  Human movement variability arises from the process of mastering redundant
(bio)mechanical degrees of freedom to successfully accomplish any given motor
task where flexibility and stability of many possible joint combinations helps
to adapt to environment conditions. While the analysis of movement of
variability is becoming increasingly popular as a diagnostic tool or skill
performance evaluation, there are remain challenges on applying the most
appropriate methods. We therefore investigate nonlinear methods such as
reconstructed state space (RSSs), uniform time-delay embedding, recurrence
plots (RPs) and recurrence quantification analysis (RQAs) with real-world
time-series data of wearable inertial sensors. That said, twenty healthy
participants imitated vertical and horizontal arm movements in normal and
faster velocity from an humanoid robot. We applied nonlinear methods to the
collected data to found visual differences in the patterns of RSSs and RPs and
statistical differences with RQAs. We conclude that Shannon Entropy with RQA is
a robust method that helps to quantify activities, types of sensors, windows
lengths and level of smoothness. Hence this work might enhance the development
of better diagnostic tools for applications in rehabilitation and sport science
for skill performance or new forms of human-humanoid interaction for
quantification of movement adaptations and motor pathologies.
