Autism and social robotics: A systematic review

Training potential of a teleoperated humanoid robot for use by a young psychiatrist during childcare leave

Background

Childcare leave extensions can sometimes negatively affect the professional clinical training of early-career psychiatrists in Japan. During childcare leave, being able to learn in the examination room while staying at home would be useful. Therefore, we developed a training system using a teleoperated robot (Sota) for young psychiatrists who wanted to participate in the examination room during childcare leave while remaining at home.

Case Presentation

We report the case of a patient with autism spectrum disorder (ASD) comorbid with Tourette's disorders (P). A young female psychiatrist (D) used the training system to learn from a board-certified psychiatrist. In this case, the board-certified psychiatrist, P, and the robot were placed in the examination room. D teleoperated Sota from home, allowing her to talk to the board-certified psychiatrist and P. She learned about the clinical features of Tourette's syndrome by observing the examination of the board-certified psychiatrist and hearing the patient's distress. P was satisfied with the fact that he was seen not only by a board-certified psychiatrist but also by D.

Conclusion

These case findings suggest that our system is helpful for young psychiatrists who want to study in the examination room during childcare leave while staying at home. Future studies should include a single-case experimental design with information regarding key outcome variables and other relevant variables gathered regularly over time.

Do Children with Autism Spectrum Disorders Show Selective Trust in Social Robots?

PURPOSE

Previous researches suggest that social robots can facilitate the learning of children with Autism Spectrum Disorder (ASD) by enhancing their interests, engagement, and attention. However, there is limited understanding regarding whether children with ASD can learn directly from the testimony of social robots and whether they can remain vigilant based on the perceived accuracy of these robots. Therefore, the present study was conducted to examine whether children with ASD demonstrated selective trust towards social robots.

METHODS

Twenty-nine children with ASD between ages of 4-7 years, and 38 typically-developing (TD) age and IQ-matched peers participated in classic selective trust tasks. During the tasks, they learned the names of novel objects from either a pair of social robots or a pair of human informants, where one informant had previously been established as accurate and the other inaccurate.

RESULTS

Children with ASD trusted information from an accurate social robot over an inaccurate one, similar to their performance with human informants. However, compared to TD children, children with ASD exhibited lower levels of selective trust regardless of the type of informants they learned from.

CONCLUSIONS

Our study suggests that children with ASD can selectively trust and acquire knowledge from social robots, shedding light on the potential use of social robots in supporting individuals with ASD.

Parental involvement in robot-mediated intervention: a systematic review

Introduction

Over the years, the conceptual approach to pediatric rehabilitation has reevaluated the parent's role in the therapeutic process, considering parental involvement as a necessary condition for the effectiveness of the intervention. In the field of pediatric intervention, the therapeutic use of robots represents a growing clinical interest, but the feasibility and applicability of these robotic interventions, including those involving parents, remain unclear. This systematic review aims to investigate parental involvement in robot-mediated interventions (RMI) for children and adolescents in the current literature. Our main goal is to analyze and summarize all existing studies to discuss and draw future research directions and implications for clinical practice.

Method

After collecting results from 1,106 studies, the studies selected were analyzed using thematic analysis. The literature review was conducted in accordance with the PRISMA guidelines by searching databases such as PubMed and Web of Science until 07 February 2023. Studies that met the following inclusion criteria were included: (1) the use of a robot as a therapeutic-rehabilitation tool and (2) the presence/involvement of parents/caregivers in child-robot therapeutic sessions.

Results

A total of 10 articles were included. The extracted data included study design, participant characteristics, type of robot used, outcome measures, aim, and type of intervention. The results reveal that parental involvement in RMI could be feasible and useful in improving intervention efficacy, particularly in improving the child's social-communicative abilities and the caregiver's educational skills.

Discussion

RMI intervention with parental participation could be a useful therapeutic strategy in pediatrics. However, to date, few studies have investigated this specific topic, and the reported results may enhance future research to understand its effectiveness in specific areas of use.

Systematic review registration

identifier: CRD42024553214.

Social robots in research on social and cognitive development in infants and toddlers: A scoping review

There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and cognitive development in infants and toddlers aged between 2 and 35 months. Moreover, it aims to identify the research focus, findings, and reported gaps and challenges when using robots in research. We included empirical studies published between 1990 and May 29, 2023. We searched for literature in PsychINFO, ERIC, Web of Science, and PsyArXiv. Twenty-nine studies met the inclusion criteria and were mapped using the scoping review method. Our findings reveal that most studies were quantitative, with experimental designs conducted in a laboratory setting where children were exposed to physically present or virtual robots in a one-to-one situation. We found that robots were used to investigate four main concepts: animacy concept, action understanding, imitation, and early conversational skills. Many studies focused on whether young children regard robots as agents or social partners. The studies demonstrated that young children could learn from and understand social robots in some situations but not always. For instance, children's understanding of social robots was often facilitated by robots that behaved interactively and contingently. This scoping review highlights the need to design social robots that can engage in interactive and contingent social behaviors for early developmental research.

Using varied technological agents-assisted simultaneous prompting for teaching discrete skills to children with developmental disabilities

This study examines the effectiveness of combining simultaneous prompting method with small group teaching through computer projection, SMART board, tablet computer and humanoid robot to teach discrete skills to children with developmental disabilities (CDD). The study included 14 CDD aged 10-15. It utilizes a multiple probe design across behaviors and probe conditions and replicates them across subjects. Each participant is taught discrete skills within a small group teaching arrangement. The study includes daily probes, full probes, teaching sessions, generalization, and follow-up sessions. It also collects interobserver reliability and application reliability data. Graphical analysis demonstrates the effectiveness of computer-based simultaneous prompting incorporating different technologies in a small group teaching setting. Additionally, we examined differences in children's responses to different technological agents in teaching discrete skills to children with developmental disabilities. The study provided preliminary data on which of these agents is best. The results demonstrate its effectiveness by showing that participants maintained the learned behaviors and applied them to a variety of tools, equipment, and individuals in the first, third, and fourth weeks after the intervention. Additionally, the study highlights the subjects' high accuracy in acquiring behavior through observational learning. Finally, simple humanoid robots, tablets, smart SMART boards, and computer projections have been effective in teaching discrete skills to CDD, respectively.

