1 |
LU Y, LV S H, WANG X D, et al. A survey on Wi⁃Fi based human behavior analysis technology [J]. Chinese journal of computers, 2019, 42(2): 1–21. DOI: 10.11897/SP.J.1016.2019.00231
DOI
|
2 |
WANG J, ZHAO Y N, FAN X X, et al. Device⁃free identification using intrinsic CSI Features [J]. IEEE transactions on vehicular technology, 2018, 67(9): 8571–8581. DOI:10.1109/tvt.2018.2853185
DOI
|
3 |
MA Y S, ZHOU G, WANG S Q. WiFi sensing with channel state information [J]. ACM computing surveys, 2019, 52(3): 1–36. DOI:10.1145/3310194
DOI
|
4 |
HALPERIN D, HU W J, SHETH A, et al. Tool release [J]. ACM SIGCOMM computer communication review, 2011, 41(1): 53. DOI:10.1145/1925861.1925870
DOI
|
5 |
HE W F, WU K S, ZOU Y P, et al. WiG: WiFi⁃based gesture recognition system [C]//2015 24th International Conference on Computer Communication and Networks (ICCCN). Las Vegas, USA, 2015: 1–7. DOI:10.1109/icccn.2015.7288485
DOI
|
6 |
LI H, YANG W, WANG J X, et al. WiFinger: talk to your smart devices with finger⁃grained gesture [C]//The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA, 2016: 250–261. DOI:10.1145/2971648.2971738
DOI
|
7 |
WANG Y, YANG J, CHEN Y Y, et al. Tracking human queues using single⁃point signal monitoring [C]//Annual International Conference on Mobile Systems, Applications, and Services. New Hampshire, USA, 2014:42–54. DOI: 10.1145/2594368.2594382
DOI
|
8 |
MA Y S, ZHOU G, WANG S Q, et al. SignFi: sign language recognition using Wi⁃Fi [J]. ACM on interactive, mobile, wearable and ubiquitous technologies, 2018, 2(1): 1–21. DOI:10.1145/3191755
DOI
|
9 |
OHARA K, MAEKAWA T, MATSUSHITA Y. Detecting state changes of indoor everyday objects using Wi⁃Fi channel state information [J]. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2017, 1(3): 1–28. DOI:10.1145/3131898
DOI
|
10 |
SINGH U, DETERME J F, HORLIN F, et al. Crowd forecasting based on wifi sensors and lstm neural networks [J]. IEEE transactions on instrumentation and measurement, 2020: 1. DOI:10.1109/tim.2020.2969588
DOI
|
11 |
BAHL P, PADMANABHAN V N. Radar: an in⁃building RF⁃based user location and tracking system [C]//19th Annual Joint Conference of the IEEE Computer and Communications Societies. Tel Aviv, Israel, 2000:775–784.DOI:10.1109/infcom.2000.832252
DOI
|
12 |
KOTARU M, JOSHI K, BHARADIA D, et al. SpotFi: decimeter level localization using Wi⁃Fi [J]. ACM SIGCOMM Computer communication review, 2015, 45(4): 269–282. DOI:10.1145/2829988.2787487
DOI
|
13 |
SCHMIDT R. Multiple emitter location and signal parameter estimation [J]. IEEE transactions on antennas and propagation, 1986, 34(3): 276–280. DOI:10.1109/tap.1986.1143830
DOI
|
14 |
VASISHT D, KUMAR S, KATABI D. Decimeter⁃level localization with a single Wi⁃Fi access point [C]//The 13th USENIX Symposium on Networked Systems Design and Implementation. Santa Clara, USA, 2016: 165–178
|
15 |
QIAN K, WU C S, YANG Z, et al. Widar: decimeter⁃level passive tracking via velocity monitoring with commodity Wi⁃Fi [C]//ACM Mobihoc 2017. Chennai, India, 2017: 1–10. DOI: http://dx.doi.org/10.1145/3084041.3084067
DOI
|
16 |
