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1. Motor Skill Improvement Using Compressive Garments in a Child with Multidimensional Impairments and Ehlers–Danlos Syndrome

Author

Jean Xavier, Soizic Gauthier, Ingrid Zammouri, Salvatore Maria Anzalone, and David Cohen

Subjects
Psychiatry, RC435-571
Abstract

We report the case of an 8-year-old child with a complex neurodevelopmental disorder, including severe developmental coordination disorder with dysgraphia, anxiety and depression, mild social functioning impairments, headache and chronic musculoskeletal pain, secondary to Ehlers–Danlos syndrome (EDS) hypermobility type. We explored whether wearing whole-body compressive garments (CGs) could improve his motor skills assessed through standardized and experimental procedures. In addition to the effectiveness of CGs on pain, we found partial improvements in his motor skills, specifically postural control, hand movements, and body schema representation, after wearing CGs for 15 days. During an experimental motor imitation task with a virtual tightrope walker, we found improvements in interpersonal synchronization with performances closer to those of typical developing (TD) controls. We conclude that CGs appear to be an innovative and interesting adjuvant treatment for motor skill impairments in children with multidimensional impairments involving EDS. These promising results require confirmation by further evidence-based research.

Published
2022
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2. Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement

Author

Serena eIvaldi, Salvatore Maria Anzalone, Woody eRousseau, Olivier eSigaud, and Mohamed eChetouani

Subjects
Social learning, engagement, joint attention, human-robot interaction, Knowledge sharing, Humanoids, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

We hypothesize that the initiative of a robot during a collaborative task with a human can influence the pace of interaction, the human response to attention cues, and the perceived engagement.We propose an object learning experiment where the human interacts in a natural way with the humanoid iCub. Through a two-phases scenario, the human teaches the robot about the properties of some objects.We compare the effect of the initiator of the task in the teaching phase (human or robot) on the rhythm of the interaction in the verification phase. We measure the reaction time of the human gaze when responding to attention utterances of the robot. Our experiments show that when the robot is the initiator of the learning task, the pace of interaction is higher and the reaction to attention cues faster.Subjective evaluations suggest that the initiating role of the robot, however, does not affect the perceived engagement. Moreover, subjective and third-person evaluations of the interaction task suggest that the attentive mechanism we implemented in the humanoid robot iCub is able to arouse engagement and make the robot's behavior readable.

Published
2014
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15. Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent

Author

Mohamed Chetouani, Liliana Lo Presti, Salvatore Maria Anzalone, Alain Berthoz, Mohamed Zaoui, David Cohen, Soizic Gauthier, Jean Xavier, Marco La Cascia, Vito Monteleone, Centre de Recherches sur la Cognition et l'Apprentissage (CeRCA), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Université de Poitiers, Sorbonne Université (SU), Perception, Interaction, Robotique sociales (PIROS), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Vito Monteleone, Liliana Lo Presti, Marco La Cascia, S. Gauthier, J. Xavier, M. Zaoui, A. Berthoz, D. Cohen, M. Chetouani, S. Anzalone, Université de Poitiers-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherches Psychanalyse, Médecine et Société (CRPMS (EA_3522)), Université Paris Diderot - Paris 7 (UPD7), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Collège de France (CdF (institution)), French National Centre for Scientific Research, Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)

Subjects
Computer science, Semi-automated annotation, Interpersonal communication, Human Behavior, Machine learning, computer.software_genre, Human behavior, Task (project management), [SCCO]Cognitive science, Annotation, Artificial Intelligence, Change-point, Psychology, Training, ComputingMilieux_MISCELLANEOUS, Psychiatry, Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, business.industry, Manual, Computational modeling, Social agents, Dynamics (music), Task analysis, Tool, Task analysi, Artificial intelligence, business, computer, Software, Change detection
Abstract

The study of human behaviors in cognitive sciences provides clues to understand and describe people’s personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this paper proposes a methodology to semi-automatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change-points. This paper describes the necessary steps to detect and classify change-points in behavioral data by using a dataset collected in a real use-case scenario. This dataset includes motor observations from children with typical development and with neuro-developmental disorders. Abnormal movements which are present in such disorders are useful to validate the system in conditions that are challenging even for experienced annotators. Results show that the system: can be effective in the semi-automated annotation task; can be efficient in presence of abnormal behaviors; may achieve good performance when trained with limited manually annotated data.

