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Autism spectrum disorder is a neurodevelopmental disorder that is characterized by patterns of behaviours and difficulties with social communication and interaction. Children on the spectrum exhibit atypical, restricted, repetitive, and challenging behaviours. In this study, we investigate the feasibility of integrating wearable sensors and machine learning techniques to detect the occurrence of challenging behaviours in real-time. A session of a child with autism interacting with different stimuli groups that included social robots was annotated with observed challenging behaviors. The child wore a wearable device that captured different motion and physiological signals. Different features and machine learning configurations were investigated to identify the most effective combination. Our results showed that physiological signals in addition to typical kinetic measures led to more accurate predictions. The best features and learning model combination achieved an accuracy of 97%. The findings of this work motivate research toward methods of early detection of challenging behaviours, which may enable the timely intervention by caregivers and possibly by social robots.

A social robot that meets the acceptability requirements of the target end-users presents a significant challenge to robot designers. The design process is often iterative and requires continuous improvements and optimization over time. One key aspect in designing an acceptable social robot is anthropomorphism. Social roboticists have developed assessment tools to evaluate different aspects for the perception of the observer. In this study, we evaluated the attitude of children toward four robots with different degrees of anthropomorphic traits. Questionnaires based on the Negative Attitude toward Robots Scale (NARS) and the Human-Robot Interaction Evaluation Scale (HRIES) were used to acquire the responses of 33 participants. To identify any changes due to interactions, a pre-test questionnaire was given prior to the interaction with a robot. It was then followed by a post-test questionnaire. Statistical tests were used to analyze the effects of gender, test (i.e., pre-test vs post-test), and the four robots, on the observers’ perception. Statistical differences were found between the four robots in the subscales of HRIES, namely, Sociability, Animacy, and Disturbance. The preferences of the children were leaning toward the humanoid robot (i.e., Alpha) with the moderate anthropomorphic traits in the Disturbance subscale. Low to moderate correlations were found between the subscales of NARS and HRIES.

We are developing a social robot that should autonomously interact long-term with pediatric oncology patients. The child and the robot need to get acquainted with one another before a long-term interaction can take place. We designed five interaction design patterns and two sets of robot behaviors to structure a getting acquainted interaction. We discuss the results of a user study (N = 75, 8–11 y.o.) evaluating these patterns and robot behaviors. Specifically, we are exploring whether the children successfully got acquainted with the robot and to what extent the children bonded with the robot. Results show that children effectively picked up how to talk to the robot. This is important, because the better the performance the more comfortable the children are, the more socially attractive the robot is, and the more intimate the conversation gets. The evaluation furthermore revealed that it is important for children, in order to get familiar with the robot, to have shared interests with the robot. Finally, most children did initiate a bond with the robot.

Improving the quality of life of people with dementia in long-term care facilities is very important and can be achieved by designing engaging activities for the residents. The introduction of social robots for people with dementia has already proven its benefits, and we expected that adding contextual cues to this interaction would enhance the positive engagement of these individuals. A total of five participants took part in a comparison study in which they engaged in a free-play session with the robot PLEO and in a free-play session with the robot PLEO within a jungle-themed context. The gaze and arm/hand behaviors of the participants were measured and were used to determine the level of their engagement. Contrary to our expectations, we found a significant decrease in engagement for the sessions where the context was added to the interaction. Our explanation of this result is that the added cues increased the threshold to interact with the robot, but the elderly were still engaged as spectators.

Social robots are emerging to become useful assistive tools for use in the therapy and education of children with Autism Spectrum Disorder (ASD). The nature of ASD causes its symptoms and manifestations to vary widely, resulting in a variety of robotic form factors that have been developed for this application. These robots vary in structure, shape, size, color, and function. In this paper, we propose a train-based model to be used as an educational and rehabilitation tool. We explore the responses from children with ASD in a special needs centre toward a variety of toys (e.g. ball, cymbals, truck) and robots (i.e. humanoid and a robotic seal), including trains. Experiments were conducted to verify whether or not trains have any advantage over other form factors and to extract any features of interest to the children. Results show that trains represent an appealing platform to a wider range of children with ASD. Additionally, results showed that simple features that can be easily incorporated into trains play a significant role in the interactions and could serve as reward mechanism.

