  A robotic system which approximates the user intention and appropriate
complimentary motion is critical for successful human-robot interaction. %While
the existing wearable sensors can monitor human movements in real-time,
prediction of human movement is a significant challenge due to its highly
non-linear motions optimised through the redundancy in the degrees of freedom.
Here, we demonstrate robustness of the Gaussian Process (GP) clustered with a
stochastic classification technique for trajectory prediction using an object
handover scenario. By parametrising real 6D hand movements during human-human
object handover using dual quaternions, variations of handover configurations
were classified in real-time and then the remaining hand trajectory was
predicted using the GP. The results highlights that our method can classify the
handover configuration at an average of $43.4\%$ of the trajectory and the
final hand configuration can be predicted within the normal variation of human
movement. In conclusion, we demonstrate that GPs combined with a stochastic
classification technique is a robust tool for proactively estimating human
motions for human-robot interaction.