Published
2021

25. A BCI Teleoperated Museum Robotic Guide.

Author

Antonio Chella, Enrico Pagello, Emanuele Menegatti, Rosario Sorbello, Salvatore Maria Anzalone, Francesco Cinquegrani, Luca Tonin, Francesco Piccione, Konstantinos Prifitis, Claudia Blanda, Evelina Buttita, and Emanuela Tranchina

Published
2009
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28. A Robot-Mediated Activity Using the Nao Robot to Promote COVID-19 Precautionary Measures among Older Adults in Geriatric Facilities

Author

Lauriane Blavette, Anne-Sophie Rigaud, Salvatore Maria Anzalone, Clément Kergueris, Baptiste Isabet, Sébastien Dacunha, and Maribel Pino

Subjects
Health Personnel, Surveys and Questionnaires, Health, Toxicology and Mutagenesis, Physical Distancing, Public Health, Environmental and Occupational Health, older adults, social robot, COVID-19, precautionary measures, health promotion, engagement, Humans, Robotics, Aged
Abstract

Precautionary measures (e.g., social distancing, mask wearing, washing hands regularly) to limit the transmission of the Coronavirus 19 (COVID-19) have been put in place worldwide. However, a limited understanding of precautionary measures and low compliance with them has been observed in older adults with neurocognitive disorders, persons with intellectual disability, or mental illness. The objective of this study is to create and evaluate a robot-mediated activity to deliver information on COVID-19 precautionary measures in an accessible and engaging way using the humanoid robot Nao. An interactive scenario explaining and demonstrating COVID-19 precautionary measures is created using the verbal and non-verbal behaviours of the robot. The scenario (≈5 min) is presented to 124 users of a geriatric hospital, including the following: older patients (n = 45), accompanying persons (n = 39), and health professionals (n = 40). The data regarding perceived usefulness, acceptability, and accessibility are collected using a questionnaire. A video analysis of the participants’ behaviour during the interaction with the robot is performed to examine the quality of engagement in the activity. The results show a good acceptance, satisfaction, and perceived usefulness of the robot-mediated activity. These findings suggest that robot-mediated interventions using humanoid robots can be an effective tool for the delivery of health promotion information.

Published
2022
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29. Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review

Author

Thomas Gargot, Dominique Archambault, Mohamed Chetouani, David Cohen, Wafa Johal, Salvatore Maria Anzalone, Service de Psychiatrie de l'Enfant et de l'Adolescent [CHU Pitié-Salpêtrière] (SPEA), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Perception, Interaction, Robotique sociales (PIROS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), and University of New South Wales [Sydney] (UNSW)

Subjects
[SDV.MHEP.PED]Life Sciences [q-bio]/Human health and pathology/Pediatrics, Computational, Cognitive Neuroscience, Automatic, Movement, [SCCO.NEUR]Cognitive science/Neuroscience, Autism spectrum disorders, Clumsiness, Computer Science Applications, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, mental disorders, Diagnosis, ComputingMilieux_COMPUTERSANDSOCIETY, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Computer Vision and Pattern Recognition
Abstract

International audience; Autism spectrum disorder (ASD) is mainly described as a disorder of communication and socialization. However, motor abnormalities are also common in ASD. New technologies may offer quantitative and automatic metrics to measure movement difficulties. We sought to identify computational methods to automatize the assessment of motor impairments in ASD. We systematically searched for the terms ’autism’, ’movement’, ’automatic’, ’computational’ and ’engineering’ in IEEE (Institute of Electrical and Electronics Engineers), Medline and Scopus databases and reviewed the literature from inception to 2018. We included all articles discussing: (1) automatic assessment/new technologies, (2) motor behaviours and (3) children with ASD. We excluded studies that included patient’s or parent’s reported outcomes as online questionnaires that focused on computational models of movement, but also eye tracking, facial emotion or sleep. In total, we found 53 relevant articles that explored static and kinetic equilibrium, like posture, walking, fine motor skills, motor synchrony and movements during social interaction that can be impaired in individuals with autism. Several devices were used to capture relevant motor information such as cameras, 3D cameras, motion capture systems, accelerometers. Interestingly, since 2012, the number of studies increased dramatically as technologies became less invasive, more precise and more affordable. Open-source software has enabled the extraction of relevant data. In a few cases, these technologies have been implemented in serious games, like “Pictogram Room”, to measure the motor status and the progress of children with ASD. Movement computing opens new perspectives for patient assessment in ASD research, enabling precise characterizations in experimental and at-home settings, and a better understanding of the role of sensorimotor disturbances in the development of social cognition and ASD. These methods would likely enable researchers and clinicians to better distinguish ASD from other motors disorders while facilitating an improved monitoring of children’s progress in more ecological settings (i.e. at home or school).

Published
2022

30. Human-humanoid interaction and cooperation: a review

Author

Lorenzo Vianello, Luigi Penco, Waldez Gomes, Yang You, Salvatore Maria Anzalone, Pauline Maurice, Vincent Thomas, Serena Ivaldi, Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), This work was also supported by the Inria-DGA grant (humanoïde résilient'), and the Inria 'ADT' wbCub/wbTorque, ANR-18-CE33-0001,The_flying_coworker,L'équipier volant(2018), ANR-19-CE19-0029,iReCHeCk,iReCHeCk - Un robot compagnon pour apprendre l'écriture manuscrite(2019), European Project: 731540,H2020,An.Dy(2017), European Project: 637972,H2020 ERC,ERC-2014-STG,ResiBots(2015), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris Nanterre (UPN)-Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Université Paris 8 Vincennes-Saint-Denis (UP8)-École Pratique des Hautes Études (EPHE)

