Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning
<|reference_start|>Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning: Human Activity Recognition (HAR) plays a crucial role in various applications such as human-computer interaction and healthcare monitoring. However, challenges persist in HAR models due to the data distribution differences between training and real-world data distributions, particularly evident in cross-user scenarios. This paper introduces a novel framework, termed Diffusion-based Noise-centered Adversarial Learning Domain Adaptation (Diff-Noise-Adv-DA), designed to address these challenges by leveraging generative diffusion modeling and adversarial learning techniques. Traditional HAR models often struggle with the diversity of user behaviors and sensor data distributions. Diff-Noise-Adv-DA innovatively integrates the inherent noise within diffusion models, harnessing its latent information to enhance domain adaptation. Specifically, the framework transforms noise into a critical carrier of activity and domain class information, facilitating robust classification across different user domains. Experimental evaluations demonstrate the effectiveness of Diff-Noise-Adv-DA in improving HAR model performance across different users, surpassing traditional domain adaptation methods. The framework not only mitigates distribution mismatches but also enhances data quality through noise-based denoising techniques.<|reference_end|>
@article{ye2024adversarial,
    title={Adversarial Domain Adaptation for Cross-user Activity Recognition Using
  Diffusion-based Noise-centred Learning},
    author={Xiaozhou Ye, Kevin I-Kai Wang},
    journal={arXiv preprint arXiv:2408.03353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03353},
    primaryClass={cs.LG cs.AI cs.HC}
}