Exploration of Surgeons' Natural Skills for Robotic Catheterization
This work addresses the challenge of designing intelligent control systems for safe and efficient robotic catheterization in cardiovascular interventions, though it is incremental as it builds on existing robotic systems and analysis methods.
The study tackled the problem of identifying surgeons' natural hand motions for robotic catheterization by analyzing surface electromyography (sEMG) signals from six muscles during four basic movements, using k-means and k-NN models to uniquely identify these movements with promising results.
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.