Having been studied for more than a decade, Wi-Fi human sensing still faces a major challenge in the presence of multiple persons, simply because the limited bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically separate multiple subjects. Existing solutions mostly avoid this challenge by switching to radars with GHz bandwidth, at the cost of cumbersome deployments. Therefore, could Wi-Fi human sensing handle multiple subjects remains an open question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing system with physical separability. The principle behind MUSE-Fi is that, given a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field channel variation caused by the subject significantly overwhelms variations caused by other distant subjects. Consequently, focusing on the channel state information (CSI) carried by the traffic in and out of this device naturally allows for physically separating multiple subjects. Based on this principle, we propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI, and iii) downlink beamforming feedback, where we specifically tackle signal recovery from sparse (per-user) traffic under realistic multi-user communication scenarios. Our extensive evaluations clearly demonstrate that MUSE-Fi is able to successfully handle multi-person sensing with respect to three typical applications: respiration monitoring, gesture detection, and activity recognition.

翻译：经过十多年的研究，Wi-Fi人体感知在多人场景下仍面临重大挑战，根本原因是Wi-Fi有限的带宽无法提供足够的距离分辨率来物理分离多个目标。现有解决方案大多通过转向千兆赫兹带宽的雷达来规避这一问题，但代价是部署繁琐。因此，Wi-Fi人体感知能否处理多人场景仍是一个未解难题。本文提出MUSE-Fi——首个具备物理可分离性的Wi-Fi多人感知系统。其核心原理在于：当Wi-Fi设备（如智能手机）紧邻某目标时，该目标引起的近场信道变化将显著压倒其他远距离目标引起的信道扰动。因此，聚焦于该设备收发流量所承载的信道状态信息（CSI），即可自然实现多人物理分离。基于此原理，我们为MUSE-Fi设计了三种感知策略：i）上行CSI、ii）下行CSI、iii）下行波束赋形反馈，并特别解决了实际多用户通信场景下稀疏（每用户）流量的信号恢复问题。大量实验评估表明，MUSE-Fi在呼吸监测、手势检测和活动识别三类典型应用中均能成功实现多人感知。