首页|RFA: Regularized Feature Alignment Method for Cross-Subject Human Activity Recognition
RFA: Regularized Feature Alignment Method for Cross-Subject Human Activity Recognition
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NETL
NSTL
World Scientific
Cross-subject activity recognition is challenging in the human activity recognition field. Previous studies have often assumed that training and test data follow the same distribution, which is impractical in real-world applications. Thus, models’ performance will significantly decline when applied to data collected from new unseen subjects because of the different physical conditions and human habits. To solve the above challenges, we proposed the regularized feature alignment (RFA) network. The RFA introduces a source domain selection mechanism (SDSM) based on calculating the Wasserstein distance between different subjects. Through SDSM, subjects with high similarity in the source domain can be retained, which implicitly compacts the feature subspace distribution. We implemented linear data augmentation on the retained subjects to mitigate the effects of the decline in the training set. In addition, the regularized dropout method was adopted to explicitly compact the feature subspace distributions. Finally, multi-level feature alignment is performed via maximum mean discrepancy regularization to precisely match the source and target domain. To demonstrate the effectiveness of RFA, comprehensive experiments were conducted on four public datasets under the iterative left-one-subject-out setting. The experimental results demonstrate that RFA outperformed the state-of-the-art methods in datasets with a large divergence between subjects and achieved performance comparable to the state-of-the-art methods in a subject-balanced dataset.
NETL
NSTL
World Scientific
