ENHANCE ROBOTIC-ASSISTED SURGERY WITH A SENSING-BASED ADAPTIVE SYSTEM

The advancement of robotic-assisted surgery (RAS) has revolutionized the field by enabling surgeons to perform intricate procedures with enhanced precision, improved depth perception, and more precise control. Despite these advancements, current RAS systems still rely on teleoperation, where surgeons control the robots remotely. The complexity of the master-slave control mechanism, along with the technical challenges involved, can impose significant mental workloads on surgeons. As excessive mental workload (MWL) can adversely affect performance and increase the likelihood of errors, addressing operator mental overload has become crucial for successful operation in RAS. To tackle this problem, there has been increased interest in developing robots that can provide operators with varying levels of assistance based on their MWL (i.e., adaptive system) during task execution. However, the research in this area is notably limited, primarily due to two key factors: the absence of a real-time MWL assessment framework and the lack of effective intervention strategies to mitigate MWL in RAS.

This Ph.D. dissertation aims to fill these gaps by designing the adaptive system in RAS and exploring its impact on surgical task performance. The dissertation comprises three studies. The first study demonstrated the feasibility of the adaptive system in RAS by introducing an MWL-triggered semi-autonomous suction tool as a proof-of-concept. Building upon the insights gained from the first study, the second study focused on enhancing the adaptive system's adaptability to more complex RAS tasks. In particular, the second study proposed a task-independent MWL model that had potential to be applied to various RAS tasks. Additionally, more intelligent interventions were investigated. Furthermore, the third study aimed to investigate the benefits of adaptive system in RAS training by introducing a personalized and adaptive training program based on human MWL profile. The findings of this dissertation revealed evidence supporting the effectiveness of the adaptive system in moderating subjects’ MWL, and its potential in enhancing task performance in RAS. This dissertation highlights the potential of incorporating adaptive systems into future RAS platforms, so that to provide valuable support and assistance to surgeons during critical moments and facilitate surgical training by identifying and addressing the specific needs of surgeons.