  Despite having the robotic catheter systems which have recently emerged as
safe way of performing cardiovascular interventions, a number of important
challenges are yet to be investigated. One of them is exploration of surgeons'
natural skills during vascular catheterization with robotic systems. In this
study, surgeons' natural hand motions were investigated for identification of
four basic movements used for intravascular catheterization. Controlled
experiment was setup to acquire surface electromyography (sEMG) signals from
six muscles that are innervated when a subject with catheterization skills made
the four movements in open settings. k-means and k-NN models were implemented
over average EMG and root means square features to uniquely identify the
movements. The result shows great potentials of sEMG analysis towards designing
intelligent cyborg control for safe and efficient robotic catheterization.
