DA-HAR:用于环境无关WiFi人类活动识别的双对抗网络

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Long Sheng , Yue Chen , Shuli Ning , Shengpeng Wang , Bin Lian , Zhongcheng Wei
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引用次数: 0

摘要

作为新兴传感与通信集成发展的基石,基于WiFi信号的人体活动识别技术得到了广泛的研究。然而,由于环境动力学的影响,现有的活动感知模型在应用于新场景时会出现严重的性能下降。为了解决这一问题,我们提出了一种基于双对抗网络的环境无关的活动识别模型DA-HAR。该框架利用源域分类器和源-目标域鉴别器之间的对抗训练来提取与环境无关的活动特征。为了提高模型的性能,引入了一种基于伪标签预测的方法,为与源域样本相似的目标域样本分配标签,从而减轻源域和目标域之间活动特征的分布偏差。实验结果表明,与现有的识别系统相比,我们提出的模型具有更好的跨域识别性能,特别是当源域和目标域的活动特征分布显著不同时,准确率提高了6.96% ~ 11.22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DA-HAR: Dual adversarial network for environment-independent WiFi human activity recognition

As the cornerstone of the development of emerging integrated sensing and communication, human activity recognition technology based on WiFi signals has been extensively studied. However, the existing activity sensing models will suffer serious performance degradation when applied to new scenarios due to the influence of environmental dynamics. To address this issue, we present an environment-independent activity recognition model named DA-HAR, which utilizes dual adversarial network. The framework exploits adversarial training among source domain classifiers and source–target domain discriminators to extract environment-independent activity features. To improve the performance of the model, a pseudo-label prediction based approach is introduced to assign labels to the target domain samples that closely resemble the source domain samples, thus mitigating the distribution deviation of activity features between source domain and target domain. Experimental results show that our proposed model has better cross-domain recognition performance compared to state-of-the-art recognition systems, especially when the distribution of activity features in the source domain and the target domain is significantly different, the accuracy is improved by 6.96% 11.22%.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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