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification

In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

People with Autism Spectrum Disorder Could Interact More Easily with a Robot than with a Human: Reasons and Limits

Individuals with Autism Spectrum Disorder show deficits in communication and social interaction, as well as repetitive behaviors and restricted interests. Interacting with robots could bring benefits to this population, notably by fostering communication and social interaction. Studies even suggest that people with Autism Spectrum Disorder could interact more easily with a robot partner rather than a human partner. We will be looking at the benefits of robots and the reasons put forward to explain these results. The interest regarding robots would mainly be due to three of their characteristics: they can act as motivational tools, and they are simplified agents whose behavior is more predictable than that of a human. Nevertheless, there are still many challenges to be met in specifying the optimum conditions for using robots with individuals with Autism Spectrum Disorder.

How Do Children and Adolescents with ASD Look at Animals? A Scoping Review

Autism spectrum disorder (ASD) is characterized by interaction and communication differences, entailing visual attention skill specificities. Interactions with animals, such as in animal-assisted interventions or with service dogs, have been shown to be beneficial for individuals with ASD. While interacting with humans poses challenges for them, engaging with animals appears to be different. One hypothesis suggests that differences between individuals with ASD's visual attention to humans and to animals may contribute to these interaction differences. We propose a scoping review of the research on the visual attention to animals of youths with ASD. The objective is to review the methodologies and tools used to explore such questions, to summarize the main results, to explore which factors may contribute to the differences reported in the studies, and to deduce how youth with ASD observe animals. Utilizing strict inclusion criteria, we examined databases between 1942 and 2023, identifying 21 studies in international peer-reviewed journals. Three main themes were identified: attentional engagement and detection, visual exploration, and behavior. Collectively, our findings suggest that the visual attention of youths with ASD towards animals appears comparable to that of neurotypical peers, at least in 2D pictures (i.e., eye gaze patterns). Future studies should explore whether these results extend to real-life interactions.

Self-administered questionnaires enhance emotion estimation of individuals with autism spectrum disorders in a robotic interview setting

Background

Robots offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Determining the optimal motion of robots when interacting with individuals with ASD is important for achieving more natural human-robot interactions and for exploiting the full potential of robotic interventions. Most prior studies have used supervised machine learning (ML) of user behavioral data to enable robot perception of affective states (i.e., arousal and valence) and engagement. It has previously been suggested that including personal demographic information in the identification of individuals with ASD is important for developing an automated system to perceive individual affective states and engagement. In this study, we hypothesized that assessing self-administered questionnaire data would contribute to the development of an automated estimation of the affective state and engagement when individuals with ASD are interviewed by an Android robot, which will be linked to implementing long-term interventions and maintaining the motivation of participants.

Methods

Participants sat across a table from an android robot that played the role of the interviewer. Each participant underwent a mock job interview. Twenty-five participants with ASD (males 22, females 3, average chronological age = 22.8, average IQ = 94.04) completed the experiment. We collected multimodal data (i.e., audio, motion, gaze, and self-administered questionnaire data) to train a model to correctly classify the state of individuals with ASD when interviewed by an android robot. We demonstrated the technical feasibility of using ML to enable robot perception of affect and engagement of individuals with ASD based on multimodal data.

Results

For arousal and engagement, the area under the curve (AUC) values of the model estimates and expert coding were relatively high. Overall, the AUC values of arousal, valence, and engagement were improved by including self-administered questionnaire data in the classification.

Discussion

These findings support the hypothesis that assessing self-administered questionnaire data contributes to the development of an automated estimation of an individual's affective state and engagement. Given the efficacy of including self-administered questionnaire data, future studies should confirm the effectiveness of such long-term intervention with a robot to maintain participants' motivation based on the proposed method of emotion estimation.

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

BACKGROUND

Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, depressed patients tend to conceal their symptoms from others based on shame and stigma, thus hesitate to visit psychiatrists especially during early phase. We hypothesize that application of humanoid robots would be a novel solution. Depressed patients may feel more comfortable talking with such robots than humans.

METHODS

We recruited 13 patients with major depressive disorder (MDD) and 27 healthy volunteers as controls. Participants took both tele-operated humanoid robot and human interviews to evaluate severity of depression using the Hamilton Depression Rating Scale (HDRS). In addition, participants completed a self-administered questionnaire asking about their impressions of the robot interview.

RESULTS

Confidence interval and t-test analysis have revealed that the HDRS scores are equally reliable between robot and human interviews. No significant differences were observed between the two interviews regarding "nervousness about the interview" and "hesitancy to talk about depressed moods and suicidal ideation." Compared to human interviews, robot interviews yielded significantly lower scores on shame-related factors especially among patients with MDD.

LIMITATION

Small sample size, and the evaluator is male only.

CONCLUSIONS

This is the first report to show the reliability of tele-operated humanoid robot interviews for assessment of depression. Robot interviews are potentially equally reliable as human interviews. Robot interviews are suggested to be more appropriate in assessing shame-related suppressed emotions and hidden thoughts of depressed patients in clinical practice, which may reduce the stigma associated with depression.

The League: A person-centred approach to the development of social robotics for paediatric anxiety

BACKGROUND

Social robots are promising tools to improve the quality of life of children and youth living with anxiety and should be developed based on the priorities of end users. However, pathways to include young people in patient-oriented research, particularly in the overlap between technology and mental health, have been historically limited.

OBJECTIVE

In this work, we describe engagement with experts with lived experiences of paediatric anxiety in a social robotics research programme. We report the experiences of patient advisors in a co-creation process and identify considerations for other research groups looking to involve end users in technology development in the field of youth mental health.

DESIGN

We engaged individuals with a lived experience of paediatric anxiety (current, recent past, or from a parent perspective) using three different models over the course of three years. Two initial patient partners were involved during project development, eight were engaged as part of an advisory panel ('the League') during study development and data analysis and four contributed as ongoing collaborators in an advisory role. League members completed a preparticipation expectation survey and a postparticipation experience survey.

