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Realistic Humanlike Robots for Treatment of ASD, Social Training, and Research; Shown to Appeal to Youths with ASD, Cause Physiological Arousal, and Increase Human-to-Human Social Engagement
... Some initial analyses regarding requirements for child-and ASD-appropriate robot design show that the robot should be customizable and easily reprogrammable, not too human-like (appearance clearly distinguishable from a human), below child height, robust, and congruent, for instance regarding appearance and intonation [47,[53][54][55][56]. Diverse international studies (e.g., from UK, USA, Italy, and Japan) delivered interesting insights in the design and functioning of the robots to achieve the aforementioned positive effects [39,45,57]. ...
... In contrast to these rather simplistic designs, researchers in Italy and the US investigated the use of more realistic-looking robots, suspecting greater effectiveness regarding the transfer of the learnings from the robot-human interaction to human-human interaction [57]. In their cross-cultural study, the research teams from Italy (using Alice robot) and the US (using Zeno RoboKind) laid the foundation for the use of realistic-looking robots, showing that children and adults with ASD did not show signs of fear, were highly interested in interacting with the robots and showed high levels of engagement [57]. ...
... In contrast to these rather simplistic designs, researchers in Italy and the US investigated the use of more realistic-looking robots, suspecting greater effectiveness regarding the transfer of the learnings from the robot-human interaction to human-human interaction [57]. In their cross-cultural study, the research teams from Italy (using Alice robot) and the US (using Zeno RoboKind) laid the foundation for the use of realistic-looking robots, showing that children and adults with ASD did not show signs of fear, were highly interested in interacting with the robots and showed high levels of engagement [57]. According to the authors, the benefit of realistic robots is that they can be used to simulate human-to-human social interactions with controlled repeatability, tireless repetition, and absence of frustration or other uncontrolled negative affect of the therapist towards the child [57]. ...
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.
... Some neurological researchers demonstrated that emotions have fundamental and effective role in human decision making, intend, goals, learning, motivations and almost in all aspects of the life (Leu et al,2014). In recent years, human computer interaction and collaborative robot-assisted systems are widely studying and applied in different applications such the robot language café (Wang et al,2017), humanlike guidance for driving navigation in urban environment (Lopes et al, 2017) and realistic humanlike robots for treatment (Hanson et al, 2012). Healthcare collaborative robotic agents are also entered to our daily life. ...
This paper presents a 3D graphical agent in role of a healthcare expert in order to analyze the general health status by examining the body mass index. The user and the agent are seated face to face and interaction starts with voice and vision parameters. For having more natural communication the 3D agent expresses feeling and emotions through eyes and face gestures. It gets the required data by voice to analyze for determining the category of general health condition. Besides, the agent gives basic suggestions regarding to the body mass index category and value. A user study carried out with a group of 15 adult women and men about their feeling and experience of communication with our agent which shows a high degree of
satisfaction in the use and the interaction way of assessing their body mass index.
... For expressing attention and emotions, this type of robot can be used. It should be noted that Alice is the premium brand of the Mina Robot, which was created by Hanson et al. [65]. Earlier, we reviewed a paper of Taheri et al. [66] that used the term Mina robot, which was originally the Alice robot, for a clinical intervention program in low and high-functioning Iranian children. ...
An array of developmental disorders known collectively as autism spectrum disorder (ASD) that can cause significant social interaction, communication, and behavioral challenges. To remedy this autistic impact at its very early stage, researchers are focusing their attention on robotics research with humanoid robots. It has been claimed that under certain circumstances, children with ASD (CwASD) interact more actively and spontaneously with robots than humans. Therefore, Human-Robot Interaction (HRI) has become a beneficial approach in training CwASD to abate their autism disabilities. This paper extensively reviews the prior findings on HRI for CwASD. With some critical analysis, a couple of findings have been highlighted to address the research gaps on the effectiveness of the intervention sessions administered by the robots. A total of nine traits of autism, intervention sessions administered by various tasks, different types of robots used, number of CwASD’s along with their age, diagnosis methods applied to quantify the autism levels, and, finally, the outcomes with identified research gaps have been analyzed. With some comparative depictions, the reader can visualize the broad scenario and overall research condition of robot-assisted training (RAT) for CwASD. This review recommends employing robots in supervised applications to enhance therapeutic effectiveness, broaden acquiescence, and inspire trust among practitioners, CwASD, and guardians. Overall, RAT is a promising use for intelligent social robots, especially for helping CwASD achieve therapeutic and educational goals. We expect interdisciplinary collaboration to address its difficulties quickly.
