  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.