FINDINGS

Eight individuals from a range of anxiety-related diagnostic groups participated in the League as patient partners. Members were teenagers (n = 3), young adults aged 22-26 years who had connected with a youth mental health service as children within the past eight years (n = 3) or parents of children presently living with anxiety (n = 2). Preferred methods of communication, expectations and reasons for participating were collected. The League provided specific and actionable feedback on the design of workshops on the topic of social robotics, which was implemented. They reported that their experiences were positive and fairly compensated, but communication and sustained engagement over time were challenges. Issues of ethics and language related to patient-centred brain health technology research are discussed.

CONCLUSIONS

There is an ethical imperative to meaningfully incorporate the voices of youth and young adults with psychiatric conditions in the development of devices intended to support their mental health and quality of life.

PATIENT OR PUBLIC CONTRIBUTION

Six young people and two parents with lived experiences of paediatric anxiety participated in all stages of developing a research programme on social robotics to support paediatric mental health in a community context. They also provided input during the preparation of this manuscript.

Effectiveness of a Robot-Assisted Psychological Intervention for Children with Autism Spectrum Disorder

Difficulties with social interaction characterise children with Autism Spectrum Disorders and have a negative impact in their everyday life. Integrating a social-humanoid robot within the standard clinical treatment has been proven promising. The main aim of this randomised controlled study was to evaluate the effectiveness of a robot-assisted psychosocial intervention and the secondary aim was to investigate potential differences between a robot-assisted intervention group and a control group receiving intervention by humans only. The analysis of the results showed that robot-assisted intervention could be beneficial by improving children's psychosocial skills. This improvement was highlighted by neuropsychological testing and parent reporting. Group comparison only presented minimal statistically significant differences. The study underpins the potential of robot-assisted interventions to augment standard care.

Social robots in the instruction of social skills in autism: a comprehensive descriptive analysis of single-case experimental designs

PURPOSE

The rapid technological advances, the traits of individuals with ASD and their interest in technology are promising for the instruction of social skills to individuals with autism spectrum disorder (ASD) using various technological interventions. Robotic interventions are among these. However, although robotics is frequently used with individuals with ASD, there is a limited number of reviews on social skills instruction and methods. The present study aimed to conduct a comprehensive descriptive analysis on single-case experimental designs where social skills were instructed to individuals with ASD and social robots were included as independent variables.

MATERIALS AND METHODS

Thirteen single-case experimental designs published in peer-reviewed journals in which social skills were taught to individuals with ASD using social robots were reviewed with a comprehensive descriptive analysis based on five categories: (a) key characteristics, (b) methodological characteristics, (c) findings, (d) data analysis, and (e) key parameters in single-case experimental designs.

RESULTS

Social robots are generally effective in the instruction of social skills. Several social skills (e.g., making eye contact, social interaction, simple greetings) were instructed in the studies. Humanoid robots and NAO were used generally. The study data were predominantly analyzed statistically. There were several problems in research based on the basic parameters in single-case experimental designs.

CONCLUSIONS

The researches in this study differ in several respects (e.g., results, data analysis, and dependent variable). Thus, there is still a need for several robotics studies in the instruction of social skills. IMPLICATIONS FOR REHABILITATIONThis study will be a guide for teachers who currently use robots in their classrooms but do not know which skills to use in teaching and how to use them functionally, as it shows applied research with robots.The findings of this research will show implementers working with children with ASD that technological tools can be used in rehabilitation environments, and that teachers can take a place in their robots in interventions for children with ASD, giving them a different perspective.It will be seen that the education of children with ASD is not only 1:1 and with humans, but robots can also provide education. In this way, the power of technology in teaching will become clearer. Especially in rehabilitation.Finally, this research will offer new options in teaching especially for teachers who aim at teaching social skills and will give them the opportunity to comprehensively examine the processes of different studies on these subjects.

Social robotics for children: an investigation of manufacturers' claims

As the market for commercial children's social robots grows, manufacturers' claims around the functionality and outcomes of their products have the potential to impact consumer purchasing decisions. In this work, we qualitatively and quantitatively assess the content and scientific support for claims about social robots for children made on manufacturers' websites. A sample of 21 robot websites was obtained using location-independent keyword searches on Google, Yahoo, and Bing from April to July 2021. All claims made on manufacturers' websites about robot functionality and outcomes (n = 653 statements) were subjected to content analysis, and the quality of evidence for these claims was evaluated using a validated quality evaluation tool. Social robot manufacturers made clear claims about the impact of their products in the areas of interaction, education, emotion, and adaptivity. Claims tended to focus on the child rather than the parent or other users. Robots were primarily described in the context of interactive, educational, and emotional uses, rather than being for health, safety, or security. The quality of the information used to support these claims was highly variable and at times potentially misleading. Many websites used language implying that robots had interior thoughts and experiences; for example, that they would love the child. This study provides insight into the content and quality of parent-facing manufacturer claims regarding commercial social robots for children.

A Scoping Review of the Use of Robotics Technologies for Supporting Social-Emotional Learning in Children with Autism

This scoping review synthesises the current research into robotics technologies for promoting social-emotional learning in children with autism spectrum disorder. It examines the types of robotics technologies employed, their applications, and the gaps in the existing literature. Our scoping review adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guidelines. The systematic search of relevant databases allowed us to identify studies that use robotics technologies for fostering social, emotional, and cognitive skills in young children with autism. Our review has revealed that various robots, such as Nao, Kaspar, and Zeno, have been used to support the development of social and emotional skills through imitation games, turn-taking, joint attention, emotional recognition, and conversation. As most of these studies were conducted in clinical settings, there is a need for further research in classroom and community-based environments. Additionally, the literature calls for more high-quality longitudinal studies to assess the long-term effectiveness and sustainability of robot-assisted therapy and to assess adaptive and personalised interventions tailored to individual needs. More emphasis is recommended on professional development for educators, parents, and health professionals to incorporate robotics technologies as evidence-based interventions as a pathway for creating inclusive learning environments for children with autism.

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects

Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).

Artificial intelligence in psychiatry research, diagnosis, and therapy

Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.

