FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense–the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the “blind spots” issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications.

💡 Research Summary

The paper “FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas” presents a groundbreaking system that significantly extends the operational range and reliability of contactless respiration monitoring using ubiquitous commercial WiFi devices. The core challenge addressed is the limited sensing range (typically 2-4 meters) and the existence of “blind spots” in prior WiFi-based sensing methods, which have hindered real-world deployment, especially in homes where the WiFi access point may not be in the same room as the subject.

FarSense’s fundamental innovation lies in using the ratio of Channel State Information (CSI) readings from two antennas on a standard WiFi card, instead of the raw CSI from a single antenna. This CSI ratio operation effectively cancels out common-mode noise sources, such as time-varying phase offsets caused by lack of tight synchronization and power amplifier uncertainties, which plague individual CSI streams. The resulting ratio signal is much cleaner and more sensitive to subtle chest movements induced by breathing.

The system leverages two novel schemes. First, it employs the CSI ratio itself to boost the signal-to-noise ratio, thereby dramatically increasing the feasible sensing distance. Second, and crucially, the division operation yields a stable phase component in the CSI ratio. FarSense then elaborately combines the amplitude and phase information of this CSI ratio. These two components are orthogonal and complementary in their sensitivity to respiration at different locations. By fusing them, the system successfully addresses the “blind spot” issue where either amplitude or phase alone might fail to detect respiration, ensuring robust detection across all locations.

The authors establish a theoretical “CSI-ratio model” that formalizes the relationship between target movement and changes in the CSI ratio, providing a foundational framework for fine-grained wireless sensing beyond just respiration monitoring.

Extensive experiments demonstrate FarSense’s superior performance. It can accurately monitor human respiration even when the target is 8 meters away from the WiFi transceiver pair, more than doubling the sensing range of state-of-the-art methods while maintaining a 100% detection rate. The system proves robust in challenging real-life scenarios: it works when the transmitter and receiver are in different rooms with a wall in between (enabling through-wall sensing for the first time with commodity WiFi), and when both devices are mounted on the ceiling far from the subject. This represents a major step towards practical, unobtrusive in-home respiration monitoring leveraging existing WiFi infrastructure without requiring specialized hardware. The proposed CSI-ratio method is also highlighted as a general technique that could benefit a wide array of other RF-based sensing applications.