Simple and Robust Automatic Detection and Recognition of Human Movement Patterns in Tasks of Different Complexity
Lisa Gutzeit, Marc Otto, Elsa Andrea Kirchner
Editors: Andreas Holzinger, Alan Pope, Hugo Plácido Silva
In Physiological Computing Systems, Springer, pages o.A., 2019.

Abstract :

In many different research areas it is important to understand human behavior, e.g., in robotic learning or human-computer interaction. To learn new robotic behavior from human demonstrations, human move- ments need to be recognized to select which sequences should be trans- ferred to a robotic system and which are already available to the system and therefore do not need to be learned. In interaction tasks, the current state of a human can be used by the system to react to the human in an ap- propriate way. Thus, the behavior of the human needs to be analyzed. To apply the identification and recognition of human behavior in dfferent ap- plications, it is of high interest that the used methods work autonomously with minimum user interference. This paper focuses on the analysis of hu- man manipulation behavior in tasks of different complexity while keeping manual efforts low. By identifying characteristic movement patterns in the movement, human behaviors are decomposed into elementary build- ing blocks using a fully automatic segmentation algorithm. With a simple k-Nearest Neighbor classification these identified movement sequences are assigned to known movement classes. To evaluate the presented approach, pick-and-place, ball-throwing, and lever-pulling movements were recorded with a motion tracking system. It is shown that the proposed method outperforms the widely used Hidden Markov Model-based classification. Especially in case of a small number of labeled training examples, which considerably minimizes manual efforts, our approach still has a high ac- curacy. For simple lever-pulling movements already one training example per class succed to achieve a classification accuracy of above 95%.



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