... Scassellati et al. [16] mentioned that social robots are less complex than human and animate social beings, and more interactive tools than inanimate toys; the former could be a source of distress or confusion, and the later cannot usually elicit novel social behaviors for children with ASD. In the past, researchers have reported utilizing computer avatars [18,40] and social robots with low or high facial complexities [16,[41][42][43][44] in teaching different facial expressions to children with ASD. As an exploratory study, Duquette et al. [41] involved two children with low-functioning autism in robot-assisted and two other children with ASD in humanmediated reciprocal interactions and imitative games. ...
Reciprocal interaction and facial expression are some of the most interesting topics in the fields of social and cognitive robotics. On the other hand, children with autism show a particular interest toward robots, and facial expression recognition can improve these children’s social interaction abilities in real life. In this research, a robotic platform has been developed for reciprocal interaction consisting of two main phases, namely as Non-structured and Structured interaction modes. In the Non-structured interaction mode, a vision system recognizes the facial expressions of the user through a fuzzy clustering method. The interaction decision-making unit is combined with a fuzzy finite state machine to improve the quality of human–robot interaction by utilizing the results obtained from the facial expression analysis. In the Structured interaction mode, a set of imitation scenarios with eight different posed facial behaviors were designed for the robot. As a pilot study, the effect and acceptability of our platform have been investigated on autistic children between 3 and 7 years old and the preliminary acceptance rate of \(\sim \) 78% is observed in our experimental conditions. The scenarios start with simple facial expressions and get more complicated as they continue. The same vision system and fuzzy clustering method of the Non-structured interaction mode are used for automatic evaluation of a participant’s gestures. Lastly, the automatic assessment of imitation quality was compared with the manual video coding results. The Pearson’s r on these equivalent grades were computed as \(\hbox {r}\,=\,0.89\) which shows a sufficient agreement on the automatic and manual scores.
... A lot of progress have been made in the field of AI applications and advances in machine learning had made possible the design of systems that are able to learn from available information to develop different kinds of systems 8 . Studies have reported on several interventions in the field of surgery and assistive therapies especially in geriatrics and with disabled patients 9 . Recent trends in AI and Human-Machine Interaction (HMI) is however beginning to move towards robotics, and the concept of Human-Robot Interaction (HRI) is becoming a common parlance in the field of AI. ...
Access to medical care is a global issue. Technology-aided approaches have been applied in addressing this. Interventions have however not focused on medical diagnosis as a fully automated procedure and available applications employ mainly text-based inputs rather than conversation in natural language. We explored the utility of ontology-based chatbot technology for the design of intelligent agents for medical diagnosis through a systematic review of the most recent related literature. English articles published in 2011-2016 returned 233 hits which yielded 11 relevant articles after a 3-stage screening. Findings showed that the creation of expert systems had been the focus of many the studies which utilize the physician-system-patient framework with system training based mostly on expert knowledge for designing web-or mobile phone-based applications that serve assistive purposes. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the physical examination part of diagnosis, connection to data sources on patients' vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Future works should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological options
Artificial intelligence is a field within computer science that attempts to simulate and build enhanced human intelligence into computers, mobiles, and various other machines. It can be termed as a powerful tool that has the capability to process huge sums of information with ease and assess patterns created over a period of time to give significant results or suggestions. It has garnered focus from almost every field from education to healthcare. Broadly, AI applications in healthcare include early detection and diagnosis, suggesting treatments, evaluating progress, medical history, and predicting outcomes. This chapter discussed AI, ASD, and what role AI currently plays in advancing autistic lives including detection, analysis, and treatment of ASD and how AI has been improving healthcare and the existing medical and technology aids available for autistic people. Current and future advancements are discussed and suggested in the direction of improving social abilities and reducing the communication and motor difficulties faced by people with ASD.
We outline the design and construction of novel robotic arms using machine perception, convolutional neural networks, and symbolic AI for logical control and affordance indexing. We describe our robotic arms built with a humanlike mechanical configuration and aesthetic, with 28 degrees of freedom, touch sensors, and series elastic actuators. The arms were modelled in Roodle and Gazebo with URDF models, as well as Unity, and implement motion control solutions for solving live games of Baccarat (the casino card game), rock paper scissors, handshaking, and drawing. This includes live interactions with people, incorporating both social control of the hands and facial gestures, and physical inverse kinematics (IK) for grasping and manipulation tasks. The resulting framework is an integral part of the Sophia 2020 alpha platform, which is being used with ongoing research in the authors work with team AHAM, an ANA Avatar Xprize effort towards human-AI hybrid telepresence. These results are available to test on the broadly released Hanson Robotics Sophia 2020 robot platform, for users to try and extend.
Respiratory Sinus Arrhythmia (RSA), heart rate, and accuracy and latency of emotion recognition were evaluated in children
with autism spectrum disorders (ASD) and typically developing children while viewing videos of faces slowly transitioning
from a neutral expression to one of six basic emotions (e.g., anger, disgust, fear, happiness, sadness, and surprise). Children
with ASD were slower in emotion recognition and selectively made more errors in detecting anger. ASD children had lower amplitude
RSA and faster heart rate. Within the ASD group, children with higher amplitude RSA recognized emotions faster. Less severe
ASD symptoms and increased gaze to the eye region in children with ASD were related to more accurate emotion recognition.
