My research is focusing on developing smart robotic rehabilitation interfaces that use machine intelligence to adjust the level of difficulty, assess physical and mental obstacles on the part of the user, and provide analysis of the multi-sensing data collected in real time as the user exercises. The main goal of the interfaces is to engage the patient in repetitive exercise sessions and to provide better data visualization to the therapist for the patient's recovery progress. In this doctoral consortium, I will present three prototype user interfaces that can be applied in assistive environments and enhance the productivity and interaction among therapist and patient. The data processing and the decision making algorithms compose the core components of this study.
['Alexandros Lioulemes']
Adaptive User and Haptic Interfaces for Smart Assessment and Training