Our study aimed at comparing, over a period of 3 years, the effectiveness of three different educational approaches addressed to children with autism and severe mental retardation. The first one was a treatment and education of autistic and related communication handicapped children (TEACCH) program implemented in a residential center; the second was a TEACCH program implemented at home and at mainstream schools, after a specific parent psychoeducational training; the third approach referred to inclusive education in mainstream schools, in which a nonspecific approach was implemented. Each subject was assessed twice, using the Psycho-Educational Profile-Revised (PEP-R) and Vineland Adaptive Behavior Scale (VABS)-survey form. Effectiveness of TEACCH appeared to be confirmed, showing positive outcomes in the natural setting, and revealing its inclusive value.

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.

This report, based on four studies with children with low-functioning autism, aimed at evaluating the effects of repetitive transcranial magnetic stimulation delivered on the left and right premotor cortices on eye-hand integration tasks; defining the long-lasting effects of high-frequency repetitive transcranial magnetic stimulation; and investigating the real efficacy of high-frequency repetitive transcranial magnetic stimulation by comparing three kinds of treatments (high-frequency repetitive transcranial magnetic stimulation, a traditional eye-hand integration training, and both treatments combined). Results showed a significant increase in eye-hand performances only when high-frequency repetitive transcranial magnetic stimulation was delivered on the left premotor cortex; a persistent improvement up to 1 h after the end of the stimulation; better outcomes in the treatment combining high-frequency repetitive transcranial magnetic stimulation and eye-hand integration training. Based on these preliminary findings, further evaluations on the usefulness of high-frequency repetitive transcranial magnetic stimulation in rehabilitation of children with autism are strongly recommended.

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