Robots are more and more present in our lives, particularly in the health sector. In therapeutic centers, some therapists are beginning to explore various tools like video games, Internet exchanges, and robot-assisted therapy. These tools will be at the disposal of these professionals as additional resources that can support them to assist their patients intuitively and remotely. The humanoid robot can capture young children’s attention and then attract the attention of researchers. It can be considered as a play partner and can directly interact with children or without a third party’s presence. It can equally perform repetitive tasks that humans cannot achieve in the same way. Moreover, humanoid robots can assist a therapist by allowing him to teleoperated and interact from a distance. In this context, our research focuses on robot-assisted therapy and introduces a humanoid social robot in a pediatric hospital care unit. That will be performed by analyzing many aspects of the child’s behavior, such as verbal interactions, gestures and facial expressions, etc. Consequently, the robot can reproduce consistent experiences and actions for children with communication capacity restrictions. This work is done by applying a novel approach based on deep learning and reinforcement learning algorithms supported by an ontological knowledge base that contains relevant information and knowledge about patients, screening tests, and therapies. In this study, we realized a humanoid robot that will assist a therapist by equipping the robot NAO: 1) to detect whether a child is autistic or not using a convolutional neural network, 2) to recommend a set of therapies based on a selection algorithm using a correspondence matrix between screening test and therapies, and 2) to assist and monitor autistic children by executing tasks that require those therapies.

Dysgraphia is a disorder of expression with the writing of letters, words, and numbers. Dysgraphia is one of the learning disabilities attributed to the educational sector, which has a strong impact on the academic, motor, and emotional aspects of the individual. The purpose of this study is to identify dysgraphia in children by creating an engaging robot-mediated activity, to collect a new dataset of Latin digits written exclusively by children aged 6 to 12 years. An interactive scenario that explains and demonstrates the steps involved in handwriting digits is created using the verbal and non-verbal behaviors of the social humanoid robot Nao. Therefore, we have collected a dataset that contains 11,347 characters written by 174 participants with and without dysgraphia. And through the advent of deep learning technologies and their success in various fields, we have developed an approach based on these methods. The proposed approach was tested on the generated database. We performed a classification with a convolutional neural network (CNN) to identify dysgraphia in children. The results show that the performance of our model is promising, reaching an accuracy of 91%.

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