Deep Domain Adaptation, Pseudo-Labeling, and Shallow Network for Accurate and Fast Gait Prediction of Unlabeled Datasets

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,...

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Bibliographic Details
Main Authors: Jaeyoung Na, Hyunwoo Kim, Giuk Lee, Woochul Nam
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Domain adaptation
dual-stage architecture
gait phase
inference speed
Online Access:https://ieeexplore.ieee.org/document/10116064/

Internet

https://ieeexplore.ieee.org/document/10116064/

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