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.

翻译：人类活动识别（HAR）在人机交互和健康监测等众多应用中发挥着关键作用。然而，由于训练数据分布与现实世界数据分布之间存在差异，HAR模型仍面临挑战，这在跨用户场景中尤为明显。本文提出了一种新颖的框架，称为基于扩散的噪声中心对抗学习领域自适应（Diff-Noise-Adv-DA），旨在通过利用生成扩散建模和对抗学习技术来解决这些挑战。传统的HAR模型常常难以应对用户行为和传感器数据分布的多样性。Diff-Noise-Adv-DA创新性地整合了扩散模型中的固有噪声，利用其潜在信息来增强领域自适应能力。具体而言，该框架将噪声转化为活动和领域类别信息的关键载体，从而促进跨不同用户领域的鲁棒分类。实验评估表明，Diff-Noise-Adv-DA在提升跨用户HAR模型性能方面具有显著效果，超越了传统的领域自适应方法。该框架不仅缓解了分布不匹配问题，还通过基于噪声的去噪技术提升了数据质量。