In this paper, we present an interactive learning and adaptation framework. The framework combines Interactive Reinforcement Learning methods to effectively adapt and refine a learned policy to cope with new users. We argue that implicit feedback provided by the primary user and guidance from a secondary user can be integrated to the adaptation mechanism, resulting at a tailored and safe interaction. We illustrate this framework with a use case in Robot Assisted Therapy, presenting a Robot Yoga Trainer that monitors a yoga training session, adjusting the session parameters based on human motion activity recognition and evaluation through depth data, to assist the user complete the session, following a Reinforcement Learning approach. CCS Concepts •Computing methodologies → Reinforcement learning; Learning from implicit feedback; Activity recognition and understanding;
Serious games usually refer to virtual games used for training, simulation, or education and can engage users in cognitive and physical tasks. The design of serious games may offer insights on users' cognitive and physical behaviors while they try to accomplish structured tasks. In this study, we concentrate on the design principles of serious games that can be used for assessment, which we employed for our own design. To test our prototype, we conducted an experiment with control participants. Results from surveys, our collected game features, and sensor outputs were compared and analyzed with hypotheses based on previous research studies. Finally, we interpret the results of our experiment, and we describe issues and real life uncertainties that associate with sensor errors.
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