Development of skills in children with ASD using a robotic platform

The interaction and communication skills are essential to live in society. However, individuals with autism spectrum disorders (ASD) have a gap in these abilities which affects their daily life. Previous studies suggest that children with ASD demonstrate some positive behaviors in presence of a robotic platform. This study intends to evaluate the effect of a robotic platform on children with ASD, checking if the platform can be a stimulating agent for children's interaction, as well as a skill learning promoter. So, it is used the robot Lego Mindstorms NXT as a mediator/reward to encourage children with ASD to interact with others and also to learn some cognitive skills.The authors are grateful to teachers and students of the primary and secondary schools of Aver-o-Mar and their parents for their participation in the project. The authors are also grateful to the Portuguese Foundation for funding through the R&D project RIPD/ADA/109407/2009

Social robots in educational contexts: developing an application in enactive didactics

Due to advancements in sensor and actuator technology robots are becoming more and more common in everyday life. Many of the areas in which they are introduced demand close physical and social contact. In the last ten years the use of robots has also increasingly spread to the field of didactics, starting with their use as tools in STEM education. With the advancement of social robotics, the use of robots in didactics has been extended also to tutoring situations in which these \u201csocially aware\u201d robots interact with mainly children in, for example, language learning classes. In this paper we will give a brief overview of how robots have been used in this kind of settings until now. As a result it will become transparent that the majority of applications are not grounded in didactic theory. Recognizing this shortcoming, we propose a theory driven approach to the use of educational robots, centred on the idea that the combination of enactive didactics and social robotics holds great promises for a variety of tutoring activities in educational contexts. After defining our \u201cEnactive Robot Assisted Didactics\u201d approach, we will give an outlook on how the use of humanoid robots can advance it. On this basis, at the end of the paper, we will describe a concrete, currently on-going implementation of this approach, which we are realizing with the use of Softbank Robotics\u2019 Pepper robot during university lectures

Social Robots to enhance therapy and interaction for children: From the design to the implementation "in the wild"

