Adaptive Framework for Emotional Engagement in Child-Robot Interactions for Autism Interventions

Javed, Hifza; Jeon, Myounghoon; Park, Chung Hyuk

doi:10.1109/urai.2018.8441775

Cited by 10 publications

(23 citation statements)

References 10 publications

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“…Rudovic et al ( 2018 ) implemented deep learning in a robot for ASD therapy to automatically estimate children's valence, arousal and engagement levels. Javed et al ( 2018 ) utilized multimodal perception, including the analyses of children's motion, speech, and facial expression, to estimate children's emotional states. Using multiple feedback modalities may overload users with redundant information, increase task completion time, and reduce the efficiency of cognitive training (Taranović et al, 2018c ).…”

Section: Resultsmentioning

confidence: 99%

“…For example, Tsiakas et al ( 2016 ) used interactive reinforcement learning methods to facilitate the adaptive robot-assisted therapy, that is, adapt the task difficulty level and task duration to users with different skill levels (e.g., expert or novice user), in the context that users need to perform a set of cognitive or physical training tasks. Javed et al ( 2018 ) developed a Hidden Markov model (HMM) in their adaptive framework for child-robot interaction, aiming to enable a child with ASD to engage in robot-assisted ASD therapy over long term. In their HMM, the states took into consideration a child's emotional state or mood, and the actions were the robot's behaviors or other audiovisual feedback.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Review of Robotic Rehabilitation for Cognitive Training

et al. 2021

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A large and increasing number of people around the world experience cognitive disability. Rehabilitation robotics has provided promising training and assistance approaches to mitigate cognitive deficits. In this article, we carried out a systematic review on recent developments in robot-assisted cognitive training. We included 99 articles in this work and described their applications, enabling technologies, experiments, and products. We also conducted a meta analysis on the articles that evaluated robot-assisted cognitive training protocol with primary end users (i.e., people with cognitive disability). We identified major limitations in current robotics rehabilitation for cognitive training, including the small sample size, non-standard measurement of training and uncontrollable factors. There are still multifaceted challenges in this field, including ethical issues, user-centered (or stakeholder-centered) design, the reliability, trust, and cost-effectiveness, personalization of the robot-assisted cognitive training system. Future research shall also take into consideration human-robot collaboration and social cognition to facilitate a natural human-robot interaction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Robotic Rehabilitation for Cognitive Training

et al. 2021

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“…Children with ASD [10], [12], [13], [15]- [17], [19], [21]- [43] Children with autism [7]- [9], [12], [13], [19], [20], [34], [37], [41], [42], [44]- [47] Autistic children [8], [15], [19], [20], [36], [46], [48]- [52] Children on/with autism spectrum [22], [53] ASD children [54] Autism children [51] TD children [10], [12], [19], [21], [24], [26], [43], [47], [52] Normal children [8], [20], [36], [49], [51] Neurotypical children [13], [16], [23], [50] Typical individuals…”

Section: Wording Articlesmentioning

confidence: 99%

“…Articles Humanoid NAO [7], [8], [13], [15], [17], [20], [22], [24], [25], [32], [36], [37], [39]- [42], [47]- [49], [51], [55], [56] ZECA [12], [33], [50], [52], [54], [57] Darwin-Mini [7], [16], [23], [43], [58] Kaspar [9], [28], [37], [59] iRobiQ [30], [44], [45] CARO [30], [44], [ [34] nipulator with a basketball hoop attached to its end-effector. RBB displays different affective states by moving the hoop in 3D with different speed levels, accompanied with soft background music [34].…”

Section: Robot Type Robotmentioning

confidence: 99%

Robot-Based Intervention for Children With Autism Spectrum Disorder: A Systematic Literature Review

et al. 2021

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Children with autism spectrum disorder (ASD) have deficits in the socio-communicative domain and frequently face severe difficulties in the recognition and expression of emotions. Existing literature suggested that children with ASD benefit from robot-based interventions. However, studies varied considerably in participant characteristics, applied robots, and trained skills. Here, we reviewed robot-based interventions targeting emotion-related skills for children with ASD following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We systematically searched for all relevant articles published in English language until April 2021, using the databases Scopus, Web of Science, and PubMed. From a total of 609 identified papers, 60 publications including 50 original articles and 10 non-empirical articles including review articles and theoretical articles were eligible for the synthesis. A total of 892 participants were included in the robot-based intervention studies; 570 of them were children with ASD. Nao and Zeca were the most frequently used robots; Recognition of basic emotions and getting into interaction were the most frequently trained skills; while happiness, sadness, fear, and anger were the most frequently trained emotions. The studies reported a wide range of challenges with respect to robot-based intervention, ranging from limitations for certain ASD subgroups and security aspects of the robots to efforts regarding the automatic recognition of the children's emotional state by the robotic systems. Finally, we summarised and discussed recommendations regarding the application of robot-based interventions for children with ASD.

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“…A method for the automatic classification of engagement with a dynamic Bayesian network using visual and acoustic cues and support vector machine classifiers is described in [24]. Another approach considers the facial expressions of children with ASD to evaluate their engagement [25]. A robot-mediated joint attention intervention system using as input the child's gaze is presented in [26].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task

Hadfield

Chalvatzaki

Koutras

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this work we tackle the problem of child engagement estimation while children freely interact with a robot in their room. We propose a deep-based multi-view solution that takes advantage of recent developments in human pose detection. We extract the child's pose from different RGB-D cameras placed elegantly in the room, fuse the results and feed them to a deep neural network trained for classifying engagement levels. The deep network contains a recurrent layer, in order to exploit the rich temporal information contained in the pose data. The resulting method outperforms a number of baseline classifiers, and provides a promising tool for better automatic understanding of a child's attitude, interest and attention while cooperating with a robot. The goal is to integrate this model in next generation social robots as an attention monitoring tool during various CRI tasks both for Typically Developed (TD) children and children affected by autism (ASD).

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Adaptive Framework for Emotional Engagement in Child-Robot Interactions for Autism Interventions

Cited by 10 publications

References 10 publications

A Systematic Review of Robotic Rehabilitation for Cognitive Training

A Systematic Review of Robotic Rehabilitation for Cognitive Training

Robot-Based Intervention for Children With Autism Spectrum Disorder: A Systematic Literature Review

A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task

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