A Randomized Controlled Trial of an Intelligent Robotic Response to Joint Attention Intervention System

Assisted Robots in Therapies for Children with Autism in Early Childhood

Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, communication, and flexibility in reasoning. There are different types of treatment (pharmacological, educational, psychological, and rehabilitative). Currently, one way to address this problem is by using robotic systems to address the abilities that are altered in these children. The aim of this review will be to analyse the effectiveness of the incorporation of the different robotic systems currently existing in the treatment of children up to 10 years of age diagnosed with autism. A systematic review has been carried out in the PubMed, Scopus, Web of Science, and Dialnet databases, with the following descriptors: child, autism, and robot. The search yielded 578 papers, and nine were selected after the application of the PRISMA guideline. The quality of the studies was analysed with the PEDRo scale, and only those with a score between four and six were selected. From this study, the conclusion is that the use of robots, in general, improves children's behaviour in the short term, but longer-term experiences are necessary to achieve more conclusive results.

Optimizing Voice Recognition Informatic Robots for Effective Communication in Outpatient Settings

Aim/Objective Within the dynamic healthcare technology landscape, this research aims to explore patient inquiries within outpatient clinics, elucidating the interplay between technology and healthcare intricacies. Building upon the initial intelligent guidance robot implementation shortcomings, this investigation seeks to enhance informatic robots with voice recognition technology. The objective is to analyze users' vocal patterns, discern age-associated vocal attributes, and facilitate age differentiation through subtle vocal nuances to enhance the efficacy of human-robot communication within outpatient clinical settings. Methods This investigation employs a multi-faceted approach. It leverages voice recognition technology to analyze users' vocal patterns. A diverse dataset of voice samples from various age groups was collected. Acoustic features encompassing pitch, formant frequencies, spectral characteristics, and vocal tract length are extracted from the audio samples. The Mel Filterbank and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for speech and audio processing tasks alongside machine learning algorithms to assess and match vocal patterns to age-related traits. Results The research reveals compelling outcomes. The incorporation of voice recognition technology contributes to a significant improvement in human-robot communication within outpatient clinical settings. Through accurate analysis of vocal patterns and age-related traits, informatic robots can differentiate age through nuanced verbal cues. This augmentation leads to enhanced contextual understanding and tailored responses, significantly advancing the efficiency of patient interactions with the robots. Conclusion Integrating voice recognition technology into informatic robots presents a noteworthy advancement in outpatient clinic settings. By enabling age differentiation through vocal nuances, this augmentation enhances the precision and relevance of responses. The study contributes to the ongoing discourse on the dynamic evolution of healthcare technology, underscoring the complex synergy between technological progression and the intricate realities within healthcare infrastructure. As healthcare continues to metamorphose, the seamless integration of voice recognition technology marks a pivotal stride in optimizing human-robot communication and elevating patient care within outpatient settings.

Information and communication technologies-based interventions for children with autism spectrum conditions: a systematic review of randomized control trials from a positive technology perspective

Information and communication technologies (ICTs) have become more widely used in the past years to help people with autism spectrum conditions (ASC). Serious games embedded into computers or tablets, as well as social robots, are the most employed ICT-related tools that are appealing to and appropriate for autistic children. The goal of ICT applications is to enhance behavioral abnormalities associated with ASC while also creating an interactive link between one person and one computer. Comparatively, to human-based therapy, ICT tools aid to inspire autistic children by providing predictability and regularity of tasks. Regaining social skills is the primary behavioral goal for which ICT tools have been designed and implemented. In the past several years, many studies have been created to show how effective it is at improving targeted behaviors. However, only a small number of researchers have used an RCT approach to evaluate its effectiveness. In this systematic review, we only included RCT studies where ICT technologies were used to help children with ASC in improving their social skills. Only 14 RCT studies satisfied the criteria and 12 described significant improvements, showing how the use of technology in educational contexts produced better improvement in developing several social skill facets with respect to the traditional face-to-face approach. Some studies used interventions and outcome measures focused on the core ASC symptoms, but many others addressed neurocognitive functions directly, like social cognition or emotional regulation, while other more general functions such as language or adaptive behaviors. We propose a classification based on processes and outcome measures to foster future research in this specific area of research. The behavioral intervention mediated by technological tools such as computer-based, tablet, and social robotics, undoubtedly provides a comfortable environment that promotes constant learning for people with ASC. Evidence provided in this review highlights the translational potential of this field of study in primary care practice and educational settings.

Comparing the effectiveness of robot-based to human-based intervention in improving joint attention in autistic children

Background

Children with autism have impairments in initiation of joint attention (IJA) and response to joint attention (RJA).

Aims

The present study compared the learning effectiveness of robot-based intervention (RBI) with that of content-matched human-based intervention (HBI) in improving joint attention (JA). We examined whether RBI would enhance RJA, in comparison to HBI. We also examined whether RBI would increase IJA, in comparison to HBI.

Methods and procedures

Thirty-eight Chinese-speaking children with autism aged 6 to 9 years were randomly assigned to RBI and HBI groups. Before intervention, their autism severity, cognitive abilities, and language skills were assessed. Each child received six 30-min training sessions over 3 weeks. During training, he/she watched one or two robot/human dramas twice where two robot/human actors demonstrated eye contact and RJA.

