True Detect: Deep Learning-Based Device-Free Activity Recognition Using WiFi

Author(s):  
Muhammad Sulaiman ◽  
Syed Ali Hassan ◽  
Haejoon Jung
2018 ◽  
Vol 17 (2) ◽  
pp. 293-306 ◽  
Author(s):  
Lina Yao ◽  
Quan Z. Sheng ◽  
Xue Li ◽  
Tao Gu ◽  
Mingkui Tan ◽  
...  

2014 ◽  
Vol 6 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Stephan Sigg ◽  
Shuyu Shi ◽  
Yusheng Ji

The authors consider two untackled problems in RF-based activity recognition: the distinction of simultaneously conducted activities of individuals and the recognition of gestures from purely time-domain-based features. Recognition is based on a single antenna system. This is important for the application in end-user devices which are usually single-antenna systems and have seldom access to more sophisticated, e.g. frequency-based features. In case studies with software defined radio nodes utilised in an active, device-free activity recognition (DFAR) system, the authors observe a good recognition accuracy for the detection of multiple simultaneously conducted activities with two and more receive devices. Four gestures and two baseline situations are distinguished with good accuracy in a second case study.


Author(s):  
Mingzhi Pang ◽  
Xu Yang ◽  
Jing Liu ◽  
Peihao Li ◽  
Faren Yan ◽  
...  

2020 ◽  
Vol 69 (5) ◽  
pp. 5416-5425 ◽  
Author(s):  
Jie Wang ◽  
Yunong Zhao ◽  
Xiaorui Ma ◽  
Qinghua Gao ◽  
Miao Pan ◽  
...  

2019 ◽  
Vol 15 (2) ◽  
pp. 954-964 ◽  
Author(s):  
Qinghua Gao ◽  
Jie Wang ◽  
Liming Zhang ◽  
Hao Yue ◽  
Bin Lin ◽  
...  

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