Deep Domain Adaptation, Pseudo-Labeling, and Shallow Network for Accurate and Fast Gait Prediction of Unlabeled Datasets
Developing personalized gait phase prediction models is difficult because acquiring accurate gait phases requires expensive experiments. This problem can be addressed via semi-supervised domain adaptation (DA), which minimizes the discrepancy between the source and target subject features. However,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Book |
| Published: |
IEEE,
2023-01-01T00:00:00Z.
|
| Subjects: | |
| Online Access: | Connect to this object online. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|