Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi

Zheng, Yue; Zhang, Yi; Qian, Kun; Zhang, Guidong; Liu, Yunhao; Wu, Chenshu; Yang, Zheng

doi:10.1145/3307334.3326081

Cited by 364 publications

(242 citation statements)

References 43 publications

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“…(1) Widar3.0 data: Zheng et al [ 14 ] collected dozens of different gestures at 5.825 GHz using the Linux CSI Tool. Due to the excessive number of gesture samples, we extracted push, sweep, clap, and slide actions from 14 users.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The dataset samples come from three experimental environments: classroom, auditorium, and office. Each environment is divided into five gesture collection locations, and the user performs gestures in five directions at each location (details in [ 14 ]). In this dataset, each gesture uses one transmitting device and six receiving devices to collect.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Therefore, these small sample problems have affected the popularity and application of gesture recognition algorithms. Moreover, the experimental results of most existed systems depend on specific experimental environments and fixed users, which will significantly reduce the performance of cross-domain recognition [ 14 , 15 ]. In this paper, we refer to this phenomenon of not adapting to new scenes as environmental dependence.…”

Section: Introductionmentioning

confidence: 99%

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WiGAN: A WiFi Based Gesture Recognition System with GANs

Jiang

2020

Sensors

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In recent years, a series of research experiments have been conducted on WiFi-based gesture recognition. However, current recognition systems are still facing the challenge of small samples and environmental dependence. To deal with the problem of performance degradation caused by these factors, we propose a WiFi-based gesture recognition system, WiGAN, which uses Generative Adversarial Network (GAN) to extract and generate gesture features. With GAN, WiGAN expands the data capacity to reduce time cost and increase sample diversity. The proposed system extracts and fuses multiple convolutional layer feature maps as gesture features before gesture recognition. After fusing features, Support Vector Machine (SVM) is exploited for human activity classification because of its accuracy and convenience. The key insight of WiGAN is to generate samples and merge multi-grained feature maps in our designed GAN, which not only enhances the data but also allows the neural network to select different grained features for gesture recognition. According to the result of experiments conducted on two existing datasets, the average recognition accuracy of WiGAN reaches 98% and 95.6%, respectively, outperforming the existing system. Moreover, the recognition accuracy under different experimental environments and different users shows the robustness of WiGAN.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WiGAN: A WiFi Based Gesture Recognition System with GANs

Jiang

2020

Sensors

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“…The novelty of WiMU lies in generating virtual samples for different possible combinations of performed gestures and comparison of both for the recognition and classification task. Widar-3.0 [31] is a WiFi sensing system for gesture recognition, which is based on the kinetic characteristic of various gestures. The gesture characteristics are derived from their velocity profiles and makes the system agnostic to different domains.…”

Section: Introductionmentioning

confidence: 99%

“…WiFi sensing-based monitoring systems only require a WiFi router or access point and one or more WiFi enabled devices. WiFi sensing with CSI measurements have been used in various applications, such as human presence/localization [20][21][22], activity recognition [23][24][25], fall classification and detection [26][27][28][29], gesture recognition [30][31][32], and user identification [33][34][35].Recent work has leveraged WiFi sensing for human presence detection and localization. Qian et al[20] used a WiFi-based Multiple Inputs and Multiple Outputs (MIMO) system and CSI measurements to detect presence of humans with dynamic movement speeds utilizing a Support Vector Machine (SVM), resulting in a true positive rate greater than 93%.…”

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confidence: 99%

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

et al. 2019

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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.China [3]. Every 1 in 37 people in the United Kingdom are diagnosed with PD at some stage of their life [1]. PD is caused by the neuronal loss and damage in the motor area of the brain, particularly the centers of the limbic, visceromotor, and somatomotor systems [4]. Most PD patients experience difficulty in walking, which indicates an onset of gait, balance, and other disabilities [5]. Half of the PD patients experience Freezing of Gate (FOG) symptoms, which represent an episodic absence of forward feet movement, despite of the intention for forward progress [6]. The frequency as well as the duration of FOG episodes increases with the progression of PD. FOG and falls are both interconnected phenomena, and FOG is known to hinder daily activities and increase fall risk [7], resulting in severe consequences for older adults.Conventional clinical methods to validate FOG occurrences and their severity are based on self-report questionnaires [8] and doctors' reports based on direct observation. Due to the impact of FOG on population in general and falls in older adults in particular, there is a high demand for automating the process of detection and recognition of FOG events. Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detectio...

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