Subjects
0209 industrial biotechnology, human-robot interaction, 020901 industrial engineering & automation, 05 social sciences, cooperation, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, 02 engineering and technology, General Medicine, humanoid robots, 050107 human factors
Abstract

International audience; Humanoid robots are versatile platforms with the potential to assist humans in several domains, from education to healthcare, from entertainment to the factory of the future. To find their place into our daily life, where complex interactions and collaborations with humans are expected, their social and physical interaction skills need to be further improved. Recent findings: The hallmark of humanoids is their anthropomorphic shape, which facilitates the interaction but at the same time increases the expectations of the human in terms of advanced cooperation capabilities. Cooperation with humans requires an appropriate modeling and real-time estimation of the human state and intention. This information is required both at a high-level by the cooperative decision-making policy and at a low-level by the interaction controller that implements the physical interaction. Real-time constraints induce simplified models that limit the decision capabilities of the robot during cooperation.

Published
2021

31. P.114 Automatic assessment of motors impairments in autism spectrum disorders: a systematic review

Author

David Cohen, Salvatore Maria Anzalone, Dominique Archambault, Mohamed Chetouani, Wafa Johal, and Thomas Gargot

Subjects
Pharmacology, medicine.medical_specialty, business.industry, Audiology, medicine.disease, Spectrum (topology), Psychiatry and Mental health, Neurology, Medicine, Autism, Pharmacology (medical), Neurology (clinical), business, Biological Psychiatry
Published
2020

32. Quantifying patterns of joint attention during human-robot interactions: An application for autism spectrum disorder assessment

Author

Mohamed Chetouani, Filippo Muratori, David Cohen, Salvatore Maria Anzalone, Sofiane Boucenna, Lucia Billeci, Antonio Narzisi, Jean Xavier, Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Centre National de la Recherche Scientifique (CNRS), Scientific Institute ‘Stella Maris', Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), and CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)

Subjects
Autism spectrum disorder, Behavioural analysis, Social robotics, Software, Signal Processing, 1707, Artificial Intelligence, Joint attention, 02 engineering and technology, 01 natural sciences, Human–robot interaction, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, medicine, 010306 general physics, Set (psychology), ComputingMilieux_MISCELLANEOUS, Social robot, medicine.disease, Robot, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Psychology, Humanoid robot, Cognitive psychology
Abstract

In this paper we explore the dynamics of Joint Attention (JA) in children with Autism Spectrum Disorder (ASD) during an interaction task with a small humanoid robot. While this robot elicits JA in children, a coupled perception system based on RGB-D sensors is able to capture their behaviours. The proposed system shows the feasibility and the practical benefits of the use of social robots as assessment tools of ASD. We propose a set of measures to describe the behaviour of the children in terms of body and head movements, gazing magnitude, gazing directions (left vs. front vs. right) and kinetic energies. We assessed these metrics by comparing 42 children with ASD and 16 children with typical development (TD) during the JA task with the robot, highlighting significant differences between the two groups. Employing the same metrics, we also assess a subgroup of 14 children with ASD after 6-month of JA training with a serious game. The longitudinal data confirms the relevance of the proposed metrics as they reveal the improvements of children behaviours after several months of training.

Published
2018

33. Autisme, jeux sérieux et robotique : réalité tangible ou abus de langage ?

Author

Salvatore Maria Anzalone, Jean Xavier, Sofiane Boucenna, David Cohen, Charline Grossard, Laurence Chaby, Ouriel Grynszpan, Mohamed Chetouani, Université de Paris – UFR Institut de psychologie [Sociétés et Humanités] (UP UFR Psychologie), Université de Paris (UP), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Psychiatrie de l'Enfant et de l'Adolescent [CHU Pitié-Salpêtrière] (SPEA), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université - UFR d'Ingénierie (UFR 919), and Sorbonne Université (SU)

Subjects
[SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, 03 medical and health sciences, Psychiatry and Mental health, 0302 clinical medicine, Arts and Humanities (miscellaneous), [SCCO.PSYC]Cognitive science/Psychology, 0501 psychology and cognitive sciences, Sociology, Humanities, 030217 neurology & neurosurgery, Applied Psychology, ComputingMilieux_MISCELLANEOUS, 050104 developmental & child psychology
Abstract

Resume Le nombre d’etudes sur l’utilisation des technologies de l’information, de la communication et de la robotique au service des enfants autistes a progresse rapidement au cours des 15 dernieres annees. Nous detaillons les espoirs, mais aussi les ecueils et les verrous technologiques des deux domaines les plus dynamiques, a savoir : (1) la realisation de jeux serieux destines a entrainer des habiletes specifiques (reconnaissance des emotions, interactions sociales) ; (2) les interactions avec des robots. Nous prendrons plus particulierement deux exemples a partir de projets auxquels nous collaborons ou avons directement collabore, JeMiMe pour les jeux serieux, et Michelangelo pour la robotique. Nous concluons que si nous sommes convaincus de l’impact potentiel de ces methodes dans la prise en charge des enfants autistes, les limites sont encore importantes et la validite clinique tres limitee.

