GRACE: Online Gesture Recognition for Autonomous Camera-Motion Enhancement in Robot-Assisted Surgery

Camera navigation in minimally invasive surgery changed significantly since the introduction of robotic assistance. Robotic surgeons are subjected to a cognitive workload increase due to the asynchronous control over tools and camera, which also leads to interruptions in the workflow. Camera motion automation has been addressed as a possible solution, but still lacks situation awareness. We propose an online surgical Gesture Recognition for Autonomous Camera-motion Enhancement (GRACE) system to introduce situation awareness in autonomous camera navigation. A recurrent neural network is used in combination with a tool tracking system to offer gesture-specific camera motion during a robotic-assisted suturing task. GRACE was integrated with a research version of the da Vinci surgical system and a user study (involving 10 participants) was performed to evaluate the benefits introduced by situation awareness in camera motion, both with respect to a state of the art autonomous system (S) and current clinical approach (P). Results show GRACE improving completion time by a median reduction of $\text{18.9} \text{s}$ (8.1%) with respect to S and $\text{65.1}\text{s}$ (21.1%) with respect to P. Also, workload reduction was confirmed by statistical difference in the NASA Task Load Index with respect to S ($p < \text{0.05}$). Reduction of motion sickness, a common issue related to continuous camera motion of autonomous systems, was assessed by a post-experiment survey ($p < \text{0.01}$).