论文标题
具有动态时间序列分类的医疗VR培训模拟器中的评分和评估
Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification
论文作者
论文摘要
这项研究提出并评估虚拟现实（VR）培训模拟器的评分和评估方法。 VR模拟器捕获了详细的N维人类运动数据，可用于性能分析。开发了定制的医疗触觉VR培训模拟器，并用于记录来自271名临床经验水平的271名学员的数据。提出了DTW多元原型（DTW-MP）。 VR数据被归类为新手，中级或专家。适用于时间序列分类的算法的准确性为：动态时间扭曲1-Nearest邻居（DTW-1NN）60％，最接近的质心软dttw分类77.5％，深度学习：Resnet 85％，FCN 75％，CNN 72.5％和McDCNN 28.5％。专家VR数据记录可用于新手指导。评估反馈可以帮助学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。可以实时动态检测错误，从而提高警报以防止伤害。
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.
