Socially-Assistive Robotics (SAR) has been extensively used for a variety of applications, including educational assistants, exercise coaches and training task instructors. The main goal of such systems is to provide a personalized and tailored session that matches user abilities and needs. While objective measures (e.g., task performance) can be used to adjust task parameters (e.g., task difficulty), towards personalization, it is essential that such systems also monitor task engagement to personalize their training strategies and maximize the effects of the training session. We propose an Interactive Reinforcement Learning (IRL) framework that combines explicit feedback (task performance) with implicit human-generated feedback (task engagement) to achieve efficient personalization. We illustrate the framework with a cognitive training task, describing our data-driven methodology (data collection and analysis, user simulation) towards designing our proposed real-time system. Our data analysis and the reinforcement learning experiments on real user data indicate that the integration of task engagement as human-generated feedback in the RL mechanism can facilitate robot personalization, towards a real-time personalized robot-assisted training system.

Multiple sclerosis (MS) is a disease that affects the central nervous system, which consists of the brain and spinal cord. Although this condition cannot be cured, proper treatment of persons with MS (PwMS) can help control and manage the relapses of several symptoms. In this survey article, we focus on the different technologies used for the assessment and rehabilitation of motor impairments for PwMS. We discuss sensor-based and robot-based solutions for monitoring, assessment and rehabilitation. Among MS symptoms, fatigue is one of the most disabling features, since PwMS may need to put significantly more intense effort toward achieving simple everyday tasks. While fatigue is a common symptom across several neurological chronic diseases, it remains poorly understood for various reasons, including subjectivity and variability among individuals. To this end, we also investigate recent methods for fatigue detection and monitoring. The result of this survey will provide both clinicians and researchers with valuable information on assessment and rehabilitation technologies for PwMS, as well as providing insights regarding fatigue and its effect on performance in daily activities for PwMS.