WiFall: Device-free fall detection by wireless networks

By Yuxi Wang, Kaishun Wu, Lionel Ming-shuan Ni. IEEE Conference on Computer Communications, 2017

973 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/b7b9fb1f9f418de528cf39059aea8e33d55a1c66

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. Yan Wang
4. Yingying Chen
5. Hongbo Liu
6. Jian Liu
7. Daqing Zhang
8. Yasha Wang

In Computer Vision and Pattern Recognition

1. Jun Liu
2. Qiuhong Ke
3. Hossein Rahmani
4. Zehua Sun
5. Wen Wang
6. Alex X. Liu
7. Jindong Wang
8. Chaohui Yu

In Electrical and Electronic Engineering

1. Alex X. Liu
2. Yan Wang
3. Yingying Chen
4. Hongbo Liu
5. Jian Liu
6. Wen Wang
7. Chen Wang
8. Tao Gu

Citations in the corpus

• Human respiration detection with commodity wifi devices: do user location and body orientation matter? (2016, background): “While early work focused mainly on environment monitoring and daily-activity recognition, the past few years have witnessed a surge of interest in health threat monitoring to ensure timely intervention and safety for elders, such as fall detection [12][43][38] and vital sign monitoring [13][3].”
• Wireless Sensing for Human Activity: A Survey (2020, background): “WiFall [24] is the first CSIbased fall detection system.”
• RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices (2017, background): “If a person presents in the environment, additional signal paths are introduced by the scattering of human body.”
• WiWho: WiFi-Based Person Identification in Smart Spaces (2016, background): “…works have shown that we can leverage the wireless signals to detect human motion and activities [1], [2], [6]–[8], recognize gestures [3], [9] and other types of sensing (e.g. hearing people talk [10], counting crowd [11], estimate queue length [12], detecting fall [13] and monitoring sleep [14]).”
• FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare (2019, background): “Many endeavors have been made [Wang et al. , 2014; Albinali et al. , 2010; Wang et al. , 2017b].”
• A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “Some examples of activity detection applications include: fall detection [34], [67], [68], fitness tracking [69],”
• Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Besides, WiFi signals have also be used for fall detection [344].”
• Keystroke Recognition Using WiFi Signals (2015, methodology): “CSI Based: CSI values obtained from COTS WiFI network interface cards (NICs) (such as Intel 5300 and Ath-eros 9390) have been recently proposed for activity recognition [6–10 , 13] and localization [ 14– 16 ].”
• WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019, methodology): “The CSI describes the channel properties of a wireless communication link [2].”
• CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments (2016, background): “ing [14]–[16], whole-home activity and gesture recognition [17]–[20], robust detection [21]–[24], cyber-physical systems security [25], [26], traffic congestion and speed estimation [27], [28], reducing deployment overhead [29]–[31], understanding the underlying system [32]–[36], human counting [9],”
• Precise Power Delay Profiling with Commodity Wi-Fi (2015, methodology): “Such a resolution imposes inevitable uncertainty in mobility detection [2], [3], [4], [5], [17], gesture recognition [18], or localization [1], [6].”
• Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, methodology): “Researchers have developed CSI based applications, including fall detection [11], presence detection [38], indoor localization [21], [34], and human crowd counting [31].”
• Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, methodology): “Recently CSI has been used for human activity recognition [10,26,27,30,35] as well as indoor localization [19,32].”