WiWho: WiFi-Based Person Identification in Smart Spaces

By Yunze Zeng, Parth H. Pathak, Prasant Mohapatra. International Symposium on Information Processing in Sensor Networks, 2016

413 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/94a37f6352899d0e3a01d8d8c0200b59e91cf870

How researchers see this paper

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

In Biomedical Engineering

1. Gang Zhou
2. Mingmin Zhao
3. Antonio Torralba
4. Kun Qian
5. Muhammad Shahzad
6. Dina Katabi
7. Yunhao Liu
8. Chen Wang

In Electrical and Electronic Engineering

1. Zheng Yang
2. Yunhao Liu
3. Gang Zhou
4. Kun Qian
5. Chenshu Wu
6. Guidong Zhang
7. Yi Zhang
8. Alex X. Liu

In Signal Processing

1. Gang Zhou
2. Hang Zhao
3. Kun Qian
4. Yunhao Liu
5. Zheng Yang
6. Chenshu Wu
7. Yonglong Tian
8. Antonio Torralba

Citations in the corpus

• Rapid (2017, methodology): “To achieve this objective, we compare Rapid with a radio-based (CSI-based) approach [31] and an audio-based identification system [8] since they all use gait information for personal identification.”
• Through-Wall Human Pose Estimation Using Radio Signals (2018, background): “Finally, some prior papers have explored human identification using wireless signals [49, 43, 18].”
• Gait recognition using wifi signals (2016, background): “built a system called WiWho that also uses WiFi CSI signals to recognize human gaits [34].”
• Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi (2019, background): “Due to complexity of human activity, existing approaches extract signal features, either statistical [14, 15, 23, 28, 30, 45, 49] or physical [6, 31, 34, 38, 39, 44, 51, 52] ones, and map them to discrete activities.”
• RF-based 3D skeletons (2018, background): “Advanced sensing technologies have enabled people tracking based on the RF signals that bounce off their bodies, even when they do not carry any wireless transmitters [2, 17, 35].”
• Wireless Sensing for Human Activity: A Survey (2020, background): “[68] propose to identify a person’s steps and walking gait for user identification leveraging the CSI amplitude features, but it requires the human subject to walk along a path with a distance of 1 meter parallel to the LoS path between the WiFi transmitter and receiver.”
• WiFi Sensing with Channel State Information (2019, background): “For example, CSI amplitude variations in the time domain have different patterns for different humans, activities, gestures, and so on, which can be used for human presence detection [3, 24, 67, 73, 75, 83, 112, 114, 121, 148, 149, 152], fall detection [32, 68, 92, 135, 137], motion detection [23, 2”
• Towards 3D human pose construction using wifi (2020, background): “…localization and tracking [5, 22, 25, 33, 39, 42, 48–50, 52, 53, 65], walking speed estimation [17, 43], breathing and heartbeats inference [6, 55, 58, 61], human identification [16, 43, 54], human or human movement detection [32, 34, 47, 66], and gesture recognition [27, 31, 35, 56, 62, 63].”
• Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi (2021, background): “On the human side, existing model-based works only tracks coarse human motion status, such as location [21], [28], velocity [17], [20], gait [30], [31] and figure [24], [32].”