Online Performance Prediction and Profiling of Human Activities by Observation

The capacity of a system to automatically analyze and predict the performance of a human in a particular task can
provide important information in Human-Robot Interaction. Despite its usefulness, the above topic has received rather limited attention in the literature. In the current work, we introduce a method for performance prediction and profiling of human activities. Using little information about a task, our method is able to extract the characteristic motion patterns of an agent, analyze them and predict his/her performance in a given activity. We demonstrate the robustness of the method in several different activities, that involve both periodic and oscillatory primitive motions. In addition, we evaluate it thoroughly on data obtained from public datasets and discuss
its usefulness for contemporary robotic applications.