This research proposes and evaluates scoring and assessment methods for Virtual Reality (VR) training simulators. VR simulators capture detailed n-dimensional human motion data which is useful for performance analysis. Custom made medical haptic VR training simulators were developed and used to record data from 271 trainees of multiple clinical experience levels. DTW Multivariate Prototyping (DTW-MP) is proposed. VR data was classified as Novice, Intermediate or Expert. Accuracy of algorithms applied for time-series classification were: dynamic time warping 1-nearest neighbor (DTW-1NN) 60%, nearest centroid SoftDTW classification 77.5%, Deep Learning: ResNet 85%, FCN 75%, CNN 72.5% and MCDCNN 28.5%. Expert VR data recordings can be used for guidance of novices. Assessment feedback can help trainees to improve skills and consistency. Motion analysis can identify different techniques used by individuals. Mistakes can be detected dynamically in real-time, raising alarms to prevent injuries.

本研究提出并评估了用于虚拟现实（VR）培训模拟器的评分和评估方法。借助动态时间规整多元原型（DTW-MP）分类算法，本研究分析了271名医学生的VR训练数据，提高了专家级数据的可用性。通过运动分析，可以检测出个人使用的不同技术并动态检测错误，从而提高连续性和防止受伤。

医疗虚拟现实培训模拟器中的动态时间序列分类评分和评估