Haptic Feedback, Force Feedback, and Force-Sensing in Simulation Training for Laparoscopy: A Systematic Overview

OBJECTIVES

To provide a systematic overview of the literature assessing the value of haptic and force feedback in current simulators teaching laparoscopic surgical skills.

DATA SOURCES

The databases of Pubmed, Cochrane, Embase, Web of Science, and Google Scholar were searched to retrieve relevant studies published until January 31st, 2017. The search included laparoscopic surgery, simulation, and haptic or force feedback and all relevant synonyms.

METHODS

Duplicates were removed, and titles and abstracts screened. The remaining articles were subsequently screened full text and included in this review if they followed the inclusion criteria. A total of 2 types of feedback have been analyzed and will be discussed separately: haptic- and force feedback.

RESULTS

A total of 4023 articles were found, of which 87 could be used in this review. A descriptive analysis of the data is provided. Results of the added value of haptic interface devices in virtual reality are variable. Haptic feedback is most important for more complex tasks. The interface devices do not require the highest level of fidelity. Haptic feedback leads to a shorter learning curve with a steadier upward trend. Concerning force feedback, force parameters are measured through force sensing systems in the instrument and/or the environment. These parameters, especially in combination with motion parameters, provide box trainers with an objective evaluation of laparoscopic skills. Feedback of force-use both real time and postpractice has been shown to improve training.

CONCLUSIONS

Haptic feedback is added to virtual reality simulators to increase the fidelity and thereby improve training effect. Variable results have been found from adding haptic feedback. It is most important for more complex tasks, but results in only minor improvements for novice surgeons. Force parameters and force feedback in box trainers have been shown to improve training results.

Development and application of a multi-modal task analysis to support intelligent tutoring of complex skills

BACKGROUND

Contemporary work in the design and development of intelligent training systems employs task analysis (TA) methods for gathering knowledge that is subsequently encoded into task models. These task models form the basis of intelligent interpretation of student performance within education and training systems. Also referred to as expert models, they represent the optimal way(s) of performing a training task. Within Intelligent Tutoring Systems (ITSs), real-time comparison of trainee task performance against the task model drives automated assessment and interactive support (such as immediate feedback) functionality. However, previous task analysis (TA) methods, including various forms of cognitive task analysis (CTA), may not be sufficient to support identification of the detailed design specifications required for the development of an ITS for a complex training task incorporating multiple underlying skill components, as well as multi-modal information presentation, assessment, and feedback modalities. Our current work seeks to develop an ITS for training Robotic Assisted Laparoscopic Surgery (RALS), a complex task domain that requires a coordinated utilization of integrated cognitive, psychomotor, and perceptual skills.

RESULTS

In this paper, we describe a methodological extension to CTA, referred to as multi-modal task analysis (MMTA) that elicits and captures the nuances of integrated and isolated cognitive, psychomotor, and perceptual skill modalities as they apply to training and performing complex operational tasks. In the current case, we illustrate the application of the MMTA method described here to RALS training tasks. The products of the analysis are quantitatively summarized, and observations from a preliminary qualitative validation are reported.

CONCLUSIONS

We find that iterative use of the described MMTA method leads to sufficiently complete and robust task models to support encoding of cognitive, psychomotor, and perceptual skills requisite to training and performance of complex skills within ITS task models.

Prevailing Trends in Haptic Feedback Simulation for Minimally Invasive Surgery

Background The amount of direct hand-tool-tissue interaction and feedback in minimally invasive surgery varies from being attenuated in laparoscopy to being completely absent in robotic minimally invasive surgery. The role of haptic feedback during surgical skill acquisition and its emphasis in training have been a constant source of controversy. This review discusses the major developments in haptic simulation as they relate to surgical performance and the current research questions that remain unanswered. Search Strategy An in-depth review of the literature was performed using PubMed. Results A total of 198 abstracts were returned based on our search criteria. Three major areas of research were identified, including advancements in 1 of the 4 components of haptic systems, evaluating the effectiveness of haptic integration in simulators, and improvements to haptic feedback in robotic surgery. Conclusions Force feedback is the best method for tissue identification in minimally invasive surgery and haptic feedback provides the greatest benefit to surgical novices in the early stages of their training. New technology has improved our ability to capture, playback and enhance to utility of haptic cues in simulated surgery. Future research should focus on deciphering how haptic training in surgical education can increase performance, safety, and improve training efficiency.

Sensor fusion for laparoscopic surgery skill acquisition

Surgical techniques are becoming more complex and require substantial training to master. The development of automated, objective methods to analyze and evaluate surgical skill is necessary to provide trainees with reliable and accurate feedback during their training programs. We present a system to capture, visualize, and analyze the movements of a laparoscopic surgeon for the purposes of skill evaluation. The system records the upper body movement of the surgeon, the position, and orientation of the instruments, and the force and torque applied to the instruments. An empirical study was conducted using the system to record the performances of a number of surgeons with a wide range of skill. The study validated the usefulness of the system, and demonstrated the accuracy of the measurements.

Learning Kinematic Constraints in Laparoscopic Surgery

To better understand how kinematic variables impact learning in surgical training, we devised an interactive environment for simulated laparoscopic maneuvers, using either 1) mechanical constraints typical of a surgical "box-trainer" or 2) virtual constraints in which free hand movements control virtual tool motion. During training, the virtual tool responded to the absolute position in space (Position-Based) or the orientation (Orientation-Based) of a hand-held sensor. Volunteers were further assigned to different sequences of target distances (Near-Far-Near or Far-Near-Far). Training with the Orientation-Based constraint enabled much lower path error and shorter movement times during training, which suggests that tool motion that simply mirrors joint motion is easier to learn. When evaluated in physically constrained (physical box-trainer) conditions, each group exhibited improved performance from training. However, Position-Based training enabled greater reductions in movement error relative to Orientation-Based (mean difference: 14.0 percent; CI: 0.7, 28.6). Furthermore, the Near-Far-Near schedule allowed a greater decrease in task time relative to the Far-Near-Far sequence (mean -13:5 percent, CI: -19:5, -7:5). Training that focused on shallow tool insertion (near targets) might promote more efficient movement strategies by emphasizing the curvature of tool motion. In addition, our findings suggest that an understanding of absolute tool position is critical to coping with mechanical interactions between the tool and trocar.

Learning kinematic mappings in laparoscopic surgery

We devised an interactive environment in which subjects could perform simulated laparoscopic maneuvers, using either unconstrained movements or standard mechanical contact typical of a box-trainer. During training the virtual tool responded to the absolute position in space (Position-Based) or the orientation (Orientation-Based) of a hand-held sensor. Volunteers were further assigned to different sequences of target distances (Near-Far-Near or Far-Near-Far). Orientation-Based control produced much lower error and task times during training, which suggests that the motor system more easily accommodates tool use with degrees of freedom that match joint angles. When evaluated in constrained (physical box-trainer) conditions, each group exhibited improved performance from training. However, Position-Based training enabled greater reductions in movement error relative to Orientation-Based (mean -13.7%, CI:-27.1, -0.4). Furthermore, the Near-Far-Near schedule allowed a greater decrease in task time relative to the Far-Near-Far sequence (mean -13.5%, CI:-19.5, -7.5). Training at shallow insertion in virtual laparoscopy might promote more efficient movement strategies by emphasizing the curvature of tool motion. In addition, our findings suggest that an understanding of absolute tool position is critical to coping with mechanical interactions between the tool and trochar.