Publication
Simple and Robust Automatic Detection and Recognition of Human Movement Patterns in Tasks of Different Complexity
Lisa Gutzeit; Marc Otto; Elsa Andrea Kirchner
In: Andreas Holzinger; Alan Pope; Hugo Plácido Silva (Hrsg.). Physiological Computing Systems. Pages 39-57, Springer, 7/2019.
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%.