NAOTherapist is a cognitive robotic architecture whose main goal is to develop non-contact upper-limb rehabilitation sessions autonomously with a social robot for patients with physical impairments. In order to achieve a fluent interaction and an active engagement with the patients, the system should be able to adapt by itself in accordance with the perceived environment. In this paper, we describe the interaction mechanisms that are necessary to supervise and help the patient to carry out the prescribed exercises correctly. We also provide an evaluation focused on the child-robot interaction of the robotic platform with a large number of schoolchildren and the experience of a first contact with three pediatric rehabilitation patients. The results presented are obtained through questionnaires, video analysis and system logs, and have proven to be consistent with the hypotheses proposed in this work.

In motor rehabilitation therapies, especially with pediatric patients, there is often a loss of interest and motivation due to the lack of variety in exercises and therapeutic activities. NAOTherapist (NT) is a socially assistive robotics (SAR) platform designed to support hands-off rehabilitation sessions through two interactive games: mirror and Simon. It integrates a cognitive architecture with an NAO humanoid robot that acts as a coach, as well as a red-green-blue-depth (RGB-D) sensor that eases body capture. This work aims to deploy and evaluate the developed technology for a long-term experience.

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient’s condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist—a robotic architecture for rehabilitation with social robots—has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metric.

As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.