Background: Training surgeons to use surgical robots are becoming part of surgical training curricula. We propose a novel method of training fine-motor skills such as Microscopic Selection Task (MST) for robot-assisted surgery using virtual reality (VR) with objective quantification of performance. We also introduce vibrotactile feedback (VTFB) to study its impact on training performance. Methods: We use a VR-based environment to perform MST with varying degrees of difficulties. Using a well-known human-computer interaction paradigm and incorporating VTFB, we quantify the performance: speed, precision and accuracy. Results: MST with VTFB showed statistically significant improvement in performance metrics leading to faster completion of MST with higher precision and accuracy compared to that without VTFB. Discussion: The addition of VTFB to VR-based training for robot-assisted surgeries may improve performance outcomes in real robotic surgery. VTFB, along with proposed performance metrics, can be used in training curricula for robot-assisted surgeries. K E Y W O R D S human-computer interaction, psychomotor, surgical robot, true positive rate, vibrotactile feedback 1 | INTRODUCTION Surgical robots are increasingly being used in almost every multispecialty hospital. Currently, they have multiple applications in pelvic, abdominal, select-chest and neurosurgical procedures. 1-6 The technology of surgical robots today has advanced sufficiently to provide a high degree of freedom in the robots' arm movements, control and stereoscopic vision. The improved user-interface of these technologies has led to better on-the-job performance and faster learning curves in the use of surgical robots such as da Vinci®, 7 ROSA® 8,9 and Senhance™. 10 Psychomotor skills play a vital role in surgical task performance with increased speed, accuracy, sensitivity and precision. Surgical robotic systems typically seek to help surgeons perform surgeries with higher accuracy, faster responses to intraoperative complications and increased dexterity. Quantification of psychomotor skills became essential with robotic surgical training systems that emphasize rare complex surgical scenarios, challenges and evaluations of these skills. Training and evaluation of these skills are crucial for effective analysis of the surgeons in the tasks as mentioned earlier. The evaluation of the performance is currently through cognitive, technical and non-technical skills. Technical skills (TS)
Understanding the response of Pacinian Corpuscle (PC) for an electrical stimulus through a computational model can give better insight into the physiology. Although there are simpler models available in the literature, models simulating spike-rate and threshold characterizations are still missing. These characterizations may lead to the development of tactile displays combining both electrical and mechanical stimuli, especially high-frequency vibrations. We developed a PC model with equivalent circuits of the electrode-skin interface, PC's neurite, and the first Ranvier node. The input electrical stimulus is a current pulse with varying amplitude (0 to 2 mA) and varying frequency (5 Hz to 1600 Hz). The model is characterized initially for the frequency response, and then the spike-rate and threshold characteristics were simulated. The spike-rate traces for electrical stimuli show the phase-locking phenomenon similar to the mechanical stimuli responses of PC, however the plateau lengths are larger for the spike-rate traces with electrical stimuli compared to that of the mechanical stimuli. This is reflected as a large difference in the threshold characteristics for one and two impulsesper-cycle. Moreover, threshold characteristics are little influenced by the neural noise. This model can be extended to study the combination of electrical and mechanical stimuli.
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