In-the-Wild Affect Analysis of Children with ASD Using Heart Rate

Recognizing the affective state of children with autism spectrum disorder (ASD) in real-world settings poses challenges due to the varying head poses, illumination levels, occlusion and a lack of datasets annotated with emotions in in-the-wild scenarios. Understanding the emotional state of children with ASD is crucial for providing personalized interventions and support. Existing methods often rely on controlled lab environments, limiting their applicability to real-world scenarios. Hence, a framework that enables the recognition of affective states in children with ASD in uncontrolled settings is needed. This paper presents a framework for recognizing the affective state of children with ASD in an in-the-wild setting using heart rate (HR) information. More specifically, an algorithm is developed that can classify a participant's emotion as positive, negative, or neutral by analyzing the heart rate signal acquired from a smartwatch. The heart rate data are obtained in real time using a smartwatch application while the child learns to code a robot and interacts with an avatar. The avatar assists the child in developing communication skills and programming the robot. In this paper, we also present a semi-automated annotation technique based on facial expression recognition for the heart rate data. The HR signal is analyzed to extract features that capture the emotional state of the child. Additionally, in this paper, the performance of a raw HR-signal-based emotion classification algorithm is compared with a classification approach based on features extracted from HR signals using discrete wavelet transform (DWT). The experimental results demonstrate that the proposed method achieves comparable performance to state-of-the-art HR-based emotion recognition techniques, despite being conducted in an uncontrolled setting rather than a controlled lab environment. The framework presented in this paper contributes to the real-world affect analysis of children with ASD using HR information. By enabling emotion recognition in uncontrolled settings, this approach has the potential to improve the monitoring and understanding of the emotional well-being of children with ASD in their daily lives.

Social skills training using multiple humanoid robots for individuals with autism spectrum conditions

Introduction

Social skills training (SST) is used to help individuals with autism spectrum conditions (ASC) better understand the perspectives of others and social interactions, develop empathy skills, and learn how to engage with others socially. However, many individuals with ASC cannot easily sustain high motivation and concentration during such an intervention when it is administered by humans. We developed a social skills training program using multiple humanoid robots (STUH), including an android robot, that aimed to enable individuals with ASC to become familiar with the perspectives of others and improve their sociability and empathy skills. The objective of the present study was to investigate the effectiveness of STUH for these individuals.

Methods

In STUH, we prepared 50 social exercises that consisted of conversations and behavioral interactions between an android robot and a simple humanoid robot. We prepared another humanoid robot that featured a cartoon-like and mechanical design, which played the role of host. In the first half-session of STUH, participants worked on the exercise from the perspective of an outsider. In the second half-session of STUH, they simulated experience by using robots as their avatars. The intervention associated with STUH was conducted for five days in total. We conducted an analysis of variance (ANOVA) featuring the intervention time point as the independent variable to examine changes in each score on the sociability index items.

Results

In total, 14 individuals with ASC participated in the study. The results of multiple comparison tests using the Bonferroni method indicated that all sociability index items improved between preintervention and follow-up. Our program enabled the participants to become familiar with the perspectives of others and improve their sociability.

Discussion

Given the promising results of this study, future studies featuring long-term follow-up should be conducted to draw definitive conclusions about the efficacy of our training system.

The Potential Use of Sex Robots in Adults with Autistic Spectrum Disorders: A Theoretical Framework

Although the importance of the sexual sphere for the health of all human beings has been recognized at an international level, often this is underestimated when it comes to disabilities and even more to intellectual disabilities. In fact, the idea that subjects with intellectual disabilities are not aware of their bodies and of their wishes in the sexual and emotional field is still widespread in our society, in such a way that they are considered as children in need of constant supervision. Moreover, further hints of criticism that can be raised are about the poor level of sexual education that is dedicated to these subjects, both by family members and by therapists. The last decades have been characterized by a considerable growth in the technological sector and many new instruments have been successfully used in the field of healthcare of weak or disabled subjects. A particularly fruitful branch has been robotics which, in subjects with autistic spectrum disorders (ASD), has revealed itself as an excellent support to stimulate communication and develop social skills. As in recent years the field of robotics has also been characterized by a strong interest in the sphere of sexuality, building and implementing what we now define as sex robots or sexbots, it could be interesting to start a debate on the potential that these new generation artificial agents could have in the field of care of subjects with ASD. These robots, possessing a technology based on stimulating verbal and nonverbal interaction, could be useful for an education that is not only sexual but also psycho-emotional in subjects with ASD.

Robot programming for a child with autism spectrum disorder: a pilot study

Children with autism spectrum disorder (ASD) show great interest in technological devices, and especially in robots. Several studies in the field have suggested that socially assistive robotics (SARs) can help children with ASD in the enhancement of their social skills and communication, and in the reduction of their stereotypical behaviors. Few published research results are available regarding robot programming or coding in the context of STEM education (Science, Technology, Engineering and Mathematics) for these children. In this pilot study, the authors designed and implemented educational activities with the robot 'Codey Rocky', a ready-to-use robot designed for code learning and programming by primary school children. In this pilot study, the participation of two eight-year-old schoolchildren, a girl with ASD and intellectual deficit and a boy of typical development in triadic interactions with the robot, led to the enhancement of the social and communication skills of the girl with ASD. A decrease in her challenging behaviors was also observed although she manifested repetitive and stereotyped behaviors throughout the educational sessions. The benefits, risks, and implications of the use of SARs for children with ASD are discussed.

A Novel Multi-Modal Teleoperation of a Humanoid Assistive Robot with Real-Time Motion Mimic

This research shows the development of a teleoperation system with an assistive robot (NAO) through a Kinect V2 sensor, a set of Meta Quest virtual reality glasses, and Nintendo Switch controllers (Joycons), with the use of the Robot Operating System (ROS) framework to implement the communication between devices. In this paper, two interchangeable operating models are proposed. An exclusive controller is used to control the robot's movement to perform assignments that require long-distance travel. Another teleoperation protocol uses the skeleton joints information readings by the Kinect sensor, the orientation of the Meta Quest, and the button press and thumbstick movements of the Joycons to control the arm joints and head of the assistive robot, and its movement in a limited area. They give image feedback to the operator in the VR glasses in a first-person perspective and retrieve the user's voice to be spoken by the assistive robot. Results are promising and can be used for educational and therapeutic purposes.

