VI Net View Invariant Quality of Human Movement Assessment
We propose a view invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end to end convolutional neural network consists of two stages, where at first a view invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre trained 2D convolutional neural network (CNN) (e.g. VGG 19 or ResNeXt 50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly available, multi view, non skeleton, non mocap, rehabilitation movement dataset (QMAR) and provide results for both cross subject and cross view scenarios on this dataset. We show that VI Net achieves average rank correlation of 0.66 on cross subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.
Sensors