Social robots have gained a lot of attention recently as they have been reported to be effective in supporting therapeutic services for children with autism. However, children with autism may exhibit a multitude of challenging behaviors that could be harmful to themselves and to others around them. Furthermore, social robots are meant to be companions and to elicit certain social behaviors. Hence, the presence of a social robot during the occurrence of challenging behaviors might increase any potential harm. In this paper, we identified harmful scenarios that might emanate between a child and a social robot due to the manifestation of challenging behaviors. We then quantified the harm levels based on severity indices for one of the challenging behaviors (i.e. throwing of objects). Our results showed that the overall harm levels based on the selected severity indices are relatively low compared to their respective thresholds. However, our investigation of harm due to throwing of a small social robot to the head revealed that it could potentially cause tissue injuries, subconcussive or even concussive events in extreme cases. The existence of such behaviors must be accounted for and considered when developing interactive social robots to be deployed for children with autism. The work is supported by a research grant from Qatar University under the grant No. QUST-1-CENG-2018-7 Scopus

Social robots are now being considered to be a part of the therapy of children with autism. During the interactions, some aggressive behaviors could lead to harmful scenarios. The ability of a social robot to detect such behaviors and react to intervene or to notify the therapist would improve the outcomes of therapy and prevent any potential harm toward another person or to the robot. In this study, we investigate the feasibility of an artificial neural network in classifying 6 interaction behaviors between a child and a small robotic toy. The behaviors were: hit, shake, throw, pickup, drop, and no interaction or idle. Due to the ease of acquiring data from adult participants, a model was developed based on adults' data and was evaluated with children's data. The developed model was able to achieve promising results based on the accuracy (i.e. 80%), classification report (i.e. overall F1-score=80%), and confusion matrix. The findings highlight the possibility of characterizing children's negative interactions with robotic toys to improve safety. - 2019 IEEE. The work is supported by a research grant from Qatar University under the grant No. QUST-1-CENG-2019-10. The statements made herein are solely the responsibility of the authors. Scopus

Earlier theorizing suggested that an (artificial) agent that combines persuasive strategies will be more persuasive. Therefore, the current research investigated whether a robot that uses two persuasive strategies is more persuasive than a robot that uses only one. Two crucial persuasive strategies that humans use in face-to-face persuasion are gazing and gestures, and therefore we studied the combined and individual contribution of these two persuasive strategies (gestures and gazing) on the persuasiveness of a storytelling robot. A robot told a classical persuasive story about the consequences of lying to forty-eight participants, and was programmed to use (persuasive) gestures (or not) and gazing (or not). Next, we asked participants to evaluate the character in the story thereby assessing the robot’s persuasiveness. Results presented evidence a robot’s persuasiveness is increased when gazing is used. When the robot used gestures, its persuasiveness only increased when it also used gazing. When the robot did not use gazing, using gestures diminished the robot’s persuasiveness. We discuss the implications for theory and design of robots that are more persuasive.

Young individuals with ASD may exhibit challenging behaviors. Among these, self-injurious behavior (SIB) is the most devastating for a person’s physical health and inclusion within the community. SIB refers to a class of behaviors that an individual inflicts upon himself or herself, which may potentially result in physical injury (e.g. hitting one’s own head with the hand or the wrist, banging one’s head on the wall, biting oneself and pulling out one’s own hair). We evaluate the feasibility of a wrist-wearable sensor in detecting challenging behaviors in a child with autism prior to any visible signs through the monitoring of the child’s heart rate, electrodermal activity, and movements. Furthermore, we evaluate the feasibility of such sensor to be used on an ankle instead of the wrist to reduce harm due to hitting oneself by hands and to improve wearable tolerance. Thus, we conducted two pilot tests. The first test involved a wearable sensor on the wrist of a child with autism. In a second test, we investigated wearable sensors on the wrist and on the ankle of a neurotypical child. Both pilot test results showed that the readings from the wearable sensors correlated with the children’s behaviors that were obtained from the videos taken during the tests. Wearable sensors could provide additional information that can be passed to social robots or to the caregivers for mitigating SIBs.

We explored the attitudes of the Heads of Education and the Directors of Research towards the need for Social Robotics Courses in Finland. The methods consisted of a cross-sectional survey (n = 21) and data was analyzed with descriptive methods and Pearson correlation tests. The results showed that the attitudes of respondents towards social robots were positive and they stated that robotics courses would be essential for universities. The respondents reported that the social service and healthcare sector will use social robots in the near future, but more training sessions are needed. So far, universities have offered only few applied robotics courses for healthcare students. This study also found that the surveyed universities have not yet taken into account the development of service and social robotics in the healthcare sector. - 2018, Springer Nature Switzerland AG.

