Augmenting User Identification with WiFi Based Gesture Recognition

@article{Shahzad2018AugmentingUI,
  title={Augmenting User Identification with WiFi Based Gesture Recognition},
  author={Muhammad Shahzad and Shaohu Zhang},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  year={2018},
  volume={2},
  pages={1 - 27}
}
  • Muhammad Shahzad, Shaohu Zhang
  • Published 18 September 2018
  • Computer Science
  • Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Over the last few years, researchers have proposed several WiFi based gesture recognition systems that can recognize predefined gestures performed by users at runtime. As most environments are inhabited by multiple users, the true potential of WiFi based gesture recognition can be unleashed only when each user can independently define the actions that the system should take when the user performs a certain predefined gesture. To enable this, a gesture recognition system should not only be able… 
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45 Citations
Highly Influential Citations
8
Background Citations
13
Methods Citations
12
Results Citations
1

45 Citations

Citation Type

Citation Type

WiHF: Enable User Identified Gesture Recognition with WiFi
TLDR
The basic idea of WiHF is to derive a cross-domain motion change pattern of arm gestures from WiFi signals, rendering both unique gesture characteristics and the personalized user performing styles, and an efficient method based on the seam carving algorithm is developed.
WiHF: Gesture and User Recognition With WiFi
TLDR
The basic idea of WiHF is to derive a domain-independent motion change pattern of arm gestures from WiFi signals, rendering the unique gesture characteristics and the personalized user performing styles, and an efficient method based on the seam carving algorithm is developed.
Towards Robust Gesture Recognition by Characterizing the Sensing Quality of WiFi Signals
TLDR
A quality-oriented signal processing framework is proposed that maximizes the contribution of the high-quality signal segments and minimizes the impact of low- quality signal segments to improve the performance of gesture recognition applications.
WiGrus: A Wifi-Based Gesture Recognition System Using Software-Defined Radio
TLDR
A WiFi-based gesture recognition system, namely WiGrus, which solves the problems of user privacy and energy consumption compared with the approaches using wearable sensors and depth cameras and is robust to the environment changes.
A Novel Gesture Recognition System Based on CSI Extracted from a Smartphone with Nexmon Firmware
TLDR
A novel gesture recognition system based on a smartphone using nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode and a new improved DTW algorithm to classify and recognize gestures.
Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing
TLDR
WiCG, an innovative and novel WiFi sensing approach for capturing and providing feedback on stroke order, which considers Chinese calligraphy as a use case as the resulting gestures are highly complex, and their assessment depends on the correct stroke order.
Deep transfer learning for gesture recognition with WiFi signals
TLDR
A new approach that uses deep transfer learning techniques to recognize gestures based on the channel state information (CSI) extracted from WiFi signals and demonstrates that the proposed method outperformed other state-of-the-art WiFi-based gesture recognition methods.
Towards Position-Independent Sensing for Gesture Recognition with Wi-Fi
TLDR
This paper develops a new position-independent sensing strategy and uses gesture recognition as an application example to demonstrate its effectiveness, and builds a prototype WiFi gesture recognition system, i.e., WiGesture to validate the effectiveness of the proposed strategy.
Gesture Feature Extraction and Recognition Based on Image Processing
TLDR
A convolutional neural network was constructed for gesture recognition, and a gesture recognition model was developed based on residual network that was proved to be valid through experiments and provides a reference for the application of CNN in the recognition of various postures or shapes.
Device-Free Human Identification Using Behavior Signatures in WiFi Sensing
TLDR
This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features.
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References

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