Neural and visual processing of social gaze cueing in typical and ASD adults

Atypical eye gaze in joint attention is a clinical characteristic of autism spectrum disorder (ASD). Despite this documented symptom, neural processing of joint attention tasks in real-life social interactions is not understood. To address this knowledge gap, functional-near infrared spectroscopy (fNIRS) and eye-tracking data were acquired simultaneously as ASD and typically developed (TD) individuals engaged in a gaze-directed joint attention task with a live human and robot partner. We test the hypothesis that face processing deficits in ASD are greater for interactive faces than for simulated (robot) faces. Consistent with prior findings, neural responses during human gaze cueing modulated by face visual dwell time resulted in increased activity of ventral frontal regions in ASD and dorsal parietal systems in TD participants. Hypoactivity of the right dorsal parietal area during live human gaze cueing was correlated with autism spectrum symptom severity: Brief Observations of Symptoms of Autism (BOSA) scores (r = âˆ'0.86). Contrarily, neural activity in response to robot gaze cueing modulated by visual acquisition factors activated dorsal parietal systems in ASD, and this neural activity was not related to autism symptom severity (r = 0.06). These results are consistent with the hypothesis that altered encoding of incoming facial information to the dorsal parietal cortex is specific to live human faces in ASD. These findings open new directions for understanding joint attention difficulties in ASD by providing a connection between superior parietal lobule activity and live interaction with human faces.

Lay Summary

Little is known about why it is so difficult for autistic individuals to make eye contact with other people. We find that in a live face-to-face viewing task with a robot, the brains of autistic participants were similar to typical participants but not when the partner was a live human. Findings suggest that difficulties in real-life social situations for autistic individuals may be specific to difficulties with live social interaction rather than general face gaze.

An Introduction to Artificial Intelligence in Developmental and Behavioral Pediatrics

ABSTRACT

Technological breakthroughs, together with the rapid growth of medical information and improved data connectivity, are creating dramatic shifts in the health care landscape, including the field of developmental and behavioral pediatrics. While medical information took an estimated 50 years to double in 1950, by 2020, it was projected to double every 73 days. Artificial intelligence (AI)-powered health technologies, once considered theoretical or research-exclusive concepts, are increasingly being granted regulatory approval and integrated into clinical care. In the United States, the Food and Drug Administration has cleared or approved over 160 health-related AI-based devices to date. These trends are only likely to accelerate as economic investment in AI health care outstrips investment in other sectors. The exponential increase in peer-reviewed AI-focused health care publications year over year highlights the speed of growth in this sector. As health care moves toward an era of intelligent technology powered by rich medical information, pediatricians will increasingly be asked to engage with tools and systems underpinned by AI. However, medical students and practicing clinicians receive insufficient training and lack preparedness for transitioning into a more AI-informed future. This article provides a brief primer on AI in health care. Underlying AI principles and key performance metrics are described, and the clinical potential of AI-driven technology together with potential pitfalls is explored within the developmental and behavioral pediatric health context.

Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review

In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.

AppraisalCloudPCT: A Computational Model of Emotions for Socially Interactive Robots for Autistic Rehabilitation

Computational models of emotions can not only improve the effectiveness and efficiency of human-robot interaction but also coordinate a robot to adapt to its environment better. When designing computational models of emotions for socially interactive robots, especially for robots for people with special needs such as autistic children, one should take into account the social and communicative characteristics of such groups of people. This article presents a novel computational model of emotions called AppraisalCloudPCT that is suitable for socially interactive robots that can be adopted in autistic rehabilitation which, to the best of our knowledge, is the first computational model of emotions built for robots that can satisfy the needs of a special group of people such as autistic children. To begin with, some fundamental and notable computational models of emotions (e.g., OCC, Scherer's appraisal theory, PAD) that have deep and profound influence on building some significant models (e.g., PRESENCE, iGrace, xEmotion) for socially interactive robots are revisited. Then, a comparative assessment between our AppraisalCloudPCT and other five significant models for socially interactive robots is conducted. Great efforts have been made in building our proposed model to meet all of the six criteria for comparison, by adopting the appraisal theories on emotions, perceptual control theory on emotions, a component model view of appraisal models, and cloud robotics. Details of how to implement our model in a socially interactive robot we developed for autistic rehabilitation are also elaborated in this article. Future studies should examine how our model performs in different robots and also in more interactive scenarios.

Important Preliminary Insights for Designing Successful Communication between a Robotic Learning Assistant and Children with Autism Spectrum Disorder in Germany

Early therapeutic intervention programs help children diagnosed with Autism Spectrum Disorder (ASD) to improve their socio-emotional and functional skills. To relieve the children’s caregivers while ensuring that the children are adequately supported in their training exercises, new technologies may offer suitable solutions. This study investigates the potential of a robotic learning assistant which is planned to monitor the children’s state of engagement and to intervene with appropriate motivational nudges when necessary. To analyze stakeholder requirements, interviews with parents as well as therapists of children with ASD were conducted. Besides a general positive attitude towards the usage of new technologies, we received some important insights for the design of the robot and its interaction with the children. One strongly accentuated aspect was the robot’s adequate and context-specific communication behavior, which we plan to address via an AI-based engagement detection system. Further aspects comprise for instance customizability, adaptability, and variability of the robot’s behavior, which should further be not too distracting while still being highly predictable.

Few-shot re-identification of the speaker by social robots

Nowadays advanced machine learning, computer vision, audio analysis and natural language understanding systems can be widely used for improving the perceptive and reasoning capabilities of the social robots. In particular, artificial intelligence algorithms for speaker re-identification make the robot aware of its interlocutor and able to personalize the conversation according to the information gathered in real-time and in the past interactions with the speaker. Anyway, this kind of application requires to train neural networks having available only a few samples for each speaker. Within this context, in this paper we propose a social robot equipped with a microphone sensor and a smart deep learning algorithm for few-shot speaker re-identification, able to run in real time over an embedded platform mounted on board of the robot. The proposed system has been experimentally evaluated over the VoxCeleb1 dataset, demonstrating a remarkable re-identification accuracy by varying the number of samples per speaker, the number of known speakers and the duration of the samples, and over the SpReW dataset, showing its robustness in real noisy environments. Finally, a quantitative evaluation of the processing time over the embedded platform proves that the processing pipeline is almost immediate, resulting in a pleasant user experience.

