WiFi-Based Cross-Domain Gesture Recognition via Modified Prototypical Networks

Zhang, Xie; Tang, Chengpei; Yin, Kang; Ni, Qingqian

doi:10.1109/jiot.2021.3114309

Cited by 33 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 59 2021 human activity recognition CNN Intel 5300 NIC few-shot learning Widar 37 2021 human identification, gesture recognition CNN-GRU Intel 5300 NIC supervised learning WiONE 60 2021 human identification CNN Intel 5300 NIC few-shot learning Ma et al. 61 2021 human activity recognition CNN, RNN, LSTM Intel 5300 NIC supervised learning THAT 62 2021 human activity recognition Transformers Intel 5300 NIC supervised learning WiGr 63 2021 gesture recognition CNN-LSTM Intel 5300 NIC supervised learning MCBAR 64 2021 human activity recognition CNN, GAN Atheros CSI Tool semi-supervised learning CAUTION 12 2022 human identification CNN Atheros CSI Tool few-shot learning CTS-AM 65 2022 human activity recognition CNN (attention) Intel 5300 NIC supervised learning WiGRUNT 66 2022 gesture recognition CNN (attention) Intel 5300 NIC supervised learning Zhuravchak et al. 67 2022 human activity recognition LSTM Nexmon CSI Tool supervised learning EfficientFi 39 …”

Section: Resultsmentioning

confidence: 99%

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Yang¹,

Chen²,

Zou³

et al. 2023

Patterns

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Yang¹,

Chen²,

Zou³

et al. 2023

Patterns

View full text Add to dashboard Cite

“…EI [7] adopted adversarial learning to remove the environment and subject specific information. WiGr [8] uses the similarity between the query samples and the class prototypes in the embedding space to perform the gesture classification.…”

Section: Cross-domain Extendable Gesture Recognition System Using Wif...mentioning

confidence: 99%

Cross-domain extendable gesture recognition system using WiFi signals

Yu-xi

2023

Preprint

View full text Add to dashboard Cite

This letter proposes a cross-domain WiFi-based gesture recognition system (WiCross) based on a dynamically weighted multi-label generative adversarial network. Most existing WiFi-based gesture recognition systems are user, orientation, and environment sensitive, which limits the application of WiFi sensing. Compared with the influence of users and environments on WiFi sensing systems, the influence of orientation on WiFi sensing systems is more difficult to remove. To alleviate the confusion caused by the orientation more effectively, we arrange the transmitting and receiving antennas according to the characteristics of the Fresnel region. We propose to dynamically weight different links according to users’ orientations and use a multi-label generative adversarial network to obtain domain-independent features. More importantly, WiCross can use domain-independent features to classify some unknown gestures without modifying any code or data set. Lightweight computing resource consumption allows WiCross to respond in real-time. The experimental results show that WiCross can achieve an in-domain recognition accuracy of 93.54% and a cross-domain recognition accuracy of 93.11%

show abstract

“…The advantages of CNNs for WiFi sensing consist of fewer training parameters and the preservation of the subcarrier and time dimension in CSI data. However, the disadvantage is that CNN has an insufficient receptive field due to the limited kernel size and thus fails to capture the [40] People Counting MLP Intel 5300 NIC Supervised learning EI [41] Human Activity Recognition CNN Intel 5300 NIC Transfer learning CrossSense [29] Human Identification,Gesture Recognition MLP Intel 5300 NIC Transfer Ensemble learning [42] Human Activity Recognition LSTM Intel 5300 NIC Supervised learning DeepSense [5] Human Activity Recognition CNN-LSTM Atheros CSI Tool Supervised learning WiADG [25] Gesture Recognition CNN Atheros CSI Tool Transfer learning WiSDAR [43] Human Activity Recognition CNN-LSTM Intel 5300 NIC Supervised learning WiVi [7] Human Activity Recognition CNN Atheros CSI Tool Supervised learning SiaNet [9] Gesture Recognition CNN-LSTM Atheros CSI Tool Few-Shot learning CSIGAN [44] Gesture Recognition CNN, GAN Atheros CSI Tool Semi-Supervised learning DeepMV [45] Human Activity Recognition CNN (Attention) Intel 5300 NIC Supervised learning WIHF [46] Gesture Recognition CNN-GRU Intel 5300 NIC Supervised learning DeepSeg [47] Human Activity Recognition CNN Intel 5300 NIC Supervised learning [48] Human Activity Recognition CNN-LSTM Intel 5300 NIC Supervised learning [35] Human Activity Recognition LSTM Nexmon CSI Tool Supervised learning [49] Human Activity Recognition CNN Nexmon CSI Tool Supervised learning [50] Human Activity Recognition CNN Intel 5300 NIC Few-Shot learning Widar [31] Human Identification, Gesture Recognition CNN-GRU Intel 5300 NIC Supervised learning WiONE [51] Human Identification CNN Intel 5300 NIC Few-Shot learning [52] Human Activity Recognition CNN, RNN, LSTM Intel 5300 NIC Supervised learning THAT [53] Human Activity Recognition Transformers Intel 5300 NIC Supervised learning WiGr [54] Gesture Recognition CNN-LSTM Intel 5300 NIC Supervised learning MCBAR [55] Human Activity Recognition CNN, GAN Atheros CSI Tool Semi-Supervised learning CAUTION [12] Human Identification CNN Atheros CSI Tool Few-Shot learning CTS-AM [56] Human Activity Recognition CNN (Attention) Intel 5300 NIC Supervised learning WiGRUNT…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Yang¹,

Chen²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we highlight the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/CHENXINYANsg/WiFi-CSI-Sensing-Benchmark.

show abstract

WiFi-Based Cross-Domain Gesture Recognition via Modified Prototypical Networks

Cited by 33 publications

References 37 publications

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Cross-domain extendable gesture recognition system using WiFi signals

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Contact Info

Product

Resources

About