How can we generate appropriate behavior for social artificial agents? A common approach is to (1) establish with controlled experiments which action is most appropriate in which setting, and (2) select actions based on this knowledge and an estimate of the setting. This approach faces challenges, as it can be very hard to acquire and reason with all the required knowledge. Estimating the setting is challenging too, as many relevant aspects of the setting (e.g. personality of the interactee) can be unobservable. We formally describe an alternative approach that can handle these challenges; responsiveness. This is the idea that a social agent can utilize the many feedback cues given in social interactions to continuously adapt its behavior to something more appropriate. We theoretically discuss the relative advantages and disadvantages of these two approaches, which allows for more explicitly considering their application in social agents.

In this paper, we discuss the prevalence of Autism Spectrum Disorder (ASD) in the Gulf region. We examine the importance of providing state-of-the-art ASD interventions, and highlight social robots as therapeutic tools that have gained popularity for their use in ASD therapy in the West. We also elaborate on the features of social robots that make them effective and describe how they can be used in such settings. We then emphasize the significance of taking cultural context into account in order to develop indigenous tools for ASD therapy, and explain the different ways in which social robots can be made culturally adaptive to maximize their potential impact on children with ASD. Springer International Publishing AG 2016. The work is supported by an NPRP grant from the Qatar National Research Fund under the grant No. NPRP 7-673-2-251. The statements made herein are solely the responsibility of the authors. Scopus

In this paper, we study a fascinating, widely reported link between autism and an anthropomorphic cartoon train, Thomas the Tank Engine. A significant portion of the character's fan base comes from children diagnosed with Autism Spectrum Disorder. Parents report that Thomas acts as a catalyst in the children's emotional and social development. Some have even reported improvements in speech and language as a result of the children's bond with the character. For this paper, we examine the available anecdotal evidence from parents and caregivers about the special relationship between their children and Thomas, and offer an explanation for this bond. Finally, we present the implications this has on the design of social robots for autism therapy and on the use of such robots as social story telling agents for therapeutic purposes.

Social Robotics is an emerging field, with many applications envisioned including in the special education system. This paper presents results of an exploratory study that interviewed six special education teachers on a) whether they can see Social Robots being used within their settings; b) what roles they could envision Social Robots to perform; and c) whether Social Robots could be bullied and conceivably become the bullies. Our exploratory study revealed a) a deep skepticism towards Social Robots being used in the special education setting; b) the roles envisioned for them were mostly mechanical, repetitive tasks with no communication needs; and c) that they were seen as a target for being bullied and becoming bullies with some believing them to be useful in educating about bullying.

In the past decade, we have witnessed an intensive progress of technology spurred on by community initiatives. The guest editors had its first meeting in 2010 to find that it was timely for the community to collectively document the recent advances in social robotics through a special issue in the International Journal of Social Robotics. This special issue presents various designs for social robots in order to make them more effective and natural for human-robot interactions. Social robots are autonomous robots that are able to interact and communicate among themselves, with humans, and with the environment and are designed to operate according to the established social and cultural norms. The main requirement for such robots is intelligence, which forms the basis of human-robot interaction. Many design requirements should be considered for social robots. Among these are the abilities to

Social Robotics is an emerging field, with many applications envisioned. Scientific and technological advancements constantly impact humans on the individual and societal level. Therefore one question increasingly debated is how to anticipate the impact of a given envisioned, emerging or new scientific or technological development and how to govern the emergence of scientific and technological advancements. Anticipatory governance has as a goal to discuss potential issues arising at the ground level of the emergence of a given scientific and technological product. Our study investigated a) the visibility of the anticipatory governance concept within the social robotic discourse and b) the implication of anticipatory governance for the social robotics field through the lens of a social robot design process and key documents from the UNESCO/ICSU 1999 World Conference on Sciences the lens. Our findings suggest that a) anticipatory governance is not a concept established within the social robotics fields so far; b) that social robotics as specific field is not engaged with within the anticipatory governance field and c) that many professional and academic fields are not yet involved in the social robotics discourse as aren’t many non-academic stakeholders. We posit that anticipatory governance can strengthen the social robotics field.

Robots might be helpful in light of the increasing number of children diagnosed with autism. Before we test the robots with children with autism, we first describe our first steps in investigating the effectiveness of robots as they interact with normally developing children. In this paper, we used a humanoid robot to tell a social story to introduce basic skills on social interaction. We developed a social story on "Greeting Others" and pilot-tested it on a normally developing 5-year old girl. We report a few of the qualitative findings that we found during the interaction of the robot and the child to provide fundamental design guidelines for future design of robots that interact with children and possibly for children with autism as well.

