An approach to promote social and communication behaviors in children with autism spectrum disorders : robot based intervention

Most autistic people present some difficulties in developing social behavior, living in their own world. This study has the goal to improve the social life of children with autism with a main focus in promoting their social interaction and communication. It is necessary to call for children’s attention and enforce their collaboration, where a robot, LEGO MindStorm, behaves as a mediator/promoter of this interaction. A set of experiments designed to share objects and fulfill simple orders, by the 11 years old autistic child at the time of daily routine work and in-game with the robot, are described. The generalization of the acquired skills by the child in new contexts and environments are also tested. Results are described showing the outcomes of the experiments.Fundação para a Ciência e a Tecnologia (FCT) - R&D projecto RIPD/ADA/109407/200

Human-centred design methods : developing scenarios for robot assisted play informed by user panels and field trials

Original article can be found at: http://www.sciencedirect.com/ Copyright ElsevierThis article describes the user-centred development of play scenarios for robot assisted play, as part of the multidisciplinary IROMEC1 project that develops a novel robotic toy for children with special needs. The project investigates how robotic toys can become social mediators, encouraging children with special needs to discover a range of play styles, from solitary to collaborative play (with peers, carers/teachers, parents, etc.). This article explains the developmental process of constructing relevant play scenarios for children with different special needs. Results are presented from consultation with panel of experts (therapists, teachers, parents) who advised on the play needs for the various target user groups and who helped investigate how robotic toys could be used as a play tool to assist in the children’s development. Examples from experimental investigations are provided which have informed the development of scenarios throughout the design process. We conclude by pointing out the potential benefit of this work to a variety of research projects and applications involving human–robot interactions.Peer reviewe

The Key Artificial Intelligence Technologies in Early Childhood Education: A Review

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we provide an up-to-date and in-depth overview of the key AI technologies in ECE that provides a historical perspective, summarizes the representative works, outlines open questions, discusses the trends and challenges through a detailed bibliometric analysis, and provides insightful recommendations for future research. We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder. This paper significantly contributes to provide an up-to-date and in-depth survey that is suitable as introductory material for beginners to AI in ECE, as well as supplementary material for advanced users.Comment: 39 pages, 9 figures, 4 table

AUTISTHERAPIBOT: A New Robotic Approach for Autistic Children

Recent studies unravels that there are a lot of negative implications happen in those children who suffers from Autistic Spectrum Disorder (ASD) which include Asperger and Kanner Syndrome. All these syndromes shares similar characteristics which are difficulties in socialization, communications and repetitive inflexible behaviors. This problem leads to difficulties in learning especially to those children suffering from autistic disorder. Thus, the objective of this project is to investigate the current teaching method used by the autism therapists in Perak at the selected special school and is to develop an autonomous robotic system to aid the teaching and learning process of autistic children in Malaysia. The LEGO Mindstorm NXT is used as an alternative approach in educating those children with ASD namely those in the Asperger range since Asperger is more common compared to Kanner. The prototype, is tested on a group of autistic kids from selected public and private autism institution. The project focuses on how to attract the autistic children and sustain their learning via the usage of robotic application such as the LEGO Mindstorm NXT. In a preliminary investigation, multiple robotic designs and programming approach are experimented to produce a robotic application that can engage with the target autistic children in order to facilitate their process of learning via the intervention of their therapists. Interviews with the therapists and live observation at the selected special school are conducted to understand the traditional learning process that are used by the therapists and identify the weaknesses in it to improvise it. The results from the investigation and tests shows that this robotic systems helps a lot in assisting the therapist in educating autistic children and scores way better if compared to the current methods they are using. The significance of this robotic application is to fulfill the depravedness in the learning capabilities of the autistic children and also to assist the therapists in their daily routine

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

”Where is your nose?” : developing body awareness skills among children with autism using a humanoid robot

