Novice and Expert Haptic Behaviours While Using a Robot Controlled Surgery System

Towards Expert-Based Speed–Precision Control in Early Simulator Training for Novice Surgeons

“…Data relative to the evolution of individual performance measures, relative to task speed and precision, were automatically monitored and recorded, using a specifically designed experimental simulator platform for image-based analysis of performance data relative to the time and precision of hand-tool movements in a five-step computer controlled pick-and-place task. The technical aspects of this platform, which was used in several experimental studies published elsewhere, are described in detail, with images and illustrations, in previous work [4][5][6][7][8].…”

“…Task completion time as a sole criterion has been explicitly demonstrated to be a poor or even misleading measure of surgical skill [1,23]. Some metrics assume a simple global optimum value, such as a minimal tool path length, or a minimal completion time, and other quantities such as forces [6,21] or velocities [23,[33][34][35]. The ideal values of these may vary in relation to changes in conditions, which may have to be considered.…”

“…How expert benchmark statistics may be exploited for automatic performance control is explained.will help the trainee gain proficiency at the task at hand, then operators will not be able to understand the message given by the system, and the latter will have failed its purpose altogether. Guidelines for user interface design and feedback procedures in simulator training contexts do not yet exist, but they can and should be worked out and tested [2,3].Relevant performance metrics [4][5][6][7][8][9][10] are essential to the development of surgical simulator systems for optimal independent training. The presentation of such metrics to the user, in a way that boosts independent learning by producing a measurable skill improvement, is the most important aspect of an effective training system [11].…”

“…Relevant performance metrics [4][5][6][7][8][9][10] are essential to the development of surgical simulator systems for optimal independent training. The presentation of such metrics to the user, in a way that boosts independent learning by producing a measurable skill improvement, is the most important aspect of an effective training system [11].…”

Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements

“…While the middle finger is the most important contributor to gross total grip force and, therefore, most important for getting a good grip of heavy objects to lift or carry, the ring finger and the small (pinky) finger are most important for the fine control of subtle grip force modulations [17], as those required for effectively manipulating the control handles of STRAS. Also, it is well-documented in the literature that grip force is systematically stronger in the dominant hand compared with the non-dominant hand [16,18]. In this study here, the grip force profiles correspond to measurements collected from specific sensor positions on these anatomically relevant parts of finger and hand regions of the dominant and non-dominant hands.…”