  This paper presents a method of reconstructing full-body locomotion sequences
for virtual characters in real-time, using data from a single inertial
measurement unit (IMU). This process can be characterized by its difficulty
because of the need to reconstruct a high number of degrees of freedom (DOFs)
from a very low number of DOFs. To solve such a complex problem, the presented
method is divided into several steps. The user's full-body locomotion and the
IMU's data are recorded simultaneously. Then, the data is preprocessed in such
a way that would be handled more efficiently. By developing a hierarchical
multivariate hidden Markov model with reactive interpolation functionality the
system learns the structure of the motion sequences. Specifically, the phases
of the locomotion sequence are assigned in the higher hierarchical level, and
the frame structure of the motion sequences are assigned at the lower
hierarchical level. During the runtime of the method, the forward algorithm is
used for reconstructing the full-body motion of a virtual character. Firstly,
the method predicts the phase where the input motion belongs (higher
hierarchical level). Secondly, the method predicts the closest trajectories and
their progression and interpolates the most probable of them to reconstruct the
virtual character's full-body motion (lower hierarchical level). Evaluating the
proposed method shows that it works on reasonable framerates and minimizes the
reconstruction errors compared with previous approaches.
