  Human motion plays an important role in many fields. Large databases exist
that store and make available recordings of human motions. However, annotating
each motion with multiple labels is a cumbersome and error-prone process. This
bachelor's thesis presents different approaches to solve the multi-label
classification problem using Hidden Markov Models (HMMs). First, different
features that can be directly obtained from the raw data are introduced. Next,
additional features are derived to improve classification performance. These
features are then used to perform the multi-label classification using two
different approaches. The first approach simply transforms the multi-label
problem into a multi-class problem. The second, novel approach solves the same
problem without the need to construct a transformation by predicting the labels
directly from the likelihood scores. The second approach scales linearly with
the number of labels whereas the first approach is subject to combinatorial
explosion. All aspects of the classification process are evaluated on a data
set that consists of 454 motions. System 1 achieves an accuracy of 98.02% and
system 2 an accuracy of 93.39% on the test set.
