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

Abstract: 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.

• The paper introduces the FarSense system, which utilizes the Channel State Information (CSI) amplitude and phase ratio from two antennas to significantly enhance sensitivity for WiFi-based respiration sensing.
• By combining orthogonal amplitude and phase information from the CSI ratio, FarSense effectively avoids sensing "blind spots" and generates a more reliable respiration pattern than methods using amplitude or phase alone.
• Experimental validation shows FarSense extends the sensing range beyond 8 meters, representing a more than 100% improvement, and enables accurate through-wall respiration monitoring with minimal error.

An Overview of FarSense for Enhanced Respiration Sensing with Commodity WiFi

The paper "FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas" presents an innovative approach to extend the range of WiFi-based respiration sensing beyond current limitations. WiFi-based sensing has garnered interest due to its potential for contactless monitoring of human activities, particularly respiration, which is a critical health metric. However, existing methods are constrained by a limited sensing range, typically requiring the target to be close to the transceivers. This research proposes FarSense, a system that utilizes the ratio of Channel State Information (CSI) readings from two antennas to enhance sensing capabilities, thereby significantly increasing the achievable sensing range.

Key Contributions and Insights

1. CSI-Ratio Model: The authors introduce a CSI-ratio model, wherein the CSI amplitude ratio between two antennas is used to enhance sensitivity. The phase information, usually unstable due to phase offset, is now effectively utilized post-cancellation of the phase offset through this ratio. This model leverages the properties of CSI, such as path length dynamics, aligning with the circular arc trajectory in the complex plane, to infer respiration sensitively.
2. Improved Sensing Through Amplitude and Phase Combination: The paper argues for integrating orthogonal amplitude and phase information extracted from the CSI ratio. This mitigates the "blind spots" issue that arises when relying on amplitude or phase alone. By projecting the complex-valued CSI ratio onto various axes, a superior respiration pattern is generated, which is subsequently selected based on the breathing-to-noise ratio (BNR).
3. Range Extension and Through-Wall Sensing: Extensive experimentation shows the proposed system's sensing range extends over 8 meters, a more than 100% improvement over existing solutions like HRD and FullBreathe. The capability of through-wall sensing posits FarSense as a foundational shift towards realistic application in home environments where WiFi hardware is commonplace.

Experimental Validation and Implications

The authors validate FarSense via experiments with varied human positions and postures, including through-wall sensing and scenarios where the transceiver pair and the subject are separated by significant distances. These scenarios exhibit FarSense's robustness and accuracy, with mean absolute errors below 0.5 BPM even in challenging environments. This positions FarSense as an effective tool for broad deployment in home settings for continuous health monitoring.

Future Directions

The findings prompt consideration of multi-target detection and wider utilization of antennas for CSI collection. FarSense sets a precedent for leveraging commodity hardware to achieve sensing traditionally restricted to specialized devices. Future work could further improve range and separation capabilities by incorporating technologies like MIMO and expanding to other RF technologies such as RFID and LTE.

Overall, FarSense represents a promising advance in WiFi-based respiration monitoring technology, with potential implications for healthcare and smart home systems. The approach may be further refined to support multiple subjects and adapt to varying environmental conditions, marking a significant step towards practical, real-world deployment of WiFi-based sensing technologies.