setup reports info on the setup referring to the camera setup, that may include different fixed cameras (nV, where n is the number of cameras), may have no specific constraint on the mutual position between camera and subject (we named Free Viewpoint -FV), or may consider the use of a wearable ego camera (referred to as Ego).…”

Section: Background and Summarymentioning

confidence: 99%

The MoCA dataset, kinematic and multi-view visual streams of fine-grained cooking actions

et al. 2020

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MoCA is a bi-modal dataset in which we collect Motion Capture data and video sequences acquired from multiple views, including an ego-like viewpoint, of upper body actions in a cooking scenario. It has been collected with the specific purpose of investigating view-invariant action properties in both biological and artificial systems. Besides that, it represents an ideal test bed for research in a number of fields – including cognitive science and artificial vision – and application domains – as motor control and robotics. Compared to other benchmarks available, MoCA provides a unique compromise for research communities leveraging very different approaches to data gathering: from one extreme of action recognition in the wild – the standard practice nowadays in the fields of Computer Vision and Machine Learning – to motion analysis in very controlled scenarios – as for motor control in biomedical applications. In this work we introduce the dataset and its peculiarities, and discuss a baseline analysis as well as examples of applications for which the dataset is well suited.

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“…This sets it apart from existing engagement models that do not take into account interparty dynamics. Our work is also related to [5] in that we tackle the same task, adopt the same problem formulation introduced by the authors, and evaluate our model on the same public data set, the UE-HRI [20]. Unlike [5] where the results are reported on a rebalanced test set, however, we evaluate our model on test sets that reflect the real data distribution.…”

Section: Positioningmentioning

confidence: 99%

HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection

Atamna¹,

Clavel²

2020

Interspeech 2020

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Natural and fluid human-robot interaction (HRI) systems rely on the robot's ability to accurately assess the user's engagement in the interaction. Current HRI systems for engagement analysis, and more broadly emotion recognition, only consider user data while discarding robot data which, in many cases, affects the user state. We present a novel recurrent neural architecture for online detection of user engagement decrease in a spontaneous HRI setting that exploits the robot data. Our architecture models the user as a distinct party in the conversation and uses the robot data as contextual information to help assess engagement. We evaluate our approach on a real-world highly imbalanced data set, where we observe up to 2.13% increase in F1 score compared to a standard gated recurrent unit (GRU).

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UE-HRI: a new dataset for the study of user engagement in spontaneous human-robot interactions

Cited by 79 publications

References 15 publications

Measuring engagement elicited by eye contact in Human-Robot Interaction

Measuring engagement elicited by eye contact in Human-Robot Interaction

The MoCA dataset, kinematic and multi-view visual streams of fine-grained cooking actions

HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection

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