A virtual trainer concept for robot-assisted human motor learning in rowing

Abstract

Keeping the attention level and observing multiple physiological and biomechanical variables at the same time at high precision is very challenging for human trainers. Concurrent augmented feedback, which is suggested to enhance motor learning in complex motor tasks, can also hardly be provided by a human trainer. Thus, in this paper, a concept for a virtual trainer is presented that may overcome the limits of a human trainer. The intended virtual trainer will be implemented in a CAVE providing auditory, visual and haptic cues. As a first application, the virtual trainer will be used in a realistic scenario for sweep rowing. To provide individual feedback to each rower, the virtual trainer quantifies errors and provides concurrent auditory, visual, and haptic feedback. The concurrent feedback will be adapted according to the actual performance, individual maximal rowing velocity, and the athlete’s individual perception.