Simulation-based training (SBT) is gaining popularity as a low-cost and convenient training technique in a vast range of applications. However, for a SBT platform to be fully utilized as an effective training tool, it is essential that feedback on performance is provided automatically in real-time during training. It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT. Existing methods either have low effectiveness in improving novice skills or suffer from low efficiency, resulting in their inability to be used in real-time. In this paper, we propose a neural network based method to generate feedback using the adversarial technique. The proposed method utilizes a bounded adversarial update to minimize a L1 regularized loss via back-propagation. We empirically show that the proposed method can be used to generate simple, yet effective feedback. Also, it was observed to have high effectiveness and efficiency when compared to existing methods, thus making it a promising option for real-time feedback generation in SBT.
The simplest way to provide feedback in SBT is the rule-based approach. The follow-me" approach (ghost drill) @cite_24 and the step-by-step" approach @cite_13 in surgical simulation are examples of this approach. However, it may be hard for a novice who has limited experience to follow a ghost drill at his own pace, and step-by-step feedback will not respond if the trainee does not follow the suggested paths.
@cite_24 @cite_13
2049629312 2547872309
Abstract Objective We present a dental training simulator that provides a virtual reality (VR) environment with haptic feedback for dental students to practice dental surgical skills in the context of a crown preparation procedure. The simulator addresses challenges in traditional training such as the subjective nature of surgical skill assessment and the limited availability of expert supervision. Methods and materials We identified important features for characterizing the quality of a procedure based on interviews with experienced dentists. The features are patterns combining tool position, tool orientation, and applied force. The simulator monitors these features during the procedure, objectively assesses the quality of the performed procedure using hidden Markov models (HMMs), and provides objective feedback on the user's performance in each stage of the procedure. We recruited five dental students and five experienced dentists to evaluate the accuracy of our skill assessment method and the quality of the system's generated feedback. Results The experimental results show that HMMs with selected features can correctly classify all test sequences into novice and expert categories. The evaluation also indicates a high acceptance rate from experts for the system's generated feedback. Conclusion In this work, we introduce our VR dental training simulator and describe a mechanism for providing objective skill assessment and feedback. The HMM is demonstrated as an effective tool for classifying a particular operator as novice-level or expert-level. The simulator can generate tutoring feedback with quality comparable to the feedback provided by human tutors. One of the roadblocks to the wide-spread use of virtual reality simulation as a surgical training platform is the need for expert supervision during training to ensure proper skill acquisition. To fully utilize the capacity of virtual reality in surgical training, it is imperative that the guidance process is automated. In this paper, we discuss a method of providing one aspect of performance guidance: advice on the steps of a surgery or procedural guidance. We manually segment the surgical trajectory of an expert surgeon into steps and present them one at a time to guide trainees through a surgical procedure. We show, using a randomized controlled trial, that this form of guidance is effective in moving trainee behavior towards an expert ideal. To support practice variation and different surgical styles adopted by experts, separate guidance templates have to be generated. To enable this, we introduce a method of automatically segmenting a surgical trajectory into steps. We propose a pre-processing step that uses domain knowledge specific to our application to reduce the solution space. We show how this can be incorporated into existing trajectory segmentation methods, as well as a greedy approach that we propose. We compare this segmentation method to existing techniques and show that it is accurate and efficient.