Sex and Sexuality in Autism Spectrum Disorders: A Scoping Review on a Neglected but Fundamental Issue

ASD consists of a set of permanent neurodevelopmental conditions, which are studded with social and communication differences, limited interests, and repetitive behaviors. Individuals with ASD have difficulty reading eye gestures and expressions, and may also have stereotyped or repetitive language, excessive adherence to routines, fixed interests, and rigid thinking. However, sexuality in adolescents and young adults with ASD is still a poorly studied and neglected issue. This review aims to evaluate sexual function and behavior in individuals with ASD to foster a greater understanding of this important, although often overlooked, issue. This review was conducted by searching peer-reviewed articles published between 01 June 2000 and 31 May 2022 using the following databases: PubMed, Embase, Cochrane Database, and Web of Science. A comprehensive search was conducted using the terms: "Autism" OR "ASD" AND "Sexuality" OR "Romantic relation" OR "sexual behavior" AND/OR "sexual awareness". After an accurate revision of 214 full manuscripts, 11 articles satisfied the inclusion/exclusion criteria. This review found that, although individuals with ASD may have sexual functioning, their sexuality is characterized by higher prevalence rates of gender dysphoria and inappropriate sexual behavior. Furthermore, sexual awareness is reduced in this patient population, and the prevalence of other variants of sexual orientation (i.e., homosexuality, asexuality, bisexuality, etc.) is higher in adolescents with ASD than in non-autistic peers. Sexual health and education should be included in the care path of patients with ASD in order to improve their quality of life and avoid/reduce inappropriate and risky behaviors.

Socially-Assistive Robots to Support Learning in Students on the Autism Spectrum: Investigating Educator Perspectives and a Pilot Trial of a Mobile Platform to Remove Barriers to Implementation

Technology offers educators tools that can tailor learning to students' learning styles and interests. Research into the use of socially-assistive robots as a learning support for children on the autism spectrum are showing promising results. However, to date, few schools have introduced these robots to support learning in students on the autism spectrum. This paper reports on a research project that investigated the barriers to implementing socially-assistive robot supported learning, and the expectations, perceived benefits and concerns of school teachers and therapists of students on the autism spectrum and adults on the autism spectrum. First, three focus groups were conducted with six adults on the autism spectrum, and 13 teachers and therapists of students from two autism-specific schools. During the focus groups, there was cautious optimism from participants about the value of socially-assistive robots for teaching support. While the data showed that participants were in favour of trialling socially-assistive robots in the classroom, they also raised several concerns and potential barriers to implementation, including the need for teacher training. In response to their concerns, the second part of the project focussed on developing a software platform and mobile application (app) to support the introduction of robots into autism-specific classrooms. The software platform and app were then trialled in two schools (n = 7 teachers and therapists). Results from focus groups indicated that participants believe socially-assistive robots could be useful for learning support, as the mobile app provides an easy to use tool to support preparing and conducting lessons that would motivate them to trial robots in the classroom.

Development of an Interactive Total Body Robot Enhanced Imitation Therapy for ASD children

Autism is a neurodevelopmental disorder in which the available therapies target the improvement of social skills, in order to ensure a high quality of life for the child. The use of Social Assistive Robots offers new therapeutic possibilities in which robots can act as therapy enhancers. IOGIOCO project emerges in this framework: it aims at the development of a Robot- Assisted Therapy protocol for the treatment of Autism Spectrum Disorder, through gesture training. The definition of these gestures and their recognition by the robot are parameters that directly affect the engagement of the children. However, the design of a protocol becomes harder in a highly unconstrained environment. Therefore, the current work aims at expanding the gesture set and improving the gesture recognition algorithm available in the IOGIOCO platform. More specifically, total body gestures have been added to the available upper limbs movements, and a custom Activity Detection method has been developed, which allows the identification of the time window in which a gesture is performed. The insertion of this method on a recognition algorithm based on a ResNet, a particular kind of Convolutional Neural Network, improved its F1-score from 57% obtained with the previously-available version, in a dataset of ASD children, to 76%, demonstrating the effectiveness of the Activity Detection method. Furthermore, the expansion of the interaction possibilities to total body movements was positively evaluated by the clinical staff, increasing the engagement of patients and the set of possible trained skills. Therefore, the results of the current work are encouraging. To reinforce the conclusions drawn, the proposed algorithm should be tested in real time on several autistic children within a complete Randomized Clinical Trial, also to study the effectiveness of this type of treatment. From the technical point of view, further improvements of the developed methodology should tackle the remained issues, such as further increasing the recognition capability, especially in the transitions from sitting to standing, that proved to be a hard task for the developed method.

Social Humanoid Robots for Children with Autism Spectrum Disorders: A Review of Modalities, Indications, and Pitfalls

Robot-assisted therapy (RAT) is a promising area of translational neuroscience for children with autism spectrum disorders (ASDs). It has been widely demonstrated that this kind of advanced technological tool provides a reliable and efficient intervention for promoting social skills and communication in children with ASD. This type of treatment consists of a human-assisted social robot acting as an intervention mediator to increase competence and skills in children with ASD. Several social robots have been validated in the literature; however, an explicit technical comparison among devices has never been performed. For this reason, in this article, we provide an overview of the main commercial humanoid robots employed for ASD children with an emphasis on indications for use, pitfalls to be avoided, and recent advances. We conclude that, in the near future, a new generation of devices with high levels of mobility, availability, safety, and acceptability should be designed for improving the complex triadic interaction among teachers, children, and robots.

The use of social robots with children and young people on the autism spectrum: A systematic review and meta-analysis

BACKGROUND

Robot-mediated interventions show promise in supporting the development of children on the autism spectrum.

OBJECTIVES

In this systematic review and meta-analysis, we summarize key features of available evidence on robot-interventions for children and young people on the autism spectrum aged up to 18 years old, as well as consider their efficacy for specific domains of learning.