Published
2017

34. Automated prediction of Extraversion during Human-Humanoid interaction

Author

Giovanna Varni, Mohamed Chetouani, Salvatore Maria Anzalone, Serena Ivaldi, Technologies, Handicaps, Interfaces et Multimodalités (CHART-THIM), Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), European Project: 600716,EC:FP7:ICT,FP7-ICT-2011-9,CODYCO(2013), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris Nanterre (UPN)-Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris Nanterre (UPN)-Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Université Paris 8 Vincennes-Saint-Denis (UP8)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris 8 Vincennes-Saint-Denis (UP8)-École Pratique des Hautes Études (EPHE), and École pratique des hautes études (EPHE)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-École pratique des hautes études (EPHE)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)

Subjects
0209 industrial biotechnology, General Computer Science, Social Psychology, media_common.quotation_subject, 02 engineering and technology, Human behavior, 050105 experimental psychology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Nonverbal communication, 020901 industrial engineering & automation, Personality, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, Electrical and Electronic Engineering, Big Five personality traits, Set (psychology), media_common, Social robot, Extraversion and introversion, business.industry, 05 social sciences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Robotics, Human-Computer Interaction, Philosophy, Control and Systems Engineering, Artificial intelligence, business, Psychology, Social psychology, Cognitive psychology
Abstract

International audience; One of the most important challenges in robotics is to achieve natural and engaging interactions between humans and robots. Interpersonal interaction, the process by which two or more people exchange information through verbal and nonverbal messages, could be exploited by robots both to establish interaction and to inform about its quality. The production of social signals being influenced by individual factors, such as personality traits, is critical for robots to have personalized models of interaction.This paper focuses on estimating social traits such as human personality from the dynamics of interpersonal interaction. The work was carried out in the framework of the Project EDHHI (Engagement During Human-Humanoid Interaction) which focuses on understanding individual factors influencing joint human-humanoid actions. In particular, this work addresses the automated prediction of the Extraversion trait during a human-humanoid interaction. We show how it is possible to take into account the specificity of Human-Robot Interaction (HRI) scenarios by contextualizing personality through the attitude that participants show towards the robots. This attitude is influenced by the a priori knowledge that people have on social robotics, and their prior anxiety in interacting with them. The proposed model exploits a set of nonverbal features chosen according to literature in Psychology and Personality Computing. These features are adopted to characterize human behaviors and the dynamics of human-robot interaction.Experimental results highlight that it possible to predict Extraversion of the human partner from nonverbal behavior during human-robot interaction with an accuracy of 62%. A higher accuracy, 70%, is obtained from the computational model by explicitly combining the dynamics of interpersonal interaction and the attitude of the participants towards the robots. Results show that 120s are needed to obtain such performance.

Published
2017

35. A distributed perception infrastructure for robot assisted living

Author

Stefano Ghidoni, Salvatore Maria Anzalone, Stefano Michieletto, Matteo Munaro, and Emanuele Menegatti

Subjects
Ambient intelligence, business.industry, Computer science, General Mathematics, media_common.quotation_subject, Real-time computing, Facial recognition system, Computer Science Applications, Software, Control and Systems Engineering, Perception, Middleware, Robot, Computer vision, Artificial intelligence, business, Assisted living, media_common
Abstract

This paper presents an ambient intelligence system designed for assisted living. The system processes the audio and video data acquired from multiple sensors spread in the environment to automatically detect dangerous events and generate automatic warning messages. The paper presents the distributed perception infrastructure that has been implemented by means of an open-source software middleware called NMM. Different processing nodes have been developed which can cooperate to extract high level information about the environment. Examples of implemented nodes running algorithms for people detection or face recognition are presented. Experiments on novel algorithms for people fall detection and sound classification and localization are discussed. Eventually, we present successful experiments in two test bed scenarios.

Published
2014

36. How children with autism spectrum disorder behave and explore the 4-dimensional (spatial 3D+time) environment during a joint attention induction task with a robot

Author

Salvatore Maria Anzalone, Sofiane Boucenna, Mohamed Chetouani, Anne-Lise Jouen, Nicolas Bodeau, Elodie Tilmont, David Cohen, Koushik Maharatna, Jean Xavier, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Sorbonne Université (SU), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Psychiatrie de l'Enfant et de l'Adolescent [CHU Pitié-Salpêtrière] (SPEA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP]

Subjects
Nao robot, Joint attention, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), Developmental psychology, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Developmental and Educational Psychology, medicine, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, ComputingMilieux_MISCELLANEOUS, Social robot, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Perception system, medicine.disease, Gaze, Psychiatry and Mental health, Clinical Psychology, Autism spectrum disorder, [SCCO.PSYC]Cognitive science/Psychology, Robot, Psychology, 030217 neurology & neurosurgery, 050104 developmental & child psychology, Cognitive psychology
Abstract

