SD, Munera M, Casas D, Villa-Moreno A, Frizera-Neto A, Stoelen MF, Belpaeme T, Cifuentes CA. Do different robot appearances change emotion recognition in children with ASD?

Introduction

Socially Assistive Robotics has emerged as a potential tool for rehabilitating cognitive and developmental disorders in children with autism. Social robots found in the literature are often able to teach critical social skills, such as emotion recognition and physical interaction. Even though there are promising results in clinical studies, there is a lack of guidelines on selecting the appropriate robot and how to design and implement the child-robot interaction.

Methods

This work aims to evaluate the impacts of a social robot designed with three different appearances according to the results of a participatory design (PD) process with the community. A validation study in the emotion recognition task was carried out with 21 children with autism.

Results

Spectrum disorder results showed that robot-like appearances reached a higher percentage of children's attention and that participants performed better when recognizing simple emotions, such as happiness and sadness.

Discussion

This study offers empirical support for continuing research on using SAR to promote social interaction with children with ASD. Further long-term research will help to identify the differences between high and low-functioning children.

Who to Observe and Imitate in Humans and Robots: The Importance of Motivational Factors

Imitation is a vital skill that humans leverage in various situations. Humans achieve imitation by observing others with apparent ease. Yet, in reality, it is computationally expensive to model on artificial agents (e.g., social robots) to acquire new skills by imitating an expert agent. Although learning through imitation has been extensively addressed in the robotic literature, most studies focus on answering the following questions: what to imitate and how to imitate. In this conceptual paper, we focus on one of the overlooked questions of imitation through observation: who to imitate. We present possible answers to the who-to-imitate question by exploring motivational factors documented in psychological research and their possible implementation in robotics. To this end, we focus on two critical instances of the who-to-imitate question that guide agents to prioritize one demonstrator over another: outcome expectancies, viewed as the anticipated learning gains, and efficacy expectations, viewed as the anticipated costs of performing actions, respectively.

The Quantitative Case-by-Case Analyses of the Socio-Emotional Outcomes of Children with ASD in Robot-Assisted Autism Therapy

With its focus on robot-assisted autism therapy, this paper presents case-by-case analyses of socio-emotional outcomes of 34 children aged 3–12 years old, with different cases of Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). We grouped children by the following characteristics: ASD alone (n = 22), ASD+ADHD (n = 12), verbal (n = 11), non-verbal (n = 23), low-functioning autism (n = 24), and high-functioning autism (n = 10). This paper provides a series of separate quantitative analyses across the first and last sessions, adaptive and non-adaptive sessions, and parent and no-parent sessions, to present child experiences with the NAO robot, during play-based activities. The results suggest that robots are able to interact with children in social ways and influence their social behaviors over time. Each child with ASD is a unique case and needs an individualized approach to practice and learn social skills with the robot. We, finally, present specific child–robot intricacies that affect how children engage and learn over time as well as across different sessions.

A Music-Therapy Robotic Platform for Children With Autism: A Pilot Study

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills including motor control, turn-taking, and emotion recognition. Innovative technology, such as socially assistive robots, has shown to be a viable method for Autism therapy. This paper presents a novel robot-based music-therapy platform for modeling and improving the social responses and behaviors of children with ASD. Our autonomous social interactive system consists of three modules. Module one provides an autonomous initiative positioning system for the robot, NAO, to properly localize and play the instrument (Xylophone) using the robot’s arms. Module two allows NAO to play customized songs composed by individuals. Module three provides a real-life music therapy experience to the users. We adopted Short-time Fourier Transform and Levenshtein distance to fulfill the design requirements: 1) “music detection” and 2) “smart scoring and feedback”, which allows NAO to understand music and provide additional practice and oral feedback to the users as applicable. We designed and implemented six Human-Robot-Interaction (HRI) sessions including four intervention sessions. Nine children with ASD and seven Typically Developing participated in a total of fifty HRI experimental sessions. Using our platform, we collected and analyzed data on social behavioral changes and emotion recognition using Electrodermal Activity (EDA) signals. The results of our experiments demonstrate most of the participants were able to complete motor control tasks with 70% accuracy. Six out of the nine ASD participants showed stable turn-taking behavior when playing music. The results of automated emotion classification using Support Vector Machines illustrates that emotional arousal in the ASD group can be detected and well recognized via EDA bio-signals. In summary, the results of our data analyses, including emotion classification using EDA signals, indicate that the proposed robot-music based therapy platform is an attractive and promising assistive tool to facilitate the improvement of fine motor control and turn-taking skills in children with ASD.

A bibliometric analysis of research trends of artificial intelligence in the treatment of autistic spectrum disorders

OBJECTIVE

Autism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder that has become the leading cause of disability in children. Artificial intelligence (AI) is a potential solution to this issue. This study objectively analyzes the global research situation of AI in the treatment of ASD from 1995 to 2022, aiming to explore the global research status and frontier trends in this field.

