TY - GEN
T1 - WiCross
T2 - 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC 2023
AU - Shi, Weiyan
AU - Wang, Xuanzhi
AU - Niu, Kai
AU - Wang, Leye
AU - Zhang, Daqing
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/10/8
Y1 - 2023/10/8
N2 - Detecting whether a target crosses the given zone (e.g., a door) can enable various practical applications in smart homes, including intelligent security and people counting. The traditional infrared-based approach only covers a line and can be easily cracked. In contrast, reusing the ubiquitous WiFi devices deployed in homes has the potential to cover a larger area of interest as WiFi signals are scattered throughout the entire space. By detecting the walking direction (i.e., approaching and moving away) with WiFi signal strength change, existing work can identify the behavior of crossing between WiFi transceiver pair. However, this method mistakenly classifies the turn-back behavior as crossing behavior, resulting in a high false alarm rate. In this paper, we propose WiCross, which can accurately distinguish the turn-back behavior with the phase statistics pattern of WiFi signals and thus robustly identify whether the target crosses the area between the WiFi transceiver pair. We implement WiCross with commercial WiFi devices and extensive experiments demonstrate that WiCross can achieve an accuracy higher than 95% with a false alarm rate of less than 5%.
AB - Detecting whether a target crosses the given zone (e.g., a door) can enable various practical applications in smart homes, including intelligent security and people counting. The traditional infrared-based approach only covers a line and can be easily cracked. In contrast, reusing the ubiquitous WiFi devices deployed in homes has the potential to cover a larger area of interest as WiFi signals are scattered throughout the entire space. By detecting the walking direction (i.e., approaching and moving away) with WiFi signal strength change, existing work can identify the behavior of crossing between WiFi transceiver pair. However, this method mistakenly classifies the turn-back behavior as crossing behavior, resulting in a high false alarm rate. In this paper, we propose WiCross, which can accurately distinguish the turn-back behavior with the phase statistics pattern of WiFi signals and thus robustly identify whether the target crosses the area between the WiFi transceiver pair. We implement WiCross with commercial WiFi devices and extensive experiments demonstrate that WiCross can achieve an accuracy higher than 95% with a false alarm rate of less than 5%.
KW - Behavior Detecting
KW - Channel State Information (CSI)
KW - WiFi Sensing
KW - Wireless Sensing
UR - https://www.scopus.com/pages/publications/85175493396
U2 - 10.1145/3594739.3610706
DO - 10.1145/3594739.3610706
M3 - Conference contribution
AN - SCOPUS:85175493396
T3 - UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing
SP - 133
EP - 136
BT - UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing
PB - Association for Computing Machinery, Inc
Y2 - 8 October 2023
ER -