TY - JOUR
T1 - Quantifying patterns of joint attention during human-robot interactions
T2 - An application for autism spectrum disorder assessment
AU - Anzalone, Salvatore Maria
AU - Xavier, Jean
AU - Boucenna, Sofiane
AU - Billeci, Lucia
AU - Narzisi, Antonio
AU - Muratori, Filippo
AU - Cohen, David
AU - Chetouani, Mohamed
N1 - Funding Information:
Authors would like to thank all the MICHELANGELO Study group: Silvio Bonfiglio (FIMI, Italy), Fabio Apicella, Federico Sicca, Chiara Lucentini, Francesca Fulceri (Fondazione Stella Maris, Italy), Giovanni Pioggia, Alessandro Tonacci (CNR, Italy), Federico Cruciani, Cristiano Paggetti (I+, Italy), Angele Giuliano, Maryrose Francisa (Accros Limit, Malta), Koushik Maharatna, Valentina Bono, Wasifa Jamal (University of Southampton, UK), Leo Galway, Mark Donnelly (University of Ulster, UK), Anne-Lise Jouen, Elodie Tilmont (ISIR-UPMC). The authors would also like to thank all the patients and families who participated in the current study. They are also very grateful for the support from the hospitals staff. Authors would give many thanks to A. Arrigo, R. Grassia and G. Varni for their kind support and collaboration. The current study was supported by a grant from the European Commission (FP7: Michelangelo under grant agreement no. 288241 ), the fund “Entreprendre pour aider”, the Laboratory of Excellence SMART (ANR-11-LABX-65) supported by French State funds managed by the ANR within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02. The funding agencies and the University were not involved in the study design, collection, analysis and interpretation of the data, the writing of the paper, or the decision to submit it for publication.
Funding Information:
The Michelangelo Project, funded by the European Commission, proposed several, cost-effective, technology tools to bring the ASD assessment and therapy to the home setting. As part of this project, the Michelangelo Study Group developed a network made up by different sensors [20], such as cameras, RGB-D sensors, microphones, wearable systems for electroencephalographic (EEG) [12] and electrocardiogram (ECG) [7] signals recording, to capture the fine detail of the behaviour of the children in controlled environments [3,14].Authors would like to thank all the MICHELANGELO Study group: Silvio Bonfiglio (FIMI, Italy), Fabio Apicella, Federico Sicca, Chiara Lucentini, Francesca Fulceri (Fondazione Stella Maris, Italy), Giovanni Pioggia, Alessandro Tonacci (CNR, Italy), Federico Cruciani, Cristiano Paggetti (I+, Italy), Angele Giuliano, Maryrose Francisa (Accros Limit, Malta), Koushik Maharatna, Valentina Bono, Wasifa Jamal (University of Southampton, UK), Leo Galway, Mark Donnelly (University of Ulster, UK), Anne-Lise Jouen, Elodie Tilmont (ISIR-UPMC). The authors would also like to thank all the patients and families who participated in the current study. They are also very grateful for the support from the hospitals staff. Authors would give many thanks to A. Arrigo, R. Grassia and G. Varni for their kind support and collaboration. The current study was supported by a grant from the European Commission (FP7: Michelangelo under grant agreement no. 288241), the fund “Entreprendre pour aider” the Laboratory of Excellence SMART (ANR-11-LABX-65) supported by French State funds managed by the ANR within the Investissements d'Avenir programme under reference ANR-11-IDEX-0004-02. The funding agencies and the University were not involved in the study design, collection, analysis and interpretation of the data, the writing of the paper, or the decision to submit it for publication.
Funding Information:
The Michelangelo Project, funded by the European Commission , proposed several, cost-effective, technology tools to bring the ASD assessment and therapy to the home setting. As part of this project, the Michelangelo Study Group developed a network made up by different sensors [20] , such as cameras, RGB-D sensors, microphones, wearable systems for electroencephalographic (EEG) [12] and electrocardiogram (ECG) [7] signals recording, to capture the fine detail of the behaviour of the children in controlled environments [3,14] .
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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.
AB - 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.
KW - Autism spectrum disorder
KW - Behavioural analysis
KW - Social robotics
UR - http://www.scopus.com/inward/record.url?scp=85043391708&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2018.03.007
DO - 10.1016/j.patrec.2018.03.007
M3 - Article
AN - SCOPUS:85043391708
SN - 0167-8655
VL - 118
SP - 42
EP - 50
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
ER -