  In medical tasks such as human motion analysis, computer-aided auxiliary
systems have become preferred choice for human experts for its high efficiency.
However, conventional approaches are typically based on user-defined features
such as movement onset times, peak velocities, motion vectors or frequency
domain analyses. Such approaches entail careful data post-processing or
specific domain knowledge to achieve a meaningful feature extraction. Besides,
they are prone to noise and the manual-defined features could hardly be re-used
for other analyses. In this paper, we proposed probabilistic movement
primitives (ProMPs), a widely-used approach in robot skill learning, to model
human motions. The benefit of ProMPs is that the features are directly learned
from the data and ProMPs can capture important features describing the
trajectory shape, which can easily be extended to other tasks. Distinct from
previous research, where classification tasks are mostly investigated, we
applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to
quantify the effect of different transcranial current stimulation methods on
human motions. We presented an initial result with 10 participants. The results
validate ProMPs as a robust and effective feature extractor for human motions.