We aimed to compare, during a joint attention (JA) elicitation task, how children with autism spectrum disorder (ASD) and children with typical development (TD) behave and explore their 4 dimensional (meaning spatial 3D + time) when interacting with a human or with a robotic agent. We built a system that employed a Nao robot and a perception system based on a RGB-D sensor (Kinect) to capture social engagement cues. A JA induction experiment was performed in which children with ASD ( N = 16) and matched TD children ( N = 16) had a 3-min interaction with the robot or with a therapist. Nao induced JA by gazing; by gazing and pointing; and by gazing, pointing and vocalizing at pictures. Both groups of children performed well with the therapist. However, with Nao, both groups had lower JA scores, and the children with ASD had a significantly lower score than the TD children. We found that (i) multimodal JA induction was more efficient in both groups; (ii) the 3D spatial world gaze exploration showed less accuracy; and (iii) the trunk position in ASD showed less stability in the 4 dimensions compared to TD controls. We conclude that, in ASD, JA skill depends on the interaction partner, and implies a higher motor and cognitive cost.

Published
2014

37. An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Author

Alessandro Tonacci, Antonio Narzisi, Simone Di Palma, Sara Calderoni, Salvatore Maria Anzalone, Giovanni Baldus, Gennaro Tartarisco, Giovanni Pioggia, Filippo Muratori, Federico Cruciani, Daniele Corda, and Lucia Billeci

Subjects
medicine.medical_specialty, media_common.quotation_subject, Autism Spectrum Disorders (ASD), Electrocardiogram (ECG), Imitation, Monitoring, Naturalistic, Personalization, Quantitative EEG (QEEG), Wearable sensors, Neuroscience (all), Electroencephalography, naturalistic, 050105 experimental psychology, imitation, quantitative EEG (QEEG), 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, medicine, Heart rate variability, 0501 psychology and cognitive sciences, Disengagement theory, personalization, Wearable technology, Original Research, media_common, medicine.diagnostic_test, business.industry, wearable sensors, General Neuroscience, 05 social sciences, Cognition, Neurophysiology, medicine.disease, electrocardiogram (ECG), monitoring, Autism, Psychology, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs.

Published
2016

38. International workshop on social learning and multimodal interaction for designing artificial agents (workshop summary)

Author

Ginevra Castellano, Salvatore Maria Anzalone, Angelica Lim, Giovanna Varni, Gentiane Venture, Isabelle Hupont Torres, Mohamed Chetouani, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), InfoMus Lab, DIST-Universita di Genova, Tokyo University of Agriculture and Technology (TUAT), and Università degli studi di Genova = University of Genoa (UniGe)

Subjects
Social robot, Computer science, Field (Bourdieu), [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SCCO.COMP]Cognitive science/Computer science, 020207 software engineering, 02 engineering and technology, Social learning, Social agents, Multimodal interaction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Human–computer interaction, [SCCO.PSYC]Cognitive science/Psychology, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Affective computing, ComputingMilieux_MISCELLANEOUS
Abstract

The “social learning and multimodal interaction for designing artificial agents” workshop aims at presenting scientific and philosophical advances related to social learning and multimodal interaction for enhancing the design of artificial agents. Papers presented in the workshop include studies on human behavior modeling, on social robotics and on virtual agents. Our two invited speakers, Prof. Catherine Pelachaud and Prof. Louis-Philippe Morency will enrich and open the door to further discussion by bringing their widely acknowledged expertise in the field.

Published
2016

39. Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

Author

Giovanna Varni, Serena Ivaldi, Faezeh Rahbar, Mohamed Chetouani, Salvatore Maria Anzalone, Elisabetta Zibetti, Institut des Systèmes Intelligents et Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), InfoMus Lab, DIST-Universita di Genova, Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Università degli studi di Genova = University of Genoa (UniGe), Université Paris 8 Vincennes-Saint-Denis (UP8)-École Pratique des Hautes Études (EPHE), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), École pratique des hautes études (EPHE)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), and Ivaldi, Serena

Subjects
Extraversion and introversion, media_common.quotation_subject, Human - robot interaction, Human–robot interaction, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Nonverbal communication, [SPI.AUTO] Engineering Sciences [physics]/Automatic, personality, [SCCO.PSYC] Cognitive science/Psychology, [SCCO.PSYC]Cognitive science/Psychology, Robot, Personality, non - verbal behaviour, Big Five personality traits, Psychology, Set (psychology), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Social psychology, iCub, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Cognitive psychology, media_common
Abstract

International audience; In this paper we present a system for automatic prediction of extraversion during the first thin slices of human-robot interaction (HRI). This work is based on the hypothesis that personality traits and attitude towards robot appear in the behavioural response of humans during HRI. We propose a set of four non-verbal movement features that characterize human behavior during interaction. We focus our study on predicting Extraversion using these features , extracted from a dataset consisting of 39 healthy adults interacting with the humanoid iCub. Our analysis shows that it is possible to predict to a good level (64%) the Extraversion of a human from a thin slice of interaction relying only on non-verbal movement features. Our results are comparable to the state-of-the-art obtained in HHI [ 23 ] .