DATA SOURCES

PubMed, Scopus, EBSCOhost, Google Scholar, Cochrane Library, ACM Digital Library, and IEEE Xplore. Grey literature was also searched using PsycExtra, OpenGrey, British Library EThOS, and the British Library Catalogue. Databases were searched from inception until April (6th) 2021.

SYNTHESIS METHODS

Searches undertaken across seven databases yielded 2145 articles. Forty studies met our review inclusion criteria of which 17 were randomized control trials. The methodological quality of studies was conducted with the Quality Assessment Tool for Quantitative Studies. A narrative synthesis summarised the findings. A meta-analysis was conducted with 12 RCTs.

RESULTS

Most interventions used humanoid (67%) robotic platforms, were predominantly based in clinics (37%) followed home, schools and laboratory (17% respectively) environments and targeted at improving social and communication skills (77%). Focusing on the most common outcomes, a random effects meta-analysis of RCTs showed that robot-mediated interventions significantly improved social functioning (g = 0.35 [95%CI 0.09 to 0.61; k = 7). By contrast, robots did not improve emotional (g = 0.63 [95%CI -1.43 to 2.69]; k = 2) or motor outcomes (g = -0.10 [95%CI -1.08 to 0.89]; k = 3), but the numbers of trials were very small. Meta-regression revealed that age accounted for almost one-third of the variance in effect sizes, with greater benefits being found in younger children.

CONCLUSIONS

Overall, our findings support the use of robot-mediated interventions for autistic children and youth, and we propose several recommendations for future research to aid learning and enhance implementation in everyday settings.

PROSPERO REGISTRATION

Our methods were preregistered in the PROSPERO database (CRD42019148981).

Design, Development, and a Pilot Study of a Low-Cost Robot for Child–Robot Interaction in Autism Interventions

Socially assistive robots are widely deployed in interventions with children on the autism spectrum, exploiting the benefits of this technology in social behavior intervention plans, while reducing their autistic behavior. Furthermore, innovations in modern technologies such as machine learning enhance these robots with great capabilities. Since the results of this implementation are promising, their total cost makes them unaffordable for some organizations while the needs are growing progressively. In this paper, a low-cost robot for autism interventions is proposed, benefiting from the advantages of machine learning and low-cost hardware. The mechanical design of the robot and the development of machine learning models are presented. The robot was evaluated by a small group of educators for children with ASD. The results of various model implementations, together with the design evaluation of the robot, are encouraging and indicate that this technology would be advantageous for deployment in child–robot interaction scenarios.

Elizabeth Usher memorial lecture: Speech-language pathology in the transformative age - valuing connectivity

Living in the transformative age is one of disruption, change, and infinite opportunity. However, living in a cloud-based world with self-driving cars, advanced robotics, artificial intelligence, e-health, 3-D printing, and COVID-19 can also be somewhat daunting, challenging, and even confronting. As speech-language pathologists, researchers, educators, and advocates, we need to be agile, more creative and connected to data, experiences, and people. Now more than ever, these connections will enable transformation and ensure the future of our profession.speech-language pathologists are now practising on a global scale, in multiple languages and unique contexts, and the education of our future workforce is critical. Over the past 10 years, there has been rapid growth in the number of speech-language pathology training programs delivered by universities in Australia, as well as a significant shift in the demand for services and changing employment opportunities. In Australia, the profession has been planning for the future; Making Futures Happen, Building a Future workforce, and re-developing our Professional Standards. But, are we really cognisant of the global challenges and opportunities for our profession? Do we really value global connectivity?In this discussion paper, authentic examples and plausible scenarios are being used to explore the global transformation of the speech-language pathology profession. Each will highlight some of the political, economic, societal, cultural, and technological influences on speech-language pathology research, teaching, and practices that are driving development, change, and innovation. Readers will be challenged to consider how thinking globally, with a focus on context, translation, and connection will enable them to rise to the challenges we face today and forge new paths for the future.

Reading the mind in cartoon eyes: Comparing human versus cartoon emotion recognition in those with high and low levels of autistic traits

People who have a high degree of autistic traits often underperform on theory of mind tasks such as perspective-taking or facial emotion recognition compared to those with lower levels of autistic traits. However, some research suggests that this may not be the case if the agent they are evaluating is anthropomorphic (i.e. animal or cartoon) rather than typically human. The present studies examined the relation between facial emotion recognition and autistic trait profiles in over 750 adults using either a standard or cartoon version of the Reading the Mind in the Eyes (RME) test. Results showed that those scoring above the clinical cut off for autistic traits on the Autism Quotient performed significantly worse than those with the lowest levels of autistic traits on the standard RME, while scores across these groups did not differ substantially on the cartoon version of the task. These findings add further evidence that theory of mind ability such as facial emotion recognition is not at a global deficit in those with a high degree of autistic traits. Instead, differences in this ability may be specific to evaluating human agents.

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.

Psychosocial and Behavioral Effects of the COVID-19 Pandemic on Children and Adolescents with Autism and Their Families: Overview of the Literature and Initial Data from a Multinational Online Survey

Since COVID-19 has become a pandemic, everyday life has seen dramatic changes affecting individuals, families, and children with and without autism. Among other things, these changes entail more time at home, digital forms of communication, school closures, and reduced support and intervention. Here, we assess the effects of the pandemic on quality of life for school-age autistic and neurotypical children and adolescents. First, we provide a comprehensive review of the current relevant literature. Next, we report original data from a survey conducted in several countries, assessing activities, well-being, and social life in families with autism, and their changes over time. We focus on differences between children with and without autism from within the same families, and on different outcomes for children with high- or low-functioning autism. While individuals with autism scored lower in emotional and social functioning than their neurotypical siblings, both groups of children showed comparable decreases in well-being and increases in anxiety, compared to before the pandemic. By contrast, decreases in adaptability were significantly more pronounced in autistic children and adolescents compared to neurotypical children and adolescents. Overall, although individual families reported some positive effects of pandemic restrictions, our data provide no evidence that these generalize across children and adolescents with autism, or even just to individuals with high-functioning autism. We discuss the increased challenges that need to be addressed to protect children and adolescents' well-being under pandemic conditions, but also point out potentials in the present situation that could be used towards social participation and success in older children and young adults with autism.

Artificial intelligence and Psychiatry: An overview

The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.

