TY - GEN
T1 - Human respiration detection with commodity WiFi devices
T2 - 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
AU - Wang, Hao
AU - Zhang, Daqing
AU - Ma, Junyi
AU - Wang, Yasha
AU - Wang, Yuxiang
AU - Wu, Dan
AU - Gu, Tao
AU - Xie, Bing
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/9/12
Y1 - 2016/9/12
N2 - Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
AB - Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
KW - Channel state information (CSI)
KW - The fresnel zone
KW - WiFi
U2 - 10.1145/2971648.2971744
DO - 10.1145/2971648.2971744
M3 - Conference contribution
AN - SCOPUS:84991463511
T3 - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 25
EP - 36
BT - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
Y2 - 12 September 2016 through 16 September 2016
ER -