Measuring Engagement in Robot-Assisted Autism Therapy: A Cross-Cultural Study

Rudovic, Ognjen; Lee, Jaeryoung; Mascarell-Maricic, Lea; Schuller, Björn; Picard, Rosalind W.

doi:10.3389/frobt.2017.00036

Cited by 105 publications

(106 citation statements)

References 94 publications

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“…This is probably because robots evoke emotional reactions potentially leading to specific emotional bonds between human and machine (Eyssel, 2017). Therefore, when the robot held the child's attention and interest in both itself and the tasks, it was possible to extend the therapy sessions for a longer time period (Rudovic, Lee, Mascarell-Maricic, Schuller, & Picard, 2017).…”

Section: Introductionmentioning

confidence: 99%

Affect Recognition in Autism: A single case study on integrating a humanoid robot in a standard therapy

Conti

Trubia

Buono

et al. 2019

Qwerty

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Autism Spectrum Disorder (ASD) is a multifaceted developmental disorder that comprises a mixture of social impairments, with deficits in many areas including the theory of mind, imitation, and communication. Moreover, people with autism have difficulty in recognising and understanding emotional expressions. We are currently working on integrating a humanoid robot within the standard clinical treatment offered to children with ASD to support the therapists. In this article, using the A-B-A' single case design, we propose a robot-assisted affect recognition training and to present the results on the child's progress during the five months of clinical experimentation. In the investigation, we tested the generalization of learning and the long-term maintenance of new skills via the NEPSY-II affection recognition sub-test. The results of this single case study pilot suggest the feasibility and effectiveness of using a humanoid robot to assist with emotion recognition training in children with ASD.

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Section: Introductionmentioning

confidence: 99%

Affect Recognition in Autism: A single case study on integrating a humanoid robot in a standard therapy

Conti

Trubia

Buono

et al. 2019

Qwerty

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“…While the tracking itself helps tremendously in producing realistic virtual avatar faces, there is still an open research issue in how to accurately understand the captured motion with respect to nonverbal communicative behaviors of a participant with the exception of eye gaze (and to some extend facial expressions). Hence, the state of the art practice is still to record the video of the participant and then use domain experts to analyze the video regarding the participant's nonverbal communicative behaviors [52,[118][119][120].…”

Section: Facial Motion/expression Trackingmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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Abstract:The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?

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“…Researchers have been exploring various roles that robots can play in a human-robot relationship. For example, researchers have developed social robots to mediate between Parkinson disease patients and their caregivers (Shim and Arkin, 2017), to promote collaboration and measure engagement between children with autism (Rudovic et al, 2017) and their siblings (Huskens et al, 2015), and as a tutor in learning applications (Gordon et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

et al. 2018

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The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBot TM , a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.

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Measuring Engagement in Robot-Assisted Autism Therapy: A Cross-Cultural Study

Cited by 105 publications

References 94 publications

Affect Recognition in Autism: A single case study on integrating a humanoid robot in a standard therapy

Affect Recognition in Autism: A single case study on integrating a humanoid robot in a standard therapy

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

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