TY - GEN
T1 - A multi-person respiration monitoring system using COTS wifi devices
AU - Zeng, Youwei
AU - Liu, Zhaopeng
AU - Wu, Dan
AU - Liu, Jinyi
AU - Zhang, Jie
AU - Zhang, Daqing
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/9/10
Y1 - 2020/9/10
N2 - In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. However, existing approaches only work when multiple persons exhibit dramatically different respiration rates and the performance degrades significantly when the targeted subjects have similar rates. What's more, they can only obtain the average respiration rate over a period of time and fail to capture the detailed rate change over time. These two constraints greatly limit the application of the proposed approaches in real life. Different from the existing approaches that apply spectral analysis to the CSI amplitude (or phase difference) to obtain respiration rate information, we leverage the multiple antennas provided by the commodity WiFi hardware and model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to obtain the reparation information of each person. In this demo, we will demonstrate MultiSense - a multi-person respiration monitoring system using COTS WiFi devices.
AB - In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. However, existing approaches only work when multiple persons exhibit dramatically different respiration rates and the performance degrades significantly when the targeted subjects have similar rates. What's more, they can only obtain the average respiration rate over a period of time and fail to capture the detailed rate change over time. These two constraints greatly limit the application of the proposed approaches in real life. Different from the existing approaches that apply spectral analysis to the CSI amplitude (or phase difference) to obtain respiration rate information, we leverage the multiple antennas provided by the commodity WiFi hardware and model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to obtain the reparation information of each person. In this demo, we will demonstrate MultiSense - a multi-person respiration monitoring system using COTS WiFi devices.
KW - blind source separation
KW - channel state information (CSI)
KW - multi-person respiration sensing
KW - wifi sensing
UR - https://www.scopus.com/pages/publications/85091856450
U2 - 10.1145/3410530.3414325
DO - 10.1145/3410530.3414325
M3 - Conference contribution
AN - SCOPUS:85091856450
T3 - UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
SP - 195
EP - 198
BT - UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery
T2 - 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020
Y2 - 12 September 2020 through 17 September 2020
ER -