Person-in-WiFi: Fine-Grained Person Perception Using WiFi

Wang, Fei; Zhou, Sanping; Panev, Stanislav; Han, Jinsong; Huang, Dong

doi:10.1109/iccv.2019.00555

Cited by 140 publications

(74 citation statements)

References 59 publications

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“…These algorithms have utilized generator models paired with appropriate targets and loss functions to solve many image-to-image translation problems like image denoising [10,71], image superresolution [15,28,32], image colorization [27,31], and real-to-art image translations [26,27,76]. While there has been some work that utilizes rf-based techniques that utilize machine learning to solve through-wall human pose estimation [58,73,74], our paper is the first to present general principles for using ideas of imageto-image translation for the localization problem. Specifically, the data distribution of the indoor WiFi localization data is different from all image translation work, and RF-based pose estimation data.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based wireless localization for indoor navigation

Ayyalasomayajula

Arun

et al. 2020

Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

122

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Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms stateof-the-art methods in Wi-Fi-based localization by 80% (median & 90 th percentile) across the two different spaces. CCS CONCEPTS • Networks → Location based services; • Computing methodologies → Robotic planning; Supervised learning; • Information systems → Sensor networks.

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Section: Related Workmentioning

confidence: 99%

Deep learning based wireless localization for indoor navigation

Ayyalasomayajula

Arun

et al. 2020

Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

122

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“…In the case of moving objects, the fluctuation of multipath fading is different depending on human activity and/or the number of moving people. This characteristic is used for human movement detection, human behavior classification, human or vehicle identification, human counting in a room, and human counting in a passageway [3,5,[11][12][13][14][15][16][17][18][19], among others. In these applications, the main classification algorithms for estimation, such as SVMs, recurrent neural networks (RNNs), and long short-term memory (LSTM), are used.…”

Section: Related Studiesmentioning

confidence: 99%

“…For example, three PIR sensors are used to detect people's movement [4]. On the other hand, LiDAR sensors are a crucial component for autonomous cars, but LiDAR sensors are not cheaper than Wi-Fi devices [5]. We expect Wi-Fi devices with CSI collection to be a cost-effective solution.…”

Section: Introductionmentioning

confidence: 99%

“…The CSI consists of frequency and space information, expresses the effect of multipath propagation, and corresponds to the space state between transmitting and receiving antennas. To date, CSI has been used for various studies such as human movement detection, human behavior classification, location estimation, object identification, human counting in a room, and human counting in a passageway [3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

Ogawa

Munetomo

2020

Sensors

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This paper proposes a channel state information (CSI)-based prediction method of a human flow that includes activity. The objective of the paper is to predict a human flow in an outdoor road. This human flow prediction is useful for the prediction of the number of passing people and their activity without privacy issues as a result of the absence of any camera systems. In this paper, we assume seven types of activities: one, two, and three people walking; one, two, and three people running; and one person cycling. Since the CSI can effectively express the effect of multipath fading in wireless signals, we expected the CSI to predict the various activities. In our proposed method, the amplitude and phase components are extracted from the measured CSI. The feature values for machine learning are the mean and variance of the maximum eigenvalue derived from the auto-correlation matrix and variance–covariance matrix composed of the amplitude or phase components and the passing time of flow. Using these feature values, we evaluated the prediction accuracy by leave-one-out cross-validation with a linear support vector machine (SVM). As a result, the proposed method achieved the maximum prediction accuracy of 100% for each direction and 99.5% for two directions.

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“…This article presents some typical deep neural network models used in CSI-based behavior recognition applications, including Autoencoder, Convolutional Neural Network (CNN), LSTM, RNN, Residual Neural Network (ResNet), and Restricted Boltzmann Machine (RBM). In addition, we also detailed introduce these applications, such as daily behavior recognition [138]- [142], falling detection [143], [144], syncope detection [145], hand gesture recognition [8], [146]- [149], sign language recognition [150], gait and walking direction recognition [151], [152], human presence detection [153]- [155], crowd counting [156]- [159], user authentication [160]- [163], and respiration monitoring [22].…”

Section: Deep Learning-based Behavior Recognitionmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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Person-in-WiFi: Fine-Grained Person Perception Using WiFi

Cited by 140 publications

References 59 publications

Deep learning based wireless localization for indoor navigation

Deep learning based wireless localization for indoor navigation

Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

A Survey on Human Behavior Recognition Using Channel State Information

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