LAPKaans: Tool-Motion Tracking and Gripping Force-Sensing Modular Smart Laparoscopic Training System

Abstract

Laparoscopic surgery demands highly skilled surgeons. Traditionally, a surgeon's knowledge is acquired by operating under a mentor-trainee method. In recent years, laparoscopic simulators have gained ground as tools in skill acquisition. Despite the wide range of laparoscopic simulators available, few provide objective feedback to the trainee. Those systems with quantitative feedback tend to be high-end solutions with limited availability due to cost. A modular smart trainer was developed, combining tool-tracking and force-using employing commercially available sensors. Additionally, a force training system based on polydimethylsiloxane (PDMS) phantoms for sample stiffness differentiation is presented. This prototype was tested with 39 subjects, between novices (13), intermediates (13), and experts (13), evaluating execution differences among groups in training exercises. The estimated cost is USD $200 (components only), not including laparoscopic instruments. The motion system was tested for noise reduction and position validation with a mean error of 0.94 mm. Grasping force approximation showed a correlation of 0.9975. Furthermore, differences in phantoms stiffness effectively reflected user manipulation. Subject groups showed significant differences in execution time, accumulated distance, and mean and maximum applied grasping force. Accurate information was obtained regarding motion and force. The developed force-sensing tool can easily be transferred to a clinical setting. Further work will consist on a validation of the simulator on a wider range of tasks and a larger sample of volunteers.

Keywords: additive manufacturing; force sensor; laparoscopic surgery; motion tracking; surgery simulator.

Figure A1

Protocol for validation in medical training context.

Figure 1

Explosion view of LAPKaans smart laparoscopic training system featuring 4 degrees of freedom (A1–A4). Tool-motion tracking (P1–P3), Gripping Force Sensing (F1–F3), and Force Training (C1–C3) modules mounted on the aluminum frame (T1) with the position and force sensors (S1–S3) are shown.

Figure 2

Graphical flowchart of the measurement computing. The tool-motion tracking module is depicted in blue, the gripping force sensing module in orange, the force training module in green, and the graphical user interface in yellow.

Figure 3

Gripping force sensing module measurement principle. F_T represents the real force at the tip. F_H represents the force applied at the handle adaptor.

Figure 4

Graphical User Interface Module. (a) Initial window for entry user’s identification. (b) Control and calibration window. When the task has begun, the control window shows both tools tracked distance and elapsed time. (c) Digital twin of both tools. (d) Live video from webcam. (e) Grasping force window with numeric and graphical indicators and phantom-to-handle force graph with colors and numbers corresponding to carousel position.

Figure 5

Designed tasks for the validation in the medical training context. (a) Peg transfer, (b) object transfer, (c) pea on a peg.

Figure 6

Developed system instrumented with force and motion-tracking sensors during the proposal validation with the use of common laparoscopic instruments.

Figure 7

Performance of the noise filter at different motion speeds: (a) fast motion rate and (b) slow-motion rate.

Figure 8

Phantom characterization curve. Each point represents the mean force value of handle force and force inside the phantom with standard deviation bars in both axes after 10 grasping cycles. Red, yellow, and green represent hard, medium, and soft phantoms, respectively.

Figure 9

Results of the training tasks (N = 39, bars represent mean ± standard deviation, asterisks represent statistical significance * p < 0.1 ** p < 0.05). (a) Total time needed to complete each task. (b) Average speed of both instruments (left and right hand) during the tasks. (c) Total distance covered by both instruments (left and right hand). (d) Average and maximum grasping force applied during the tasks.

Figure 10

The cost-benefit graph for our proposal compared with commercial and academic approaches.