Published
2015

40. Evaluating the Engagement with Social Robots

Author

Mohamed Chetouani, Salvatore Maria Anzalone, Sofiane Boucenna, Serena Ivaldi, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Technische Universität Darmstadt (TU Darmstadt), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Technische Universität Darmstadt - Technical University of Darmstadt (TU Darmstadt)

Subjects
Personal robot, Social robot, General Computer Science, Social Psychology, Social intelligence, business.industry, Robotics, Social relation, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Human-Computer Interaction, Philosophy, Control and Systems Engineering, Human–computer interaction, Robot, Artificial intelligence, Electrical and Electronic Engineering, Media Lab Europe's social robots, business, Psychology, iCub
Abstract

International audience; To interact and cooperate with humans in their daily-life activities, robots should exhibit human-like " intelligence ". This skill will substantially emerge from the interconnection of all the algorithms used to ensure cognitive and interaction capabilities. While new robotics technologies allow us to extend such abilities, their evaluation for social interaction is still challenging. The quality of a human-robot interaction can not be reduced to the evaluation of the employed algorithms: we should integrate the engagement information that naturally arises during interaction in response to the robot's behaviors. In this paper we want to show a practical approach to evaluate the engagement aroused during interactions between humans and social robots. We will introduce a set of metrics useful in direct, face to face scenarios, based on the behaviors analysis of the human partners. We will show how such metrics are useS. M. Anzalone · M. Chetouani Sorbonne Universités , ful to assess how the robot is perceived by humans and how this perception changes according to the behaviors shown by the social robot. We discuss experimental results obtained in two human-interaction studies, with the robots Nao and iCub respectively.

Published
2015

41. IMI2S: A Lightweight Framework for Distributed Computing

Author

Marie Avril, Salvatore Maria Anzalone, Hanan Salam, Mohamed Chetouani, Institut des Systèmes Intelligents et de Robotique (ISIR), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Resource-oriented architecture, business.industry, Computer science, Distributed computing, [SCCO.NEUR]Cognitive science/Neuroscience, Software development, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SCCO.COMP]Cognitive science/Computer science, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Distributed design patterns, Software framework, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Software construction, Component-based software engineering, [SCCO.PSYC]Cognitive science/Psychology, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Software system, business, computer, Software design description, ComputingMilieux_MISCELLANEOUS
Abstract

An increasing number of applications require the integration of heterogeneous hardware and software components. Due to the high levels of complexity that such integrations demand, several solution have been proposed in the state of art of software engineering. This paper introduces the IMI2S framework: a distributed computing software platform aimed to cope with such levels of complexity by simplifying the functional decomposition of the problems through the implementation of highly decoupled, efficient and portable software. We will present the design issues addressed in the development of the IMI2S framework. We will show through two case studies its flexibility and its general efficacy.

Published
2014

42. Learning of Social Signatures Through Imitation Game Between a Robot and a Human Partner

Author

Salvatore Maria Anzalone, Elodie Tilmont, Mohamed Chetouani, Sofiane Boucenna, David Cohen, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, media_common.quotation_subject, education, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Normalized mutual information, Robot learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Typically developing, 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Human–computer interaction, medicine, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, Protocol (object-oriented programming), ComputingMilieux_MISCELLANEOUS, media_common, Artificial neural network, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, technology, industry, and agriculture, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], medicine.disease, body regions, Autism spectrum disorder, [SCCO.PSYC]Cognitive science/Psychology, Robot, Artificial intelligence, Imitation, Psychology, business, human activities, Software, 050104 developmental & child psychology
Abstract

In this paper, a robot learns different postures by imitating several partners. We assessed the effect of the type of partners, i.e., adults, typically developing (TD) children and children with autism spectrum disorder (ASD), on robot learning during an imitation game. The experimental protocol was divided into two phases: 1) a learning phase, during which the robot produced a random posture and the partner imitated the robot; and 2) a phase in which the roles were reversed and the robot had to imitate the posture of the human partner. Robot learning was based on a sensory-motor architecture whereby neural networks (N.N.) enabled the robot to associate what it did with what it saw. Several metrics (i.e., annotation, the number of neurons needed to learn, and normalized mutual information) were used to show that the partners affected robot learning. The first result obtained was that learning was easier with adults than with both groups of children, indicating a developmental effect. Second, learning was more complex with children with ASD compared to both adults and TD children. Third, learning with the more complex partner first (i.e., children with ASD) enabled learning to be more easily generalized.

