Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation shows that the proposed method is able to extract highly effective feedback at a high level of efficiency.
CITATION STYLE
Ma, X., Wijewickrema, S., Zhou, Y., Zhou, S., O’Leary, S., & Bailey, J. (2017). Providing effective real-time feedback in simulation-based surgical training. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10434 LNCS, pp. 566–574). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_64
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