En les últimes dues dècades els robots socials s'han convertit en un camp emergent en el qual encara hi ha molt per fer. Aquest camp requereix coneixements en mecànica, control, intel·ligència artificial, sistemes, etc., però també en psicologia, disseny, ètica, etc. El nostre grup de recerca de perfil interdisciplinari ha estat treballant en el disseny de robots socials en diferents aplicacions per a nens amb necessitats especials. L'objectiu d'aquesta tesi és investigar diferents escenaris en teràpia o educació on els robots socials podrien ser una eina útil per als nens. Es van realitzar 4 estudis amb diferents propòsits: (1) dissenyar activitats amb robòtica de LEGO per avaluar el comportament social dels nens amb trastorn de l'espectre autista (TEA) (entre companys i amb adults) i analitzar la seva efectivitat, (2) dissenyar un robot social per recuperar les funcionalitats més afectades a causa de traumatismes cranioencefàlics (TCE) en nens i veure l'eficàcia del tractament, (3) proporcionar un robot mascota per alleujar els sentiments d'ansietat, solitud i estrès en nens hospitalitzats, i (4) comprovar com un robot amb comportament social i amb una personalització versus robots sense aquestes característiques mostra diferències en termes d'interacció amb nens i, per tant, pot ajudar en l'efectivitat de diferents tractaments com hem esmentat anteriorment. Els resultats van revelar diferents resultats depenent de l'aplicació: (1) efectivitat amb la plataforma robòtica social que vam dissenyar en el tractament neuropsicològic per a aquells nens afectats per TCE, (2) eficàcia amb les activitats de robòtica de LEGO dissenyades per un grup de terapeutes en termes de millora d'habilitats socials (3) un efecte positiu entre els mediadors i facilitadors de la interacció i les relacions entre els diferents agents involucrats en el procés de la cura: pacients hospitalitzats, familiars, voluntaris i personal clínic, i (4) una interacció diferent, en termes de temps, entre els dos grups durant període de dues setmanes.En las últimas dos décadas los robots sociales se han convertido en un campo emergente en el que todavía hay mucho por hacer. Este campo requiere conocimientos en mecánica, control, inteligencia artificial, sistemas, etc., pero también en psicología, diseño, ética, etc. Nuestro grupo de investigación de perfil interdisciplinar ha estado trabajando en el diseño de robots sociales en diferentes aplicaciones para niños con necesidades especiales. El objetivo de esta tesis es investigar diferentes escenarios en terapia o educación donde los robots sociales podrían ser una herramienta útil para los niños. Se realizaron 4 estudios con diferentes propósitos: (1) diseñar actividades con robótica de LEGO para evaluar el comportamiento social de los niños con trastorno del espectro autista (TEA) (entre compañeros y con adultos) y analizar su efectividad, (2) diseñar un robot social para recuperar las funcionalidades más afectadas a causa de traumatismos craneoencefálicos (TCE) en niños y ver la eficacia del tratamiento, (3) proporcionar un robot mascota para aliviar los sentimientos de ansiedad, soledad y estrés en niños hospitalizados, y (4) comprobar como un robot con comportamiento social y con una personalización versus robots sin esas características muestra diferencias en términos de interacción con niños y, por tanto, puede ayudar en la efectividad de diferentes tratamientos como mencionamos anteriormente. Los resultados revelaron diferentes resultados dependiendo de la aplicación: (1) efectividad con la plataforma robótica social que diseñamos en el tratamiento neuropsicológico para aquellos niños afectadas por TCE, (2) eficacia con las actividades de robótica de LEGO diseñadas por un grupo de terapeutas en términos de mejora de habilidades sociales (3) un efecto positivo entre los mediadores y facilitadores de la interacción y las relaciones entre los diferentes agentes involucrados en el proceso del cuidado: pacientes hospitalizados, familiares, voluntarios y personal clínico, y (4) una interacción diferente, en términos de tiempo, entre ambos grupos en el promedio de un período de dos semanas.Over the past two decades social robots have become an emerging field where there are many things still to work on. This field not only requires knowledge in mechanics, control, artificial intelligence, systems, etc., but also in psychology, design, ethics, etc. Our multidisciplinary research group has been working on designing social robotic platforms in different applications for children with special needs. The aim of this thesis is to investigate different scenarios in therapy or education where social robots could be a useful tool for children. We ran 4 studies with different purposes: (1) to design activities with LEGO robotics to assess children with autism spectrum disorder (ASD) social behaviour (between peers and with adults) and to analyze the effectiveness, (2) to design a social robotic platform to recover the functionalities most affected by traumatic brain injuries (TBI) in children and see the effectiveness of the treatment, (3) to provide a pet robot to alleviate feelings of anxiety, loneliness and stress of long-term children inpatient and their bystanders, and (4) to verify how a robot with social behaviour and personalization verses those robots without, shows differences in terms of interaction with children and thus, helps the effectiveness of different treatments as we mention above. The results revealed different outcomes depending on the application: (1) effectiveness with the social robotic platform that we designed in neuropsychological treatment in those areas affected by TBI, (2) effectiveness with the LEGO robotics activities designed by a group of therapists in terms of improvement of the social skills and engagement, (3) a positive effect within mediators and facilitators of interaction and relationships between the different agents involved in the caring process: in-patients, relatives, volunteers and clinical staff (4) slight evidence towards a different interaction, in terms of time, between both groups in the average of a two-week period