Published
2014

43. Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement

Author

Olivier Sigaud, Serena Ivaldi, Salvatore Maria Anzalone, Mohamed Chetouani, Woody Rousseau, Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
Joint attention, Knowledge sharing, Computer science, Biomedical Engineering, [SCCO.COMP]Cognitive science/Computer science, Robot learning, 050105 experimental psychology, Human–robot interaction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], lcsh:RC321-571, Task (project management), human-robot interaction, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Human–computer interaction, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, Original Research Article, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, ComputingMilieux_MISCELLANEOUS, Communication, joint attention, Social learning, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Humanoids, 05 social sciences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Team learning, [SCCO.PSYC]Cognitive science/Psychology, Robot, business, 030217 neurology & neurosurgery, Humanoid robot, iCub, Neuroscience, engagement
Abstract

We hypothesize that the initiative of a robot during a collaborative task with a human can influence the pace of interaction, the human response to attention cues, and the perceived engagement.We propose an object learning experiment where the human interacts in a natural way with the humanoid iCub. Through a two-phases scenario, the human teaches the robot about the properties of some objects.We compare the effect of the initiator of the task in the teaching phase (human or robot) on the rhythm of the interaction in the verification phase. We measure the reaction time of the human gaze when responding to attention utterances of the robot. Our experiments show that when the robot is the initiator of the learning task, the pace of interaction is higher and the reaction to attention cues faster.Subjective evaluations suggest that the initiating role of the robot, however, does not affect the perceived engagement. Moreover, subjective and third-person evaluations of the interaction task suggest that the attentive mechanism we implemented in the humanoid robot iCub is able to arouse engagement and make the robot's behavior readable.

Published
2014

44. Multimodal People Engagement with iCub

Author

Salvatore Maria Anzalone, Mohamed Chetouani, Serena Ivaldi, Olivier Sigaud, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Personal robot, Communication, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Acoustic source localization, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SCCO.PSYC]Cognitive science/Psychology, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Robot, 020201 artificial intelligence & image processing, business, Psychology, ComputingMilieux_MISCELLANEOUS, iCub
Abstract

In this paper we present an engagement system for the iCub robot that is able to arouse in human partners a sense of “co-presence” during human-robot interaction. This sensation is naturally triggered by simple reflexes of the robot, that speaks to the partners and gazes the current “active partner” (e.g. the talking partner) in interaction tasks. The active partner is perceived through a multimodal approach: a commercial rgb-d sensor is used to recognize the presence of humans in the environment, using both 3d information and sound source localization, while iCub’s cameras are used to perceive his face.

Published
2013

45. A Topic Recognition System for Real World Human-Robot Conversations

Author

Salvatore Maria Anzalone, Hiroshi Ishiguro, Enrico Pagello, Rosario Sorbello, Emanuele Menegatti, Yuichiro Yoshikawa, Anzalone, MA, Yoshikawa, Y, Ishiguro, H, Menegatti, E, Pagello, E, and Sorbello, R

Subjects
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, Personal robot, Social robot, Computer science, media_common.quotation_subject, Topic Recognition System, Personal Robots,Human-Robot Interaction, Human–robot interaction, Personalization, Task (project management), Human–computer interaction, Conversation, Natural language, Humanoid robot, media_common
Abstract

One of the main features of social robots is the ability to communicate and interact with people as partners in a natural way. However, achieving a good verbal interaction is a hard task due to the errors on speech recognition systems, and due to the understanting the natural language itself. This paper tries to overcome such kind of problems by presenting a system that enables social robots to get involved in conversation by recognizing its topic. Through the use of classical text mining approach, the presented system allows social robots to understand topics of conversation between human partners, enabling the customization of behaviours in their accordance. The system has been evaluated in different contexts, taking in account the quality and accuracy of the speech recognition syestem used by the social robot.

Published
2013

46. Perception and human interaction for developmental learning of objects and affordances

Author

Salvatore Maria Anzalone, David Filliat, Alain Droniou, Damien Gerardeaux-Viret, Mohamed Chetouani, Natalia Lyubova, Serena Ivaldi, Olivier Sigaud, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Unité d'Électronique et d'informatique (UEI), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Social robot, Computer science, business.industry, 02 engineering and technology, Cognitive architecture, Robot learning, Human–robot interaction, Developmental robotics, 020901 industrial engineering & automation, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Affordance, business, iCub, Humanoid robot
Abstract

International audience; In this paper we describe a cognitive architecture for humanoids interacting with objects and caregivers in a developmental robotics scenario. The architecture is foundational to the MACSi project: it is designed to support experiments to make a humanoid robot gradually enlarge its repertoire of known objects and skills combining autonomous learning, social guidance and intrinsic motivation. This complex learning process requires the capability to learn affordances first. Here, we present the general framework for achieving these goals, focusing on the elementary action, perception and interaction modules. Preliminary experiments performed on the humanoid robot iCub are also discussed.

