Autonomous socially assistive robotics in pediatric clinical practice

Abstract

The development of new devices to support neurological recovery is a current challenge for clinical professionals and engineers [Tapus et al. 2007b]. Particularly, in the last decade, robotic applications have demonstrated their great potential as novel approaches [Dru_zbicki et al. 2013]. Socially Assistive Robotics refers to those robots that provide assistance to hu- man beings through social interaction. This technology is particularly interesting in health- care domains since it is able to elicit more favorable responses to the treatment [Okamura et al. 2010]. All these approaches start from the same hypothesis: the interaction provided by a social robot helps patients to get engaged with the treatment, in addition to automatic data gathering and reporting, helping to relieve the workload of healthcare professionals while reducing the socio-economic costs. Under this context, this thesis arises from four foundations: neurorehabilitation, socially assistive robotics, gamification and artificial intelligence. The integration of these fundamentals aims to design a child-robot interaction framework to enhance the pediatric clinical practice. The designed framework is provided with an intelligent system, so that no engineer is required either to control the interaction or to adapt the system. During the development of this thesis the framework has been used and evaluated in two di erent tasks: pediatric rehabilitation (NAOTherapist) and motion encouragement. Being the first one the central application of the presented work. In NAOTherapist, child-robot sessions are composed of playful immersive activities based on reward and positive reinforcement to improve motivation and, therefore, adherence to treatments. The ultimate goal is to demonstrate the feasibility of this framework in real healthcare settings, so a user-centered prototyping is proposed by involving the user during each development phase. A prototype was initially evaluated with more than 120 of typically developing children, obtaining a generalized high degree of active engagement [Pulido et al. 2017]. After that, three evaluation scenarios ex- posed the platform to the real practice: a first contact to get closer to the target individual, a long-term experience to determine personalization needs [Pulido et al. 2019], and an intensive intervention to evaluate the motivation and adherence to treatment. About 20 pediatric patients participated in the studies with very promising results. In all cases, the sessions with the robot provided a greater motivation compared to the conventional treatment, getting pa-ients to exceed the objectives marked by the experts. Positive reinforcement and rewarding the patient were fundamental aspects to maintain motivation. The robot autonomy was also a key point, so making the robot taking its own decisions improved the perception of social entity. The interviewed relatives detected functional and self-esteem enhancements in their children, and experts confirmed the system utility and usability for application in pediatrics.