Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20185258 | SENSORS |
Keywords | DocType | Volume |
movement analysis,view-invariant convolutional neural network (CNN),health monitoring | Journal | 20 |
Issue | ISSN | Citations |
18 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faegheh Sardari | 1 | 0 | 0.68 |
| Adeline Paiement | 2 | 61 | 7.88 |
| Sion L. Hannuna | 3 | 69 | 10.37 |
| Majid Mirmehdi | 4 | 955 | 96.94 |