Published
2012

47. Towards Partners Profiling in Human Robot Interaction Contexts

Author

Enrico Pagello, Yuichiro Yoshikawa, Emanuele Menegatti, Rosario Sorbello, Salvatore Maria Anzalone, Hiroshi Ishiguro, Anzalone, SM, Yoshikawa, Y, Ishiguro, H, Menegatti, E, Pagello, E, and Sorbello, R

Subjects
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, Personal robot, Social robot, Computer science, business.industry, Human–robot interaction, Human–computer interaction, personal robots, Profiling (information science), Artificial intelligence, human robot interaction, profiling, business, profiling, personal robots, human robot interaction
Abstract

Individuality is one of the most important qualities of humans. Social robots should be able to model the individuality of the human partners and to modify their behaviours accordingly.This paper proposes a profiling system for social robots to be able to learn the individuality of human partners in social contexts. Profiles are expressed in terms of of identities and preferences bound together. In particular, people’s identity is captured by the use of facial features, while preferences are extracted from the discussion between the partners. Both are bound using an Hebb network. Experiments show the feasibility and the performances of the approach presented.

Published
2012

48. A Multimodal Distributed Intelligent Environment for a Safer Home

Author

Enrico Pagello, Salvatore Maria Anzalone, Stefano Ghidoni, and Emanuele Menegatti

Subjects
Ambient intelligence, Audio signal, Computer science, business.industry, Node (networking), Real-time computing, Video processing, computer.software_genre, Computer security, Software, Middleware, Intelligent environment, Audio signal processing, business, computer
Abstract

This paper presents an ambient intelligence system aimed at increasing the security of elderly people in their living environment. The proposed system processes both video and audio signals to detect dangerous events and trigger automatic warnings. The system is implemented through a software middleware for multimedia streaming and processing in which the information is processed in several, distributed software nodes. This paper focuses on the fall detection video processing node and on the sound classification and localization audio processing node. The system has been tested both in a laboratory and in a real-world scenario, demonstrating high performance levels.

Published
2012

49. A Multimodal People Recognition System for an Intelligent Environment

Author

Enrico Pagello, Salvatore Maria Anzalone, Emanuele Menegatti, Rosario Sorbello, Yuichiro Yoshikawa, Hiroshi Ishiguro, Pirrone, R, Sorbello, F, Anzalone, SM, Menegatti, E, Pagello, E, Sorbello, R, Yoshikawa, Y, and Ishiguro, H

Subjects
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, Modality (human–computer interaction), Intelligent Enviroment,Multimodal Recognition System, Computer science, business.industry, Support vector machine, Software, Eigenface, Middleware, Learning rule, Intelligent environment, Computer vision, Artificial intelligence, business
Abstract

In this paper, a multimodal system for recognizing people in intelligent environments is presented. Users are identified and tracked by detecting and recognizing voices and faces through cameras and microphones spread around the environment. This multimodal approach has been chosen to develop a flexible and cheap though reliable system, implemented through consumer electronics. Voice features are extracted through a short time spectrum analysis, while face features are extracted using the eigenfaces technique. The recognition task is achieved through the use of some Support Vector Machines, one per modality, that learn and classify the features of each person, while bindings between modalities are also learnt through a cross-anchoring learning rule based on the mutual exclusivity selection principle. The system has been developed using NMM, a middleware software capable of splitting the sensors processing in several software nodes, making the system scalable in the number of cameras and microphones.

Published
2011

50. Audio-video people recognition system for an intelligent environment

Author

Enrico Pagello, Salvatore Maria Anzalone, Antonio Chella, Hiroshi Ishiguro, Emanuele Menegatti, Yuichiro Yoshikawa, Anzalone, SM, Menegatti, E, Pagello, E, Yoshikawa, Y, Ishiguro, H, and Chella, A

Subjects
Face feature, Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, business.industry, Computer science, Intelligent environment, People recognition, Feature extraction, Reliable system, Set-up phase, Single sensor, Facial recognition system, Selection principle, Support vector machine, Software, Eigenface, Multi-modal approach, Middleware, Cepstrum, Learning rule, Cepstrum analysi, Computer vision, Artificial intelligence, business
Abstract

In this paper an audio-video system for intelligent environments with the capability to recognize people is presented. Users are tracked inside the environment and their positions and activities can be logged. Users identities are assessed through a multimodal approach by detecting and recognizing voices and faces through the different cameras and microphones installed in the environment. This approach has been chosen in order to create a flexible and cheap but reliable system, implemented using consumer electronics. Voice features are extracted by a short time cepstrum analysis, and face features are extracted using the eigenfaces technique. The recognition task is solved using the same Support Vector Machine for both voice and face features. The system learns the features of each person using SVM in a set-up phase, in which the two modalities are also bound together through a cross-anchoring learning rule based on the mutual exclusivity selection principle. In the running phase the system is able to recognize the identity of the person either using voice features, or face features or both. The system is scalable in the number of cameras and microphones thanks to NMM, a middleware software which manages the processing of the single sensors and the communications among the several software nodes.

Published
2011

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