Wi2Vi: Generating Video Frames From WiFi CSI Samples

Kefayati, Mohammad Hadi; Pourahmadi, Vahid; Aghaeinia, Hassan

doi:10.1109/jsen.2020.2996078

Cited by 20 publications

(18 citation statements)

References 21 publications

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“…Thus, it is possible to envision a system that uses both modalities to address these limitations. One such approach is used in [42], were the authors proposed a system that uses both CSI and a video camera to generate a video stream. The overall aim of this system is to be able to generate video frames from CSI at points in time at which the video camera fails or is unavailable, for instance during an attack or while the camera line of sight is blocked by an object.…”

Section: Data Multimodalitymentioning

confidence: 99%

A Survey of Approaches to Unobtrusive Sensing of Humans

et al. 2022

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The increasing amount of human-related and/or human-originated data in current systems is both an opportunity and a challenge. Nevertheless, despite relying on the processing of large amounts of data, most of the so-called smart systems that we have nowadays merely consider humans as sources of data, not as system beneficiaries or even active “components.” For truly smart systems, we need to create systems that are able to understand human actions and emotions, and take them into account when deciding on the system behavior. Naturally, in order to achieve this, we first have to empower systems with human sensing capabilities, possibly in ways that are as inconspicuous as possible. In this context, in this article we survey existing approaches to unobtrusive monitorization of human beings, namely, of their activity, vital signs, and emotional states. After setting a taxonomy for human sensing, we proceed to present and analyze existing solutions for unobtrusive sensing. Subsequently, we identify and discuss open issues and challenges in this area. Although there are surveys that address some of the concerned fields of research, such as healthcare, human monitorization, or even the use-specific techniques like channel state information or image recognition, as far as we know this is the first comprehensive survey on unobtrusive sensing of human beings.

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Section: Data Multimodalitymentioning

confidence: 99%

A Survey of Approaches to Unobtrusive Sensing of Humans

et al. 2022

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“…Compared with signal data, image data has more stable feature information [37], and the texture features of the image data portray the recurring local patterns in the image with their alignment rules, with good noise immunity and rotation invariance [38]. In order to make the recognition of action more accurate, in this paper, after segmenting the continuous activity, the segmented action signal is processed as a signal image and then its texture features are extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices

Hao

Zhang

Dang

et al. 2021

Sensors

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With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

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“…To the best of our knowledge, this is the first time CSI-to-image conversion has been achieved using GANs. From the perspective of CSIto-image conversion without GANs, a few related studies have been conducted [1], [2]. Wi2Vi [1] uses the video recovered by CSI when a security camera is disabled owing to power failure, malfunction, or attack.…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of CSIto-image conversion without GANs, a few related studies have been conducted [1], [2]. Wi2Vi [1] uses the video recovered by CSI when a security camera is disabled owing to power failure, malfunction, or attack. Under normal conditions, Wi2Vi extracts the background image from the camera image, detects a person using the difference between the background image and the image, and learns by associating it with the CSI.…”

Section: Introductionmentioning

confidence: 99%

“…[2] has successfully generated pose images generated from skeletons from CSI by learning the relationship between the skeleton model of human posture and CSI. [1], [2] are application-specific approaches, using application-specific information such as background images and skeleton models. By contrast, the present study focuses on a general-purpose CSI-to-image conversion method using GANs.…”

Section: Introductionmentioning

confidence: 99%

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CSI2Image: Image Reconstruction From Channel State Information Using Generative Adversarial Networks

et al. 2021

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This study aims to determine the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective because, at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although we have still not obtained a complete answer, a step is taken toward it herein. To achieve this, CSI2Image, a novel channel state information (CSI)-to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking whether the reconstructed image captures the desired physical space information. We demonstrate three types of learning methods: generator-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, we propose a quantitative evaluation methodology using an image-based object detection system. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that CSI2Image successfully reconstructs images. The results demonstrate that generator-only learning is sufficient for simple wireless sensing problems; however, in complex wireless sensing problems, GANs are essential for reconstructing generalized images with more accurate physical space information. INDEX TERMS wireless sensing, channel state information, deep learning, generative adversarial networks, image reconstruction

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Wi2Vi: Generating Video Frames From WiFi CSI Samples

Cited by 20 publications

References 21 publications

A Survey of Approaches to Unobtrusive Sensing of Humans

A Survey of Approaches to Unobtrusive Sensing of Humans

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices

CSI2Image: Image Reconstruction From Channel State Information Using Generative Adversarial Networks

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