Behavioural attentiveness patterns analysis – detecting distraction behaviours

The capacity of remaining focused on a task can be crucial in some circumstances. In general, this ability is intrinsic in a human social interaction and it is naturally used in any social context. Nevertheless, some individuals have difficulties in remaining concentrated in an activity, resulting in a short attention span. Children with Autism Spectrum Disorder (ASD) are a special example of such individuals. ASD is a group of complex developmental disorders of the brain. Individuals affected by this disorder are characterized by repetitive patterns of behaviour, restricted activities or interests, and impairments in social communication. The use of robots has already proved to encourage the developing of social interaction skills lacking in children with ASD. However, most of these systems are controlled remotely and cannot adapt automatically to the situation, and even those who are more autonomous still cannot perceive whether or not the user is paying attention to the instructions and actions of the robot. Following this trend, this dissertation is part of a research project that has been under development for some years. In this project, the Robot ZECA (Zeno Engaging Children with Autism) from Hanson Robotics is used to promote the interaction with children with ASD helping them to recognize emotions, and to acquire new knowledge in order to promote social interaction and communication with the others. The main purpose of this dissertation is to know whether the user is distracted during an activity. In the future, the objective is to interface this system with ZECA to consequently adapt its behaviour taking into account the individual affective state during an emotion imitation activity. In order to recognize human distraction behaviours and capture the user attention, several patterns of distraction, as well as systems to automatically detect them, have been developed. One of the most used distraction patterns detection methods is based on the measurement of the head pose and eye gaze. The present dissertation proposes a system based on a Red Green Blue (RGB) camera, capable of detecting the distraction patterns, head pose, eye gaze, blinks frequency, and the user to position towards the camera, during an activity, and then classify the user's state using a machine learning algorithm. Finally, the proposed system is evaluated in a laboratorial and controlled environment in order to verify if it is capable to detect the patterns of distraction. The results of these preliminary tests allowed to detect some system constraints, as well as to validate its adequacy to later use it in an intervention setting.A capacidade de permanecer focado numa tarefa pode ser crucial em algumas circunstâncias. No geral, essa capacidade é intrínseca numa interação social humana e é naturalmente usada em qualquer contexto social. No entanto, alguns indivíduos têm dificuldades em permanecer concentrados numa atividade, resultando num curto período de atenção. Crianças com Perturbações do Espectro do Autismo (PEA) são um exemplo especial de tais indivíduos. PEA é um grupo de perturbações complexas do desenvolvimento do cérebro. Os indivíduos afetados por estas perturbações são caracterizados por padrões repetitivos de comportamento, atividades ou interesses restritos e deficiências na comunicação social. O uso de robôs já provaram encorajar a promoção da interação social e ajudaram no desenvolvimento de competências deficitárias nas crianças com PEA. No entanto, a maioria desses sistemas é controlada remotamente e não consegue-se adaptar automaticamente à situação, e mesmo aqueles que são mais autônomos ainda não conseguem perceber se o utilizador está ou não atento às instruções e ações do robô. Seguindo esta tendência, esta dissertação é parte de um projeto de pesquisa que vem sendo desenvolvido há alguns anos, onde o robô ZECA (Zeno Envolvendo Crianças com Autismo) da Hanson Robotics é usado para promover a interação com crianças com PEA, ajudando-as a reconhecer emoções, adquirir novos conhecimentos para promover a interação social e comunicação com os pares. O principal objetivo desta dissertação é saber se o utilizador está distraído durante uma atividade. No futuro, o objetivo é fazer a interface deste sistema com o ZECA para, consequentemente, adaptar o seu comportamento tendo em conta o estado afetivo do utilizador durante uma atividade de imitação de emoções. A fim de reconhecer os comportamentos de distração humana e captar a atenção do utilizador, vários padrões de distração, bem como sistemas para detetá-los automaticamente, foram desenvolvidos. Um dos métodos de deteção de padrões de distração mais utilizados baseia-se na medição da orientação da cabeça e da orientação do olhar. A presente dissertação propõe um sistema baseado numa câmera Red Green Blue (RGB), capaz de detetar os padrões de distração, orientação da cabeça, orientação do olhar, frequência do piscar de olhos e a posição do utilizador em frente da câmera, durante uma atividade, e então classificar o estado do utilizador usando um algoritmo de “machine learning”. Por fim, o sistema proposto é avaliado num ambiente laboratorial, a fim de verificar se é capaz de detetar os padrões de distração. Os resultados destes testes preliminares permitiram detetar algumas restrições do sistema, bem como validar a sua adequação para posteriormente utilizá-lo num ambiente de intervenção

Are future psychologists willing to accept and use a humanoid robot in their practice? Italian and English students' perspective.

Despite general scepticism from care professionals, social robotics research is providing evidence of successful application in education and rehabilitation in clinical psychology practice. In this article, we investigate the cultural influences of English and Italian psychology students in the perception of usefulness and intention to use a robot as an instrument for future clinical practice and, secondly, the modality of presentation of the robot by comparing oral versus video presentation. To this end, we surveyed 158 Italian and British-English psychology students after an interactive demonstration using a humanoid robot to evaluate the social robot’s acceptance and use. The Italians were positive, while the English were negative towards the perceived usefulness and intention to use the robot in psychological practice in the near future. However, most English and Italian respondents felt they did not have the necessary abilities to make good use of the robot. We concluded that it is necessary to provide psychology students with further knowledge and practical skills regarding social robotics, which could facilitate the adoption and use of this technology in clinical settings

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\u27 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

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

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197