基于mmWave接入点的被动隐私保护人体定位
Passive and Privacy-preserving Human Localization via mmWave Access Points for Social Distancing
摘要：
疫情的爆发深刻地改变了我们的生活，尤其是我们的社交习惯和沟通行为。虽然这种巨大的冲击严重影响了人类的互动规则，但新的本地化技术正在出现，以帮助社会遵守新的政策，例如社交距离。无线传感和机器学习非常适合以隐私保护的方式减轻病毒传播。然而，其广泛部署需要具有成本效益的安装和操作解决方案。在公共环境中，需要监控个人定位信息（如社交距离），以避免在未正确观察时造成安全威胁。为此，可以利用无线设备的高渗透性来持续分析和学习传播环境，从而被动地检测违规行为并在需要时触发警报。在本文中，我们描述了一种新型的被动和隐私保护的人体定位解决方案，该解决方案依赖于毫米波通信的定向传输特性来监控社交距离，并在违规情况下通知该地区的人。因此，以保护隐私和具有成本效益的方式应对社交距离挑战。我们的解决方案在测试场景中提供了约99%的整体准确性。
英文摘要：
The pandemic outbreak has profoundly changed our life, especially our social habits and communication behaviors. While this dramatic shock has heavily impacted human interaction rules, novel localization techniques are emerging to help society in complying with new policies, such as social distancing. Wireless sensing and machine learning are well suited to alleviate viruses propagation in a privacy-preserving manner. However, its wide deployment requires cost-effective installation and operational solutions. In public environments, individual localization information-such as social distancing-needs to be monitored to avoid safety threats when not properly observed. To this end, the high penetration of wireless devices can be exploited to continuously analyze-and-learn the propagation environment, thereby passively detecting breaches and triggering alerts if required. In this paper, we describe a novel passive and privacy-preserving human localization solution that relies on the directive transmission properties of mmWave communications to monitor social distancing and notify people in the area in case of violations. Thus, addressing the social distancing challenge in a privacy-preserving and cost-efficient manner. Our solution provides an overall accuracy of about 99% in the tested scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
