Affect Recognition in Autism: a single case study on integrating a humanoid robot in a standard therapy.

Autism Spectrum Disorder (ASD) is a multifaceted developmental disorder that comprises a mixture of social impairments, with deficits in many areas including the theory of mind, imitation, and communication. Moreover, people with autism have difficulty in recognising and understanding emotional expressions. We are currently working on integrating a humanoid robot within the standard clinical treatment offered to children with ASD to support the therapists. In this article, using the A-B-A' single case design, we propose a robot-assisted affect recognition training and to present the results on the child’s progress during the five months of clinical experimentation. In the investigation, we tested the generalization of learning and the long-term maintenance of new skills via the NEPSY-II affection recognition sub-test. The results of this single case study suggest the feasibility and effectiveness of using a humanoid robot to assist with emotion recognition training in children with ASD

Adapting robot-assisted therapy of children with autism and different levels of intellectual disability

Autism Spectrum Disorder (ASD) is a complex developmental disorder that requires personalising the treatment to the personal condition, in particular for individuals with Intellectual Disability (ID), which are the majority of those with ASD. In this paper, we present a preliminary analysis of our on-going research on personalised care for children with ASD and ID. The investigation focuses on integrating a social robot within the standard treatment in which tasks and level of interaction are adapted to the ID level of the individual and follow his progress after the rehabilitation

Deep learning systems for estimating visual attention in robot-assisted therapy of children with autism and intellectual disability

Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This area of intervention poses many challenges, including the necessary flexibility and adaptability to real unconstrained therapeutic settings, which are different from the constrained lab settings where most of the technology is typically tested. Among the most common impairments of children with autism and intellectual disability is social attention, which includes difficulties in establishing the correct visual focus of attention. This article presents an investigation on the use of novel deep learning neural network architectures for automatically estimating if the child is focusing their visual attention on the robot during a therapy session, which is an indicator of their engagement. To study the application, the authors gathered data from a clinical experiment in an unconstrained setting, which provided low-resolution videos recorded by the robot camera during the child–robot interaction. Two deep learning approaches are implemented in several variants and compared with a standard algorithm for face detection to verify the feasibility of estimating the status of the child directly from the robot sensors without relying on bulky external settings, which can distress the child with autism. One of the proposed approaches demonstrated a very high accuracy and it can be used for off-line continuous assessment during the therapy or for autonomously adapting the intervention in future robots with better computational capabilities

Reading decoding and comprehension in children with autism spectrum disorders: Evidence from a language with regular orthography

Decoding and comprehension skills in children with autism spectrum disorders (ASD) were analysed in children native speakers of a language (Italian) with a highly regular orthography. Children with ASD were compared to children with matched intellectual functioning: a subgroup of children with ASD and borderline intellectual functioning (BIF) was compared to a subgroup of children with BIF but no signs of ASD; a subgroup of children with ASD and cognitive functioning within normal limits was compared to a group of typically developing children. Children with ASD (whether with or without BIF) showed essentially spared decoding skills in text as well as word and pseudo-word reading; this was at variance with children with BIF who, as a group, showed overall deficient decoding skills, despite considerable individual differences. By contrast, children with ASD (once again, irrespective of the presence of BIF) showed a selective impairment in reading comprehension, just like children with BIF but unlike the typically developing ones. Therefore, results are generally consistent with a profile of hyperlexia for children with ASD learning a regular orthography, as previously reported for other languages. Notably, this pattern was present irrespective of the degree of cognitive impairment, and clearly distinguished these children from those with borderline intellectual functioning but not signs of autism

Social robots to support practitioners in the education and clinical care of children: The CARER-AID project

The Controlled Autonomous Robot for Early detection and Rehabilitation of Autism and Intellectual Disability (CARER-AID) project aimed at verifying the effects of the introduction of a humanoid robot in the clinical routine as a supervised autonomous assistant to support clinical staff in the care of individuals with Autism Spectrum Disorder (ASD) associated with Intellectual Disability (ID). The CARER-AID project was undertaken by a multidisciplinary team composed of experts in artificial intelligence and robotics and clinical psychologists experienced in the treatment of ID. The literature shows that children with ASD seem to prefer robotic devices over non-robotic instruments and indeed humans. Starting from this, CARER-AID clinical studies provided experimental evidence that demonstrated several potential benefits of robot-assisted therapy when treating children with neurodevelopmental disorders, such as ASD with or without ID. Alongside the study in a clinical setting, the project also investigated the acceptability and the attitudes towards social robotics in an educational context. The study evaluated the teachers' perception of introducing a humanoid robot in a kindergarten and the attitudes of children with Typical Development (TD) towards. The results of the clinical and educational studies showed the usefulness of social robotics in supporting practitioners in their interventions with both TD and neurodevelopmental disorders. The CARER-AID project offers a unitary vision of a robot that can serve in different aspects and levels of the care, from the education to the therapeutic rehabilitation, from assessment to monitoring of results, providing assistance to caregivers and professionals at school and in clinical settings