FarSense

Author:

Zeng Youwei1,Wu Dan1,Xiong Jie2,Yi Enze1,Gao Ruiyang1,Zhang Daqing3

Affiliation:

1. Key Laboratory of High Confidence Software Technologies (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

2. College of Information and Computer Sciences, University of Massachusetts, Amherst, USA

3. Key Laboratory of High Confidence Software Technologies (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China, Institut Mines, Telecom SudParis, Evry, France

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%.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.

Funder

National Key Research and Development Plan

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference69 articles.

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4. Khadija Baba Lahcen Bahi and Latifa Ouadif. 2014. Enhancing geophysical signals through the use of Savitzky-Golay filtering method. Geofísica internacional 53 4 (2014) 399--409. Khadija Baba Lahcen Bahi and Latifa Ouadif. 2014. Enhancing geophysical signals through the use of Savitzky-Golay filtering method. Geofísica internacional 53 4 (2014) 399--409.

5. George EP Box Gwilym M Jenkins Gregory C Reinsel and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons. George EP Box Gwilym M Jenkins Gregory C Reinsel and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons.

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