<p>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.</p> <p>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.</p>

<p dir="ltr">Robotic surgery has transformed the landscape of minimally invasive procedures, offering unmatched precision and quicker patient recovery times. Despite all these advancements, training surgeons to use these sophisticated surgical systems effectively remains a daunting challenge, primarily due to high costs, limited accessibility, increased learning curve, and inconsistent training quality. Existing training modalities are limited by the high costs of original training robots, logistical challenges, lack of emphasis on hand movements, the necessity of expert presence, and limited scalability and effectiveness. This thesis introduces TrainVR, a low-cost based training system designed to overcome these hurdles and enhance the skillset of surgical trainees. TrainVR integrates affordable Virtual Reality (VR) technology with enhanced fidelity, creating an engaging and realistic training environment. TrainVR is designed to simulate realistic surgical environments and procedures, focusing on the development of motor, cognitive, and spatial skills for tasks required for robotic surgery through computer vision algorithms, gamified environments, performance analytics, and supporting both asynchronous and remote expert-led training scenarios. This system features customizable training modules, enabling trainees to practice a wide array of surgical procedures in a safe, virtual setting. The device also focuses on the importance of user’s hand, clutch, and ergonomics during surgical training which is crucial based on feedback from surgeons. The development of TrainVR involved crafting detailed 3D models of surgical instruments and anatomical structures, by integrating hardware, software and designing a user-friendly interface. We conducted testing with different game environments which compare the performance of the users and provide insights to improve the learning. The thesis concludes by experimenting and proposing new configurations to improve the fidelity and hand tracking which should closely match with the experience provided by the present training simulators at a substantially lower cost. TrainVR’s scalable design and compatibility with standard VR hardware make it accessible to a wide range of institutions, including those with limited resources. By offering a cost-effective, immersive, and adaptive training solution, TrainVR aims to enhance surgical education and ultimately improve patient care outcomes.</p>