This article describes an exploratory study in which children with autism interact with KASPAR, a humanoid robot, equipped with tactile sensors able to distinguish a gentle from a harsh touch, and to respond accordingly. The study investigated a novel scenario for robot-assisted play, namely to increase body awareness with tasks that taught the children about the identification of human body parts. Based on our analysis of the childrens behaviours while interacting with KASPAR, our results show that the children started looking for a longer period of time to the experimenter, and a lot of interest in touching the robot was observed. They also show that the robot can be considered as a tool for prolonging the attention span of the children, being a social mediator during the interaction between the child and the experimenter. The results are primarily based on the analysis of video data of the interaction. Overall, this first study into teaching children with autism about body parts using a humanoid robot highlighted issues of scenario development, data collection and data analysis that will inform future studies.(undefined

Evaluating the role of a humanoid robot to support learning in children with profound and multiple disabilities

Purpose The purpose of this paper is to identify ways teachers might employ a robot to achieve learning objectives with pupils with intellectual disabilities and potential outcome measures. Design/methodology/approach A series of five case studies where teacher-pupil dyads were observed during five planned video-recorded sessions with a humanoid robot. Engagement was rated in a classroom setting and during the last session with the robot. Video recordings were analysed for duration of engagement, teacher assistance and number of goals achieved. Findings Teachers identified a wide range of learning objectives ranging from an appreciation of cause and effect to improving the pupil's sense of direction. The robot's role could be to reward behaviour, provide cues or provide an active element to learning. Rated engagement was significantly higher with the robot than in the classroom. Research limitations/implications A robot with a range of functions that allowed it to be engaging and motivating for the wide range of pupils in special education would be expensive and require teachers to learn how to use it. The findings identify ways to provide evidence that this expenditure of time and money is worthwhile. Originality/value There is almost no research teachers can refer to on using robots to support learning in children with intellectual disabilities. This paper is therefore of value for researchers who wish to investigate using robots to educate children with intellectual disabilities, as it can provide vital information to aid study design

Mapping Robots to Therapy and Educational Objectives for Children with Autism Spectrum Disorder

The aim of this study was to increase knowledge on therapy and educational objectives professionals work on with children with autism spectrum disorder (ASD) and to identify corresponding state of the art robots. Focus group sessions (n = 9) with ASD professionals (n = 53) from nine organisations were carried out to create an objectives overview, followed by a systematic literature study to identify state of the art robots matching these objectives. Professionals identified many ASD objectives (n = 74) in 9 different domains. State of the art robots addressed 24 of these objectives in 8 domains. Robots can potentially be applied to a large scope of objectives for children with ASD. This objectives overview functions as a base to guide development of robot interventions for these children

Educators' views on using humanoid robots with autistic learners in special education settings in England

Researchers, industry, and practitioners are increasingly interested in the potential of social robots in education for learners on the autism spectrum. In this study, we conducted semi-structured interviews and focus groups with educators in England to gain their perspectives on the potential use of humanoid robots with autistic pupils, eliciting ideas and specific examples of potential use. Understanding educator views is essential, because they are key “gatekeepers” for the potential adoption of robots, who would directly facilitate future use with pupils. Educators were provided with several example images (e.g., NAO, KASPAR, Milo), but did not directly interact with robots or receive information on current technical capabilities. The goal was for educators to respond to the general concept of humanoid robots as an educational tool, rather than to focus on the existing uses or behaviour of a particular robot. Thirty-one autism education staff participated, representing a range of special education settings and age groups as well as multiple professional roles (e.g., teachers, teaching assistants, speech and language therapists). Thematic analysis of the interview transcripts identified four themes: Engagingness of robots, Predictability and consistency, Roles of robots in autism education, and Need for children to interact with people, not robots. Almost all interviewees were receptive toward using humanoid robots in the classroom. They perceived future robot use as likely posing a series of complex cost-benefit trade-offs over time. For example, a highly motivating, predictable social robot might increase children’s readiness to learn in the classroom, but it might also prevent children from engaging fully with other people or activities. Educator views also assumed that skills learned with a robot would generalise, and that robots’ predictability is beneficial for autistic children – claims that need further supporting evidence. These interview results offer many points of guidance to the HRI research community about how humanoid robots could meet the needs of autistic learners, as well as identifying issues that will need to be resolved for robots to be both acceptable and successfully deployed in special education contexts