Understanding and Modeling of WiFi Signal Based Human Activity Recognition

By Wen Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, Sanglu Lu. ACM/IEEE International Conference on Mobile Computing and Networking, 2015

1,064 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/539b28563ecd9fa14be7134bc17ec942958d00db

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

In Computer Vision and Pattern Recognition

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

In Electrical and Electronic Engineering

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

Citations in the corpus

• Towards 3D human pose construction using wifi (2020, methodology): “The Doppler frequency shift (DFS) fD (t) is defined as the change in the length of the signal propagation path d(t) [33, 44]:”
• Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, methodology): “Many innovative applications have been designed, including activity and gesture recognition [20, 30, 34], respiration detection [31] and direction estimation [22, 36], etc.”
• 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): “…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], location-aware activities [Wang et al. 2014], and daily activities [Wang et al. 2015].”
• Gait recognition using wifi signals (2016, methodology): “With the success of WiFi industry, commercially available WiFi devices can not only achieve high speed and low cost, but can also measure small changes in WiFi signals and thus sense the changes in their surrounding environments caused by moving objects such as humans [30, 31].”
• Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “This paper was presented at the Proceedings of the ACM MobiCom, 2015 [27].”
• WiFall: Device-free fall detection by wireless networks (2017, neutral): “In [22], the”
• Device-free gesture tracking using acoustic signals (2016, methodology): “Note that it is important to use both the I and Q components because the movement direction information is lost when we only use a single component or the magnitude [23].”
• Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “Because a mobile path signal goes through a round trip at the reflection surface, the path length change rate is approximately twice the radial velocity of the reflection surface [39].”
• Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, methodology): “To eliminate the unknown shift, CARM [18] calculates the CSI power (i.”
• Rapid (2017, methodology): “1 INTRODUCTION Device-free human sensing has recently attracted many interests for its applications in indoor navigation [29], activity recognition [27], and entertainment.”
• mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “Wi-Fi RSSI/CSI signature based passive localization systems [1, 5, 38, 39, 41, 45, 52] usually rely on high-density deployments which are not realistic for largescale deployments.”
• 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].”
• Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi (2019, methodology): “, power profiles of Doppler frequency shifts [44]) from Wi-Fi signals and map them to human gestures.”
• Human respiration detection with commodity wifi devices: do user location and body orientation matter? (2016, background): “We divide all these paths into static and dynamic ones, then the receiving signal H( f , t) can be denoted as a phase vector with the following equation [35][39]:”
• WiFi Sensing with Channel State Information (2019, background): “CARM [98, 99] NR: CFO; ST: DWT; SE: Thresholding, PCA, Feature Extraction L: HMM Activity Recognition Recognition Accuracy: >96% (8 activities)”
• LiFS: low human-effort, device-free localization with fine-grained subcarrier information (2016, methodology): “CSI has also been employed in gesture and activity recognition systems [36, 35, 28, 6, 19].”
• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, background): “Line-of-sight (LoS) paths are regarded as static paths most of the time [34, 87, 90, 110].”
• WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019, background): “Under the principle that human actions between WiFi transmitters and receivers will influence WiFi signal characteristics, WiFi-based passive human activity recognition can be feasible [9].”