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.

这项研究提出并评估了虚拟现实（VR）培训模拟器的评分和评估方法。VR模拟器捕获详细的n维人体运动数据，这对于性能分析很有用。开发了定制的医疗触觉VR训练模拟器，并将其用于记录来自271名具有多种临床经验水平的受训者的数据。提出了DTW多元原型（DTW-MP）。VR数据分为新手，中级或专家。应用于时间序列分类的算法的准确性为：动态时间规整1最近邻（DTW-1NN）60％，最近的质心SoftDTW分类77.5％，深度学习：ResNet 85％，FCN 75％，CNN 72.5％和MCDCNN 28.5 ％。专家VR数据记录可用于指导新手。评估反馈可以帮助受训人员提高技能和一致性。运动分析可以识别个人使用的不同技术。错误可以实时动态检测，并发出警报以防止伤害。