Outcomes and results

Children in the RBI (but not HBI) group produced more RJA and IJA behaviors in the delayed post-test than in the pre-test. Parents of the RBI children rated the program more positively than those of the HBI children.

Conclusions and implications

RBI may be more effective than HBI in promoting JA in autistic children with high support needs. Our findings shed light on the application of robot dramas in enhancing social communication skills.

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.

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).

Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child’s behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child’s behavior.

An Immersive Computer-Mediated Caregiver-Child Interaction System for Young Children With Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) affects 1 in 54 children in the United States. A core social communication skill negatively impacted by ASD is joint attention (JA), which influences the development of language, cognitive, and social skills from infancy onward. Although several technology-based JA studies have shown potential, they primarily focus on response to joint attention (RJA). The other important component of JA, the initiation of joint attention (IJA), has received less attention from a technology-based intervention perspective. In this work, we present an immersive Computer-mediated Caregiver-Child Interaction (C3I) system to help children with ASD practice IJA skills. C3I is a novel computerized intervention system that integrates a caregiver in the teaching loop, thereby preserving the advantages of both human and computer-administered intervention. A feasibility study with 6 dyads (caregiver-child with ASD) was conducted. A near significant increase with medium effect size on IJA performance was observed. Meanwhile, physiology-based stress analysis showed that C3I did not increase stress of the caregivers over the course of the study. To the best of our knowledge, this is the first autonomous system designed for teaching IJA skills to children with ASD incorporating caregivers within the loop to enhance the potential for generalization in real-world.

Are social robots ready yet to be used in care and therapy of autism spectrum disorder: A systematic review of randomized controlled trials

Autism is a neurodevelopmental disorder that affects the everyday life of people who have this lifelong condition. Robots hold great promise for uplifting therapy and care of the affected population. We searched Scopus, Medline, ScienceDirect, Web of Science, and PubMed databases for randomized controlled trials that had evaluated robot use in the therapy of people with autism, to see how effective social robots have been incorporated in autism care. Out of 240 papers initially identified, 19 satisfied the inclusion criteria and were fully evaluated. Overall, 10 different robots were utilized in the trials, out of which, four were non-humanoids. The number of papers with positive results for using robots on the main and secondary parameters was 11 and 5, respectively. Three papers reported that robot groups did not achieve better results than others. Robots in the papers included here were mainly added as the "entertainment agent" to elicit greater engagement from the participants, which is understandable, as robots at this stage might not be ready yet to deliver high-end care.

Repetitive Robot Behavior Impacts Perception of Intentionality and Gaze-Related Attentional Orienting

Gaze behavior is an important social signal between humans as it communicates locations of interest. People typically orient their attention to where others look as this informs about others' intentions and future actions. Studies have shown that humans can engage in similar gaze behavior with robots but presumably more so when they adopt the intentional stance toward them (i.e., believing robot behaviors are intentional). In laboratory settings, the phenomenon of attending toward the direction of others' gaze has been examined with the use of the gaze-cueing paradigm. While the gaze-cueing paradigm has been successful in investigating the relationship between adopting the intentional stance toward robots and attention orienting to gaze cues, it is unclear if the repetitiveness of the gaze-cueing paradigm influences adopting the intentional stance. Here, we examined if the duration of exposure to repetitive robot gaze behavior in a gaze-cueing task has a negative impact on subjective attribution of intentionality. Participants performed a short, medium, or long face-to-face gaze-cueing paradigm with an embodied robot while subjective ratings were collected pre and post the interaction. Results show that participants in the long exposure condition had the smallest change in their intention attribution scores, if any, while those in the short exposure condition had a positive change in their intention attribution, indicating that participants attributed more intention to the robot after short interactions. The results also show that attention orienting to robot gaze-cues was positively related to how much intention was attributed to the robot, but this relationship became more negative as the length of exposure increased. In contrast to subjective ratings, the gaze-cueing effects (GCEs) increased as a function of the duration of exposure to repetitive behavior. The data suggest a tradeoff between the desired number of trials needed for observing various mechanisms of social cognition, such as GCEs, and the likelihood of adopting the intentional stance toward a robot.

Optimal robot for intervention for individuals with autism spectrum disorders

With recent rapid advances in technology, human-like robots have begun functioning in a variety of ways. As increasing anecdotal evidence suggests, robots may offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Individuals with ASD often achieve a higher degree of task engagement through the interaction with robots than through interactions with human trainees. The type and form of robots to be used for individuals with ASD have been meticulously considered. Simple robots and animal robots are acceptable because of their simplicity and the ease of interesting and engaging interactions. Android robots have the benefit of the potential of generalization into daily life to some extent. Considering the affinity between robots and users is important to draw out the potential capabilities of robotic intervention to the fullest extent. In the robotic condition, factors such as the appearance, biological motion, clothes, hairstyle, and disposition are important. Many factors of a user, such as age, sex, and IQ, may also affect the affinity of individuals with ASD toward a robot. The potential end-users of this technology may be unaware or unconvinced of the potential roles of robots in ASD interventions. If trainers have extensive experience in using robots, they can identify many potential roles of robots based on their experience. To date, only a few studies have been conducted in the field of robotics for providing assistance to individuals with ASD, and future studies are needed to realize an optimal robot for this purpose.