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5bdc319c17c44a1f58a09e79

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

620e41105aee126c0fb6b1c5

Over the past decade, WiFi CSI-based device-free sensing technology has shown great potential in smart homes, assisted living, and many other applications. While model-based device-free sensing approaches analyze and recognize human behaviors by constructing mathematical relationships among WiFi devices, environment, human position/posture, and received channel state information, they have attracted great attention because of the interpretable physical meaning and the ability to guide the WiFi-based sensing system design. In this paper, we retrospect two general-purpose sensing models, i.e., the Fresnel zone model and CSI-ratio model, and demonstrate how these two models are leveraged to extract insightful properties and support a variety of device-free sensing applications.

5bdc319c17c44a1f58a09e79

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

61debd3e5244ab9dcbe97597

The importance of vital sign detection is self-evident in the mobile health domain. Recent work has shown that one can use RF or WiFi signals for respiration and heartbeat detection in a non-contact manner and thus improve its usability compared to the wearable-based solution. However, the existing approaches either require an ultra-wideband radio which is not commercially available or do not perform well in practical working environments. The millimeter-wave (mmWave) radio is a promising solution for fine-grained heartbeat and respiration sensing applications because of its directionality and sensitivity. However, we find traditional mmWave algorithms suffer from background noise in practical scenes. In this article, we address this issue by designing a robust algorithm for heart rate detection based on time-domain and frequency-domain information. We implement a phase-modulated system on the software-defined radio platform and evaluate the algorithm performance. Also, we evaluate the impact of several practical factors, such as detecting distance, aiming point, depression angle, human orientation and beam width on the proposed heart rate algorithm. Finally, we explore the feasibility of mmWave on vital sign detection with strong background interference and in scenes of real life where the antennas are hanging on the ceiling. The results show that the mean estimation error of respiration and heartbeats are 0.487 Bpm and 2.386 bpm.

5bdc319c17c44a1f58a09e79

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

62d16b8a5aee126c0fd126e7

With the rising of AR/VR technology and miniaturization of mobile devices, gesture recognition is becoming increasingly popular in the research area of human-computer interaction. Some pioneer ultrasound-based gesture recognition systems have been proposed. However, they mostly rely on low-resolution Doppler Effect, with the focus on whole hand motion and fail to deal with minor finger motions. This paper is to present UltraGesture, an ultrasonic finger motion perception and recognition system based on Channel Impulse Response (CIR). CIR measurements can provide with 7 mm resolution, which is sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into image, and builds a Convolutional Neural Network model to classify these images into different categories corresponding to distinct gestures. Furthermore, we use a sliding-window based method to improve accuracy and reduce response latency. UltraGesture can run on the already existed commercial speakers and microphones on most mobile devices without any hardware modification. Our results demonstrate that UltraGesture can achieve an average accuracy ofgreater than 99 percent for 12 gestures including finger click and rotation.

5bdc319c17c44a1f58a09e79

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

628d1f095aee126c0f3f457a

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing-enabled gesture recognition due to its inherent merits like remote sensing, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “where to look” and “when to look.” To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi channel state information via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a deep residual network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

5bdc319c17c44a1f58a09e79

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

6373cedf90e50fcafd2209f0

Respiration, a vital basis for life, is a key indicator of health status for the human being. Recently, with contact-based devices, some breathing signal detection methods have been proposed, which can achieve high accuracy and signal-to-noise ratio performance. However, these methods require users to be contacted with the devices, leading to a series of problems, such as hindering the movement of users. Therefore, there is an urgent need to call for a contactless solution for respiration detection. With the popularity of indoor WiFi devices, respiration detection with WiFi sensors has drawn a lot of attention. Nevertheless, the multipath effects, which commonly exist in indoor environments, have serious impacts on the propagation of wireless signals, leading to signal attenuation and poor signal quality. Moreover, although the channel state information (CSI) can be readily collected from commercial off-the-shelf (COTS) WiFi devices, the received CSI is distorted due to various offsets introduced during the propagation of the wireless signals and hardware imperfections. In this paper, we try to resolve the challenges mentioned above and propose a device-free respiration detection system, ResFi, utilizing the CSI data from COTS WiFi devices. The final evaluation shows an accuracy of 96.05% for human respiration detection, which is up to 15% higher than that of the traditional machine-learning methods.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5bdc319c17c44a1f58a09e85

Device-free sensing using ubiquitous Wi-Fi signals has recently attracted lots of attention. Among the sensed information, two important basic contexts are (i) whether a target is still or not and (ii) where the target is located. Continuous monitoring of these contexts provides us with rich datasets to obtain important high-level semantics of the target such as living habits, physical conditions and emotions. However, even to obtain these two basic contexts, offline training and calibration are needed in traditional methods, limiting the real-life adoption of the proposed sensing systems. In this paper, using the commodity Wi-Fi infrastructure, we propose a training-free human vitality sensing platform, WiVit. It could capture these two contexts together with the target's movements speed information in real-time without any human effort in offline training or calibration. Based on our extensive experiments in three typical indoor environments, the precision of activity detection is higher than 98% and the area detection accuracy is close to 100%. Moreover, we implement a short-term activity recognition system on our platform to recognize 4 types of actions, and we can reach an average accuracy of 94.2%. We also take a feasibility study of monitoring long-term activities of daily living to show our platform's potential applications in practice.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5c8dd9824895d9cbc6a7fb58

