Unsupervised Gait Phase Estimation With Domain-Adversarial Neural Network and Adaptive Window | IEEE Journals & Magazine | IEEE Xplore

Unsupervised Gait Phase Estimation With Domain-Adversarial Neural Network and Adaptive Window


Abstract:

The performanceof previous machine learning models for gait phase is only satisfactory under limited conditions. First, they produce accurate estimations only when the gr...Show More

Abstract:

The performanceof previous machine learning models for gait phase is only satisfactory under limited conditions. First, they produce accurate estimations only when the ground truth of the gait phase (of the target subject) is known. In contrast, when the ground truth of a target subject is not used to train an algorithm, the estimation error noticeably increases. Expensive equipment is required to precisely measure the ground truth of the gait phase. Thus, previous methods have practical shortcoming when they are optimized for individual users. To address this problem, this study introduces an unsupervised domain adaptation technique for estimation without the true gait phase of the target subject. Specifically, a domain-adversarial neural network was modified to perform regression on continuous gait phases. Second, the accuracy of previous models can be degraded by variations in stride time. To address this problem, this study developed an adaptive window method that actively considers changes in stride time. This model considerably reduces estimation errors for walking and running motions. Finally, this study proposed a new method to select the optimal source subject (among several subjects) by defining the similarity between sequential embedding features.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 26, Issue: 7, July 2022)
Page(s): 3373 - 3384
Date of Publication: 23 December 2021

ISSN Information:

PubMed ID: 34941536

Funding Agency:


References

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