Visual and Hearing Sensitivity Affect Robot-Based Training for Children Diagnosed With Autism Spectrum Disorder

In this paper, we investigate the impact of sensory sensitivity during robot-assisted training for children diagnosed with Autism Spectrum Disorder (ASD). Indeed, user-adaptation for robot-based therapies could help users to focus on the training, and thus improve the benefits of the interactions. Children diagnosed with ASD often suffer from sensory sensitivity, and can show hyper or hypo-reactivity to sensory events, such as reacting strongly or not at all to sounds, movements, or touch. Considering it during robot therapies may improve the overall interaction. In the present study, thirty-four children diagnosed with ASD underwent a joint attention training with the robot Cozmo. The eight session training was embedded in the standard therapy. The children were screened for their sensory sensitivity with the Sensory Profile Checklist Revised. Their social skills were screened before and after the training with the Early Social Communication Scale. We recorded their performance and the amount of feedback they were receiving from the therapist through animations of happy and sad emotions played on the robot. Our results showed that visual and hearing sensitivity influenced the improvements of the skill to initiate joint attention. Also, the therapists of individuals with a high sensitivity to hearing chose to play fewer animations of the robot during the training phase of the robot activity. The animations did not include sounds, but the robot was producing motor noise. These results are supporting the idea that sensory sensitivity of children diagnosed with ASD should be screened prior to engaging the children in robot-assisted therapy.

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.

Android Robot Promotes Disclosure of Negative Narratives by Individuals With Autism Spectrum Disorders

Many individuals with autism spectrum disorders (ASD) demonstrate some challenges with personal narrative writing. Sentence completion tests (SCT) is a class of semi-structured projective techniques and encourage respondents to disclose their private narratives. Even in SCT, only providing beginning of sentences is inadequate to compensate atypicalities in their creativity and imagination, and self-disclosure is difficult for many individuals with ASD. It is reported that many individuals with ASD often achieve a higher degree of task engagement through interactions with robots and that robotic systems may be useful in eliciting and promoting social communication such as self-disclosure for some individuals with ASD. There is a possibility that exemplification by android robots in place of human interviewers can result in a higher degree of task engagement for individuals with ASD. The objective of this study was to investigate whether additional exemplifications by android robots in the SCT can prompt self-disclosure for individuals with ASD. We compared the difference in disclosure statements and subjective emotion in the testing paper of the SCT in additional exemplification by an android robot and a human interviewer. In addition, we assessed the disclosure statements and subjective emotions in the SCT, for which exemplifications were written on testing paper to make the comparison. Our quantitative data suggested that exemplification by android robot promoted more self-disclosure, especially about the negative topic compared to exemplification by a human interviewer and that written on test paper. In addition, the level of participant embarrassment in response to exemplification by the android robot seemed to be lower compared to that in the human interviewer condition. In the assessment and support for individuals with ASD, eliciting self-disclosure is a pressing issue. It is hoped that the appropriate use of robots will lead to a better understanding and support for their application.

Differences in the Optimal Motion of Android Robots for the Ease of Communications Among Individuals With Autism Spectrum Disorders

Android robots are employed in various fields. Many individuals with autism spectrum disorders (ASD) have the motivation and aptitude for using such robots. Interactions with these robots are structured to resemble social situations in which certain social behaviors can occur and to simulate daily life. Considering that individuals with ASD have strong likes and dislikes, ensuring not only the optimal appearance but also the optimal motion of robots is important to achieve smooth interaction and to draw out the potential of robotic interventions. We investigated whether individuals with ASD found it easier to talk to an android robot with little motion (i.e., only opening and closing its mouth during speech) or an android robot with much motion (i.e., in addition to opening and closing its mouth during speech, moving its eyes from side to side and up and down, blinking, deeply breathing, and turning or moving its head or body at random). This was a crossover study in which a total of 25 participants with ASD experienced mock interviews conducted by an android robot with much spontaneous facial and bodily motion and an android robot with little motion. We compared demographic data between participants who answered that the android robot with much motion was easier to talk to than android robot with little motion and those who answered the opposite. In addition, we investigated how each type of demographic data was related to participants' feeling of comfort in an interview setting with an android robot. Fourteen participants indicated that the android robot with little motion was easier to talk to than the robot with much motion, whereas 11 participants answered the opposite. There were significant differences between these two groups in the sensory sensitivity score, which reflects the tendency to show a low neurological threshold. In addition, we found correlations between the sensation seeking score, which reflects the tendency to show a high neurological threshold, and self-report ratings of comfort in each condition. These results provide preliminary support for the importance of setting the motion of an android robot considering the sensory traits of ASD.

Therapeutic engagement in robot-assisted psychological interventions: A systematic review

PURPOSE

Therapeutic engagement is a key component of psychological interventions. Robot-assisted psychological interventions appear to have therapeutic benefits for service users that are challenging to engage. However, engagement with robots in robot-assisted psychological interventions is not well understood. The aim of this systematic review is to evaluate the quality of therapeutic engagement in robot-assisted psychological interventions (PROSPERO: 122437).

METHODS

Scopus, Web of Science, PsycInfo and Medline were searched until 15 January 2021 for studies which quantitatively evaluated therapeutic engagement in robot-assisted psychological interventions. The Effective Public Health Practice Project (EPHPP) quality assessment tool was used to assess methodological dimensions of studies.

RESULTS

3647 studies were identified through database searching. Thirty studies (N = 1462), published between 2004 and 2020, and from 14 countries, were included. Robots were typically toy animals or humanoids and were used to provide support and improve wellbeing through social interaction. Studies primarily tested robots on older adults with dementia and children with autism and indicated positive therapeutic engagement. Twelve studies included a control group. EPHPP ratings were 'strong' (N = 1), 'moderate' (N = 10) and 'weak' (N = 19).

CONCLUSIONS

Therapeutic engagement between service users and robots is generally positive. Methodological limitations of studies, such as small sample sizes, and lack of control groups and longitudinal data, mean that the field is in early stages of its development and conclusions should be drawn with caution. There are important practical and ethical implications for policymakers to consider, such as responsible clinical practice and how service users may understand the therapeutic relationship with robots.