This article presents a longitudinal study with four children with autism, who were exposed to a humanoid robot over a period of several months. The longitudinal approach allowed the children time to explore the space of robot–human, as well as human–human interaction. Based on the video material documenting the interactions, a quantitative and qualitative analysis was conducted. The quantitative analysis showed an increase in duration of pre-defined behaviours towards the later trials. A qualitative analysis of the video data, observing the childrens activities in their interactional context, revealed further aspects of social interaction skills (imitation, turn-taking and role-switch) and communicative competence that the children showed. The results clearly demonstrate the need for, and benefits of, long-term studies in order to reveal the full potential of robots in the therapy and education of children with autism.
People with ASD (Autism Spectrum Disorders) have difficulty in managing interpersonal relationships and common life social situations. A modular platform for Human Robot Interaction and Human Machine Interaction studies has been developed to manage and analyze therapeutic sessions in which subjects are driven by a psychologist through simulated social scenarios. This innovative therapeutic approach uses a humanoid robot called FACE capable of expressing and conveying emotions and empathy. Using FACE as a social interlocutor the psychologist can emulate real life scenarios where the emotional state of the interlocutor is adaptively adjusted through a semi closed loop control algorithm which uses the ASD subject's inferred "affective" state as input. Preliminary results demonstrate that the platform is well accepted by ASDs and can be consequently used as novel therapy for social skills training.
The characteristics of Electro-actuated polymers (EAP) are typically considered inadequate for applications in robotics. But in recent years, there has been both dramatic increases in EAP technological capbilities and reductions in power requirements for actuating bio-inspired robotics. As the two trends continue to converge, one may anticipate that dramatic breakthroughs in biologically inspired robotic actuation will result due to the marraige of these technologies. This talk will provide a snapshot of how EAP actuator scientists and roboticists may work together on a common platform to accelerate the growth of both technologies. To demonstrate this concept of a platform to accelerate this convergence, the authors will discuss their work in the niche application of robotic facial expression. In particular, expressive robots appear to be within the range of EAP actuation, thanks to their low force requirements. Several robots will be shown that demonstrate realistic expressions with dramatically decreased force requirements. Also, detailed descriptions will be given of the engineering innovations that have enabled these robotics advancements-most notably, Structured-Porosity Elastomer Materials (SPEMs). SPEM manufacturing techniques create delicate cell-structures in a variety of elastomers that maintain the high elongation characteristics of the mother material, but because of the porisity, behave as sponge-materials, thus lower the force required to emulate facial expressions to levels output by several extant EAP actuators.
There is good evidence that computer-aided learning is well accepted by students with autism and is of great potential benefit to them. Despite the potential, however, the field remains relatively unexplored. This paper therefore proposes a framework for further research and development in the field of computer-aided learning for students with autism. The framework is based around the core deficiencies of autism, namely a social impairment, a communication impairment, rigidity and inflexibility in thinking and a theory of mind deficit. Proposals for computer-aided learning systems for each of these areas are put forward, and our current development work outlined.
We present here a progress report of our ongoing observation [2] of a group of preschoolers at three to four years of age interacting with an interactive robot. We placed the robot in their playroom and tele-controlled it from a remote room. The children showed not only individual actions, such as approach to, exploration of, and interaction with the robot, but also collective social actions, where the children spontaneously and actively situate the robot in their circle of, for example, playing house. We assume that the robot's simple appearance and comprehensive behavior derived (1) caretaking behavior from the children and (2) exchanging of such behavior among children, both of which would be important in the development of interpersonal communication and in pedagogical interventions to it.
Robots appear to be engaging to many children with autism, and evidence suggests that engagement can facilitate social interaction not only between child and robot but also between child and another human. To date, no objective evidence has established a link between short-term child-robot interactions and long-term child-human interactions. We report on a therapy model that uses a robot in no more than 20% of available therapy time, and describe how a humanoid robot can be used during that limited time to promote generalizable child-human interactions. Preliminary evidence indicates that such low-dose robotics can promote positive child-human interactions.
Simulation is a common feature in computer entertainment. However, in computer games simulation and story are often kept distinct by interleaving interactive play and cut scenes. We describe a technique for an interactive narrative system that more closely integrates simulation and storyline. The technique uses a combination of semi-autonomous character agents and high-level story direction. The storyline is decomposed into directives to character agents to achieve particular world states. Otherwise, character agents are allowed to behave autonomously. When the player's actions create inconsistency between the simulation state and storyline, the storyline is dynamically adapted and repaired to resolve any inconsistencies.