A growing field in Human-Robot Interaction aims at social assistance for users on specific tasks. These applications allow for insights regarding the acceptance of the robot's presence and interaction-related performance effects. We present a scenario in which a socially assistive robot assists users on a cognitive task. Furthermore, we quantitatively evaluate the effects of two distinct interaction strategies on performance of the user and acceptance of the robot's presence. In one strategy, the robot acts as a structuring guide and in the other, the robot tries to individually enhance the performance of the user. Results show that users benefit from a suited interaction strategy in terms of test performance and that the robot's presence is regarded as acceptable and also desirable.

This paper aimed to explore whether human beings can understand gestures produced by telepresence robots. If it were the case, they can derive meaning conveyed in telerobotic gestures when processing spatial information. We conducted two experiments over Skype in the present study. Participants were presented with a robotic interface that had arms, which were teleoperated by an experimenter. The robot could point to virtual locations that represented certain entities. In Experiment 1, the experimenter described spatial locations of fictitious objects sequentially in two conditions: speech condition (SO, verbal descriptions clearly indicated the spatial layout) and speech and gesture condition (SR, verbal descriptions were ambiguous but accompanied by robotic pointing gestures). Participants were then asked to recall the objects' spatial locations. We found that the number of spatial locations recalled in the SR condition was on par with that in the SO condition, suggesting that telerobotic pointing gestures compensated ambiguous speech during the process of spatial information. In Experiment 2, the experimenter described spatial locations non-sequentially in the SR and SO conditions. Surprisingly, the number of spatial locations recalled in the SR condition was even higher than that in the SO condition, suggesting that telerobotic pointing gestures were more powerful than speech in conveying spatial information when information was presented in an unpredictable order. The findings provide evidence that human beings are able to comprehend telerobotic gestures, and importantly, integrate these gestures with co-occurring speech. This work promotes engaging remote collaboration among humans through a robot intermediary., Comment: 27 pages, 7 figures

In this paper, we present the design of a social robot, Nancy, which is developed as a platform for engaging social interaction. Targeting for a social, safe, interactive and user-friendly robot mate, the design philosophy of Nancy is presented with mechanical, electrical, artificial skin and software specifications. In particular, there are 32 degrees of freedom (DOFs) through the whole body of Nancy, and the social intelligence is implemented based on vision, audio and control subsystems.

Synthetic skins with humanlike characteristics, such as a warm touch, may be able to ease the social stigma associated with the use of prosthetic hands by enabling the user to conceal its usage during social touching situations. Similarly for social robotics, artificial hands with a warm touch have the potential to provide touch that can give comfort and care for humans. With the aim of replicating the warmth of human skin, this paper describes (i) the experiments on obtaining the human skin temperature at the forearm, palm and finger, (ii) embedding and testing a flexible heating element on two types of synthetic skins and (iii) implementing a power control scheme using the pulse-width modulation to overcome the limitations of operating at different voltage levels and sources. Results show that the surface temperature of the human skin can be replicated on the synthetic skins.

Prosthetic hands and arms that have realistic appearance and can be controlled by the user’s thoughts are emerging. As these technologies mature, it is likely that there will be an increased interest in endowing these with skins having humanlike softness in order to address the psychosocial issues coupled with prosthesis usage in social interactions. The objective of the project described in this paper is to find an optimal design of a prosthetic hand’s skin that would be comparable to the softness of the human hand. The project aims to develop simulation and experimental methodologies for selecting skin materials, designing their internal geometries and comparing their behavior to the biomechanical skin behavior of the human hand for activities concerning social touch (e.g. handshakes, high fives, caress, etc.). In particular, this paper is focused on modifying the internal geometry of an artificial fingertip and evaluate whether significant changes in the skin compliance can be achieved. Simulation results show that modifying the internal geometry of the synthetic skin structure can lead to an increase in skin compliance.

The handshake has become the most acceptable gesture of greeting in many cultures. Replicating the softness of the human hand can contribute to the improvement of the emotional healing process of people who have lost their hands by enabling the concealment of prosthetic hand usage during handshake interactions. Likewise, sociable robots of the future will exchange greetings with humans. The soft humanlike hands during handshakes would be able to address the safety and acceptance issues of robotic hands. This paper investigates the areas of contact during handshake interactions. After the areas of high contact were known, indentation experiments were conducted to obtain the benchmark data for duplication with synthetic skins.