Keystroke Recognition Using WiFi Signals

By Kamran Ali, Alex X. Liu, Wen Wang, Muhammad Shahzad. ACM/IEEE International Conference on Mobile Computing and Networking, 2015

569 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0ca898d1c00c9c2dab048d4a687a57744694f24f

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

In Electrical and Electronic Engineering

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

Citations in the corpus

• 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.”
• WiFi Sensing with Channel State Information (2019, background): “WiKey [4, 5] NR: LPF, PCA; ST: DWT L: kNN+DTW Keystroke Detection & Recognition Detection: 97.”
• Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “It is shown that CSI is also able to detect and recognize small movements such as lip movements [26], keystrokes [7], and heartbeats [36].”
• Towards Environment Independent Device Free Human Activity Recognition (2018, methodology): “11n CSI Tool [16], recently a lot of research work have been conducted to utilize CSI for the task of human activity recognition [3, 8, 14, 38, 47, 48, 50, 52, 55] or gesture recognition [17, 24, 37, 42, 45, 46, 59].”
• Deep Learning for Sensor-based Human Activity Recognition (2020, unknown): “ognition from both amplitude and phase aspects [171]. In addition to coarse activities like walking and jogging, CSI can also be used to recognize small movements like lip movements [152], keystrokes [5], and heartbeats [156]. Radio-frequency identification (RFID).RFID uses electromagnetic fields to”
• WiFall: Device-free fall detection by wireless networks (2017, unknown): “keystroke tracking [24] and in-air drawing [25].”
• Wireless Sensing for Human Activity: A Survey (2020, background): “Existing work has demonstrated that WiFi signals can be used to snoop keystrokes [39] and infer mobile phone password [138].”
• A Survey on Behavior Recognition Using WiFi Channel State Information (2017, methodology): “cArm Recently, other techniques have been proposed such as WiHear [3], CARM [9[, and the one proposed in [14].”
• Emotion recognition using wireless signals (2016, background): “Past work leverages this phenomenon to sense human motion: it transmits an RF signal and analyzes its reflections to track user locations [5], gestures [6, 50, 56, 10, 61, 3], activities [59, 60], and vital signs [7, 19, 20].”
• A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “—Extending location contexts: While device-free localization systems are primarily designed to localize humans without requiring the targets to carry any devices, such a device-free sensing modality has extended from detecting locations to richer contexts such as keystrokes [Ali et al. 2015], locati”