In this paper, we are interested in estimating the angle of arrival (AoA) of all the signal paths arriving at a receiver array using only the corresponding received signal magnitude measurements (or, equivalently, the received power measurements). Typical AoA estimation techniques require phase information, which is not available in some WiFi/Bluetooth receivers, and is further challenging to properly measure in a synthetic antenna array due to synchronization issues. In this paper, we then show that AoA estimation is possible with only the received signal magnitude measurements. More specifically, we first propose a framework, based on the spatial correlation of the received signal magnitude, to estimate the AoA of signal paths from fixed signal sources (both active transmitters and passive objects). Next, we extend our AoA estimation framework to a dual setting, and further utilize a particle filter, to show how a moving target (both active transmitters and passive robots/humans) can be tracked, based on only the received signal magnitude measurements of a small number of fixed receivers. We extensively validate our proposed framework with several experiments (total of 22), in both closed and open areas. More specifically, we first utilize a robot to emulate an antenna array, and estimate the AoA of active transmitters, as well as passive objects using only the received WiFi signal magnitude measurements. We next validate our tracking framework by using only three off-the-shelf WiFi devices as receivers, to track an active transmitter, a passive robot that writes the letters of IPSN on its path, and a walking human. Overall, our results show that AoA can be estimated, with a high accuracy, with only the received signal magnitude measurements, and can be utilized for high quality angular localization and tracking.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5d39832c3a55acc33f37bbba

The various offsets existed on the commodity WiFi devices greatly limit the use of ubiquitous WiFi signals for indoor applications. In this paper, we focus on the estimation and compensation of the residual carrier frequency offset (CFO) for the commodity WiFi devices. Specifically, we introduce a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO. We propose a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI. Then, we formulate the residual CFO estimation as a spectrum estimation problem and propose to utilize the MUSIC algorithm to estimate the residual CFO. Real experiments are conducted to evaluate the performance of the proposed method. The experimental results show that the residual CFO is time-varying, and compared with existing methods, the proposed method can better estimate and compensate the residual CFO, and thus achieve better results.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5d3ad05d3a55ac3dad8b93c1

Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~23 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what can be instantaneously harvested. We provide experimental validation, using both careful measurement studies as well as a controlled study with human participants, to show the viability of a custom-built WiWear-based wearable device, at least in office environments.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5d79a7023a55ac5b6503592b

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array orientation, which hinders practical large-scale deployment. In this paper, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be significantly reduced by simply replacing the ordinary linear antenna layout of the AP with the non-linear layout. In particular, we first present an autonomous self-calibrating method to significantly facilitate site survey for deploying CSI localization systems. Then we propose a systematical evaluation mechanism to show the fundamental reason why linear antenna layout usually leads to serious errors and why non-linear antenna layout is better off. Finally, we build a testbed with COTS devices and conduct comprehensive experiments. Results show that triangular antenna layout can achieve 80% angle of arrival (AoA) measurement error within 9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">◦</sup> for any direction in contrast to 16 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">◦</sup> based on linear antenna layout. Moreover, we can realize promising localization accuracy as previous works even without labor-intensive site survey, where 80% localization error is within 0.60m.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5e73435793d709897c7511f4

The popularity of Internet-of-Things (IoT) has provided us with unprecedented opportunities to enable a variety of emerging services in a smart home environment. Among those services, sensing the liquid level in a container is critical to building many smart home and mobile healthcare applications that improve the quality of life. This paper presents LiquidSense, a liquid level sensing system that is low-cost, high accuracy, widely applicable to different daily liquids and containers, and can be easily integrated with existing smart home networks. LiquidSense uses existing home WiFi network and a low-cost transducer that attached to the container to sense the resonance of the container for liquid level detection. In particular, our system mounts a low-cost transducer on the surface of the container and emits a well-designed chirp signal to make the container resonant, which introduces subtle changes to the home WiFi signals. By analyzing the subtle phase changes of the WiFi signals, LiquidSense extracts the resonance frequency as a feature for liquid level detection. Our system constructs prediction models for both continuous and discrete predictions using curve fitting and SVM respectively. We evaluate LiquidSense in home environments with containers of three different materials and six types of liquids. Results show that LiquidSense achieves an overall accuracy of 97% for continuous prediction and an overall F-score of 0.968 for discrete predication. Results also show that our system has a large coverage in a home environment and works well under non-line-of-sight (NLOS) scenarios.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5ee9f10a9fced0a24b0a1bab

This paper presents WiPolar, an approach that simultaneously tracks multiple people using commodity WiFi devices. While two recent papