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Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data
Furthermore, the proposed privacy-preserved orientation-level features are utilized to assist the position-level features in both of the two stages for enhancing medical action recognition performance.
Learning with Noisy Labels for Human Fall Events Classification: Joint Cooperative Training with Trinity Networks
The robustness and performance of the noisy label learning framework is improved by using the two teacher modules and one student module in the proposed JoCoT.
Two-stage Fall Events Classification with Human Skeleton Data
For better healthcare, it is thus not enough to do binary fall classification but to extend it to multiple fall events classification.
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