FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas

International audienceThe 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%. 1 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

Calibrating time-variant, device-specific phase noise for COTS WiFi devices

Current COTS WiFi based work on wireless motion sensing extracts human movements such as keystroking and hand motion mainly from amplitude training to classify different types of motions, as obtaining meaningful phase values is very challenging due to time-varying phase noises occurred with the movement. However, the methods based only on amplitude training are not very practical since their accuracy is not environment and location independent. This paper proposes an effective phase noise calibration technique which can be broadly applicable to COTS WiFi based motion sensing. We leverage the fact that multi-path for indoor environment contains certain static paths, such as reflections from wall or static furniture, as well as dynamic paths due to human hand and arm movements. When a hand moves, the phase value of the signal from the hand rotates as the path length changes and causes the superposition of signals over static and dynamic paths in antenna and frequency domain. To evaluate the effectiveness of the proposed technique, we experiment with a prototype system that can track hand gestures in a non-intrusive manner, i.e. users are not equipped with any device, using COTS WiFi devices. Our evaluation shows that calibrated phase values provide much rich, yet robust information on motion tracking ??? 80th percentile angle estimation error up to 14 degrees, 80th percentile tracking error up to 15 cm, and its robustness to the environment and the speed of movement