Personalized Recommendation of PoIs to People with Autism

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model

Robot Assistive Therapy Strategies for Children with Autism

Background: Autism spectrum disorder (ASD) is a category of neurodevelopmental disorder characterized by persistent deficits in social communication and social interaction across multiple contexts as well as restricted, repetitive patterns of behaviour, interests, or activities. Social robots offer clinicians new ways to interact and work with people with ASD. Robot-Assisted Training (RAT) is a growing body of research in HRI, which studies how robots can assist and enhance human skills during a task-centred interaction. RAT systems have a wide range of application for children with ASD. Aims: In a pilot RCT with an experimental group and a control group, research aims will be: to assess group differences in repetitive and maladaptive behaviours (RMBs), affective states and performance tasks across sessions and within each group; to assess the perception of family relationships between two groups before and post robot interaction; to develop a robotic app capable to run Raven’s Progressive Matrices (RPM), a test typically used to measure general human intelligence and to compare the accuracy of the robot to capture the data with that run by psychologists. Material and Methods: Patients with mild or moderate level of ASD will be enrolled in the study which will last 3 years. The sample size is: 60 patients (30 patients will be located in the experimental group and 30 patients will be located in the control group) indicated by an evaluation of the estimated enrolment time. Inclusion criteria will be the following: eligibility of children confirmed using the Autism Diagnostic Observation Schedule −2; age ≥ 7 years; clinician judgment during a clinical psychology evaluation; written parental consent approved by the local ethical committee. The study will be conducted over 10 weeks for each participant, with the pretest and post test conducted during the first and last weeks of the study. The training will be provided over the intermediate eight weeks, with one session provided each week, for a total of 8 sessions. Baseline and follow-up evaluation include: socioeconomic status of families will be assessed using the Hollingshead scale; Social Communication Questionnaire (SCQ) will be used to screen the communication skills and social functioning in children with ASD; Vineland Adaptive Behavior Scale, 2nd edition (VABS) will be used to assess the capabilities of children in dealing with everyday life; severity and variety of children’s ripetitive behaviours will be also assessed using Repetitive Behavior Scale-Revised (RBS-R). Moreover, the perception of family relationships assessment will be run by Portfolio for the validation of parental acceptance and refusal (PARENTS). Expected Results: 1) improbe communication skills; 2) reduced repetitive and maladaptive behaviors; 3) more positive perception of family relationships; 4) improved performance. Conclusions: Robot-Assisted Training aims to train and enhance user (physical or cognitive) skills, through the interaction, and not assist users to complete a task thus a target is to enhance user performance by providing personalized and targeted assistance towards maximizing training and learning effects. Robotics systems can be used to manage therapy sessions, gather and analyse data and like interactions with the patient and generate useful information in the form of reports and graphs, thus are a powerful tool for the therapist to check patient’s progress and facilitate diagnosis