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

Yang, Jianfei; Chen, Xinyan; Zou, Han; Lu, Chris Xiaoxuan; Wang, Dazhuo; Sun, Sumei; Xie, Lihua

doi:10.1016/j.patter.2023.100703

Cited by 28 publications

(12 citation statements)

References 119 publications

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“…In this paper, comparative experiments are conducted with DSC-SGRU using LeNet, MLP, RNN, LSTM, GRU, CNN-GRU [27], ResNet18, and ResNet50 etc. as the benchmark models to validate the superiority of the proposed model in this paper.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Research on WiFi human gesture recognition system based on hybrid model of DSC-SGRU

He,

Zhao,

et al. 2024

Preprint

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Wi-Fi wireless sensing technology has become a research hotspot in the field of perception, which can realize the intelligent perception of human activities and the surrounding environment. Aiming at the problem that the existing wireless sensing models have a large number of parameters, which makes it difficult to sense in real-time in scenarios with limited computing power, such as mobile devices, a lightweight feature extraction module based on Depthwise Separable Convolution (DSC) mixed with Stacked Gate Recur-rent Unit (SGRU) is proposed as a recognition model. The model first captures the spatial features of human gestures using DSC and keeps the temporal characteristics of the features unchanged, and then learns the spatio-temporal characteristics of the gestures using the SGRU network. The performance of the model is validated using the open source dataset Widar. The results show that the proposed DSC-SGRU model has only 236.891 K parameters with an accuracy of 77.6\%. Compared with existing gesture recognition models, DSC-SGRU greatly reduces the number of parameters of the model with approximate performance.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Research on WiFi human gesture recognition system based on hybrid model of DSC-SGRU

He,

Zhao,

et al. 2024

Preprint

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“…The temporal or static features are then used to train supervised classifiers. State-of-the-art Wi-Fi sensing systems utilise Deep Neural Networks (DNN) [16,32] or Convolutional Neural Networks (CNN) [33,34] to learn non-obvious features; however, instance-based classifiers such as Support Vector Machines (SVM) or K-Nearest-Neighbour (KNN) offer comparable accuracy at much better computational complexity [19,30]. Transfer Learning (TL) has also been applied, as it allows trained networks to be adaptable to deployment in new environments [35][36][37].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Sharma,

Li,

Mishra

et al. 2024

Energies

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Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

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“…UT-HAR. This dataset, containing 4GB WiFi-CSI amplitude data, was collected at the University of Toronto and Stanford [80], [83]. It recorded CSI amplitude information for six users with six activities (lying, falling, walking, running, sitting, and standing).…”

Section: Wifi-based Activity Recognitionmentioning

confidence: 99%

Review of the Open Data Sets for Contactless Sensing

Liang,

Chen,

et al. 2024

IEEE Internet Things J.

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Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

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

Cited by 28 publications

References 119 publications

Research on WiFi human gesture recognition system based on hybrid model of DSC-SGRU

Research on WiFi human gesture recognition system based on hybrid model of DSC-SGRU

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Review of the Open Data Sets for Contactless Sensing

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