RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices

By Hao Wang, Daqing Zhang, Yasha Wang, Junyi Ma, Yuxiang Wang, Shengjie Li. IEEE Transactions on Mobile Computing, 2017

588 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ca117d143e0e3f6a073ac6705b472072c8f676e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Daqing Zhang
2. Gang Zhou
3. Hong Mei
4. Hongbo Liu
5. Xiang Li
6. Jian Liu
7. Shengjie Li
8. Yasha Wang

In Computer Vision and Pattern Recognition

1. Daqing Zhang
2. Gaurav Sharma
3. Gang Zhou
4. Tolga Soyata
5. George Azzopardi
6. Xueyi Wang
7. Chen Chen
8. Youfang Lin

In Electrical and Electronic Engineering

1. Daqing Zhang
2. Gang Zhou
3. Hong Mei
4. Tolga Soyata
5. George Azzopardi
6. Xueyi Wang
7. Chen Wang
8. Hongbo Liu

Citations in the corpus

• Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “Compared with coarse RSSI with only amplitude for the whole channel, CSI contains both amplitude and phase of each subcarrier, which has also been used for activity recognition [12, 35, 39, 54].”
• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, background): “Recent progress in detection with WiFi has explored the detection of human events, such as falls [82, 92] and motion [18, 37], object detection [102], and human presence detection [3, 98].”
• A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “ing toward the measurement of multiple biomarkers, such as respiration [31], [32], heartbeat [33], and motion detection [34], [35], although practical designs are still an active”
• Elderly Fall Detection Systems: A Literature Survey (2020, background): “In Wang et al. (2017a,b), a new fall detection approach which uses ambient sensors is proposed.”
• WiFi Sensing with Channel State Information (2019, methodology): “Detection: binary classification Human Presence Detection [3, 24, 67, 73, 75, 83, 112, 114, 121, 148, 149, 152] Human Event Detection: Fall [32, 68, 92, 135, 137], Motion [23, 27, 51, 55], Walking [126], Posture Change [57, 58], Intrusion [51, 59], Sleeping [57, 58], Keystroke [5], Driving Fatigue [”
• Wireless Sensing for Human Activity: A Survey (2020, background): “Moreover, Wang et al. [26] find that the CSI phase difference over two antennas is more sensitive to fall action.”