QIAN K, WU C S, ZHANG Y, et al. Widar 2.0: passive human tracking with a single Wi⁃Fi link [C]//The 16th Annual International Conference on Mobile Systems. Applications, and Services. New York, USA, 2018: 1–12. DOI:10.1145/3210240.3210314
DOI
|
17 |
LU G Y, SONG J K. 3D image⁃based indoor localization joint with WiFi positioning [C]// ACM on International Conference on Multimedia Retrieval. New York, USA, 2018: 1–8. DOI:10.1145/3206025.3206070
DOI
|
18 |
GUO A Y, XU Z M, CHEN L Q. A human action recognition method based on Wi⁃Fi channel state information [J]. Chinese journal of sensors and actuators, 2019, 32(11): 1688–1693. DOI: 10.3969 /j.issn.1004-1699.2019.11.015
DOI
|
19 |
BU Q R, YANG G, MING X X, et al. Deep transfer learning for gesture recognition with WiFi signals [J]. Personal and ubiquitous computing, 2020. DOI:10.1007/s00779-019-01360-8
DOI
|
20 |
ARSHAD S, FENG C H, YU R Y, et al. Leveraging transfer learning in multiple human activity recognition using WiFi signal [C]//2019 IEEE 20th International Symposium on “A World of Wireless, Mobile and Multimedia Networks”. Washington, USA, 2019: 1–10. DOI:10.1109/wowmom.2019.8793019
DOI
|
21 |
WANG H, ZHANG D Q, MA J Y, et al. Human respiration detection with commodity Wi⁃Fi devices [C]//ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA, 2016: 25–36. DOI:10.1145/2971648.2971744
DOI
|
22 |
ZENG Y Z, PATHAK P H, MOHAPATRA P. WiWho: WiFi⁃based person identification in smart spaces [C]//15th ACM/IEEE International Conference on Information Processing in Sensor Networks. Vienna, Austria, 2016: 1–12. DOI: 10.1109/ipsn.2016.7460727
DOI
|
23 |
ZHANG J, WEI B, HU W, et al. WiFi⁃ID: human identification using WiFi signal [C]//2016 International Conference on Distributed Computing in Sensor Systems. Washington, USA, 2016: 1–8. DOI:10.1109/dcoss.2016.30
DOI
|
24 |
HONG F, WANG X, YANG Y N, et al. WFID: passive device⁃free human identification using Wi⁃Fi signal [C]//Mobiquitous’16. Hiroshima, Japan, 2016: 1⁃10. DOI: http://dx.doi.org/10.1145/2994374.2994377
DOI
|
25 |
WANG W, LIU A X, SHAHZAD M. Gait recognition using Wi⁃Fi signals [C]// UbiComp ’16. Heidelberg, Germany, 2016: 363–373. DOI: http://dx.doi.org/10.1145/2971648.2971670
DOI
|
26 |
WANG J, GAO Q H, PAN M, et al. Device⁃free wireless sensing: challenges, opportunities, and applications [J]. IEEE network, 2018, 32(2):132–137. DOI: 10.1109/MNET.2017.1700133
DOI
|
27 |
WANG W, LIU A X, SHAHZAD M, et al. Understanding and modeling of Wi⁃Fi signal based human activity recognition [C]//MobiCom’15. Paris, France, 2015: 65–76. DOI: http://dx.doi.org/10.1145/2789168.2790093
DOI
|
28 |
YANG Z, ZHENG Y, WU C S. Intelligent wireless sensing in the AIoT: features, algorithms, and data sets [J]. Communications of the CCF, 2020, 16(2): 50–56
|
29 |
ZHENG Y, ZHANG Y, QIAN K, et al. Zero⁃effort cross⁃domain gesture recognition with Wi⁃Fi [C]//17th Annual International Conference on Mobile Systems, Applications, and Services. New York, USA: 2019. DOI:10.1145/3307334.3326081
DOI
|
30 |
WANG J, GAO Q H, MA X R, et al. Learning to sense: deep learning for wireless sensing with less training efforts [J]. IEEE wireless communications, 2020, 27(3): 156–162. DOI:10.1109/mwc.001.1900409
DOI
|