METHODS

Web of Science (WoS) and PubMed databese were searched for Literature related to AI on ASD from 1995 to April 2022. CiteSpace, VOSviewer, Pajek and Scimago Graphica were used to analyze the collaboration between countries/institutions/authors, clusters and bursts of keywords, as well as analyses on references.

RESULTS

A total of 448 literature were included, the total number of literature has shown an increasing trend. The most productive country and institution were the USA, and Vanderbilt University. The authors with the greatest contributions were Warren, Zachary, Sakar, Nilanjan and Swanson, Amy. the most prolific and cited journal is Journal of Autism and Developmental Disorders, the highest cited and co-cited articles were Dautenhahn (Socially intelligent robots: dimensions of human-robot interaction 2007) and Scassellati B (Robots for Use in Autism Research 2012). "Artificial Intelligence", "Brain Computer Interface" and "Humanoid Robot" were the hotspots and frontier trends of AI on ASD.

CONCLUSION

The application of AI in the treatment of ASD has attracted the attention of researchers all over the world. The education, social function and joint attention of children with ASD are the most concerned issues for global researchers. Robots shows gratifying advantages in these issues and have become the most commonly used technology. Wearable devices and brain-computer interface (BCI) were emerging AI technologies in recent years, which is the direction of further exploration. Restoring social function in individuals with ASD is the ultimate aim and driving force of research in the future.

Educational Robotics for children with neurodevelopmental disorders: A systematic review

This paper aims to provide a critical review of the studies dealing with Educational Robotics for children with Neurodevelopmental Disorders. We aimed to investigate whether in the literature there is a sound evidence that activities with robots improve the abilities and performances of children with special needs. This paper explores the methodological aspects as well as the outcomes of the selected studies to provide a clear picture of the state-of-the-art on this topic. After a systematic search in the online database via keyword searches, 15 scientific papers were included in this review. We applied strict selection criteria limiting our review only to papers reporting educational robotics activities with children (from 3 up to 19 years old) with a diagnosis of neurodevelopmental disorders, in which the children had the opportunity to somehow program the behaviours of real robots. The majority of experiences showed improvements in the participants' performance or abilities, their engagement and involvement, communication/interaction with peers, during robotics sessions. Some studies reported mixed results, calling for the need to carefully design the objective and the related activities of each experience.

Theory of Robot Mind: False Belief Attribution to Social Robots in Children With and Without Autism

This study aims to probe how children with and without autism spectrum disorders (ASD) attribute false belief to a social robot and predict its action accordingly. Twenty 5- to 7-year-old children with ASD and 20 age- and IQ-matched typically developing (TD) children participated in two false belief tasks adapted for robot settings (change-of-location task and the unexpected-contents task). The results showed that most TD children are capable of attributing false belief to the social robot, that is, they could infer higher level mental states in robots, which extends our understanding in TD children’s perception and cognition on social robots. Conversely, children with ASD still show difficulty in interpreting robots’ mental states compared to their TD peers, which would greatly interfere with their interactions and communications with social robots and might impact on efficiency of robot-based intervention and education approaches. This group difference in attributing false belief to social robots could not be explained by the different perception and categorization of the robot. Our study implies that although children with ASD appear to be highly attracted by social robots, they still have difficulty in understanding mental states when socially interacting with robots, which should be taken into consideration when designing the robot-based intervention approach targeting to improve social behaviors of ASD.

Toward a motor signature in autism: Studies from human-machine interaction

BACKGROUND

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-communication and repetitive symptoms and stereotypies. Although not cardinal symptoms per se, motor impairments are fundamental aspects of ASD. These impairments are associated with postural and motor control disabilities that we investigated using computational modeling and developmental robotics through human-machine interaction paradigms.

METHOD

First, in a set of studies involving a human-robot posture imitation, we explored the impact of 3 different groups of partners (including a group of children with ASD) on robot learning by imitation. Second, using an ecological task, i.e. a real-time motor imitation with a tightrope walker (TW) avatar, we investigated interpersonal synchronization, motor coordination and motor control during the task in children with ASD (n=29), TD children (n=39) and children with developmental coordination disorder (n=17, DCD).

RESULTS

From the human-robot experiments, we evidenced that motor signature at both groups' and individuals' levels had a key influence on imitation learning, posture recognition and identity recognition. From the more dynamic motor imitation paradigm with a TW avatar, we found that interpersonal synchronization, motor coordination and motor control were more impaired in children with ASD compared to both TD children and children with DCD. Taken together these results confirm the motor peculiarities of children with ASD despite imitation tasks were adequately performed.

DISCUSSION

Studies from human-machine interaction support the idea of a behavioral signature in children with ASD. However, several issues need to be addressed. Is this behavioral signature motoric in essence? Is it possible to ascertain that these peculiarities occur during all motor tasks (e.g. posture, voluntary movement)? Could this motor signature be considered as specific to autism, notably in comparison to DCD that also display poor motor coordination skills? We suggest that more work comparing the two conditions should be implemented, including analysis of kinematics and movement smoothness with sufficient measurement quality to allow spectral analysis.

ICT and autism care: state of the art

PURPOSE OF REVIEW

Over the past 10 years, the use of information and communication technologies (ICTs) has increased in regard to the treatment of individuals with autism spectrum disorders (ASDs). ICT support mechanisms (e.g. computers, laptops, robots) are particularly attractive and are adapted to children with ASD. In addition, ICT algorithms can offer new perspectives for clinicians, outside direct apps or gaming proposals. Here, we will focus on the use of serious games and robots because of their attractiveness and their value in working on social skills.

RECENT FINDINGS

The latest knowledge regarding the use of ICT in the forms of serious games and robotics applied to individuals with ASD shows that the field of serious games has already achieved interesting and promising results, although the clinical validations are not always complete. In the field of robotics, there are still many limitations on the use of ICT (e.g. most interaction are similar to the wizard of Oz), and questions remain concerning their eventual effectiveness.

SUMMARY

To describe the implications of the findings for clinical practice or research, we describe two large projects, namely, JEMImE and Michelangelo, as examples of current studies that are aimed at enhancing social skills in children with ASD by including novel algorithms with clinical insights in robots or serious games.

Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation Using an Analytical Mapping Method and Quantitative Evaluation

Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.

Contribution of Developmental Psychology to the Study of Social Interactions: Some Factors in Play, Joint Attention and Joint Action and Implications for Robotics

Children exchange information through multiple modalities, including verbal communication, gestures and social gaze and they gradually learn to plan their behavior and coordinate successfully with their partners. The development of joint attention and joint action, especially in the context of social play, provides rich opportunities for describing the characteristics of interactions that can lead to shared outcomes. In the present work, we argue that human-robot interactions (HRI) can benefit from these developmental studies, through influencing the human's perception and interpretation of the robot's behavior. We thus endeavor to describe some components that could be implemented in the robot to strengthen the feeling of dealing with a social agent, and therefore improve the success of collaborative tasks. Focusing in particular on motor precision, coordination, and anticipatory planning, we discuss the question of complexity in HRI. In the context of joint activities, we highlight the necessity of (1) considering multiple speech acts involving multimodal communication (both verbal and non-verbal signals), and (2) analyzing separately the forms and functions of communication. Finally, we examine some challenges related to robot competencies, such as the issue of language and symbol grounding, which might be tackled by bringing together expertise of researchers in developmental psychology and robotics.

Interpersonal Synchronization, Motor Coordination, and Control Are Impaired During a Dynamic Imitation Task in Children With Autism Spectrum Disorder

Background: Impairments in imitation abilities have been commonly described in children with autism spectrum disorder (ASD). How motricity in interpersonal coordination impacts imitation, during long lasting semi-ecological conditions, has not been carefully investigated.

Methods: Eighty-five children and adolescents (39 controls with typical development, TD; 29 patients with ASD; 17 patients with developmental coordination disorder, DCD), aged 6 to 20 years, participated to a behavioral paradigm in which participants, standing and moving, interacted with a virtual tightrope walker standing and moving as well. During the protocol, we measured automatically and continuously bodily postures and movements from RGB sensor recording to assess participants' behavioral imitation.

Results: We show that (1) interpersonal synchronization (as evidenced by the synchrony between the participant's and the tightrope walker's bars) and (2) motor coordination (as evidenced by the synchrony between the participant's bar and its own head axis) increased with age and were more impaired in patients with ASD. Also, motor control as evidenced by the movement angle standard deviations of participants' bar and head were significantly impaired in ASD compared to TD or DCD.

Conclusion: Interpersonal synchronization and motor coordination during ecological interaction show both subtle impairment in children with ASD as compared to children with TD or DCD. These results questioned how motricity mature in terms of motor control and proprioception in children with ASD.

A developmental and clinical perspective of rhythmic interpersonal coordination: From mimicry toward the interconnection of minds

Imitation plays a critical role in the development of intersubjectivity and serves as a prerequisite for understanding the emotions and intentions of others. In our review, we consider spontaneous motor imitation between children and their peers as a developmental process involving repetition and perspective-taking as well as flexibility and reciprocity. During childhood, this playful dynamic challenges developing visuospatial abilities and requires temporal coordination between partners. As such, we address synchrony as form of communication and social signal per se, that leads, from an experience of similarity, to the interconnection of minds. In this way, we argue that, from a developmental perspective, rhythmic interpersonal coordination through childhood imitative interactions serves as a precursor to higher- level social and cognitive abilities, such as theory of mind (TOM) and empathy. Finally, to clinically illustrate our idea, we focus on developmental coordination disorder (DCD), a condition characterized not only by learning difficulties, but also childhood deficits in motor imitation. We address the challenges faced by these children on an emotional and socio-interactional level through the perspective of their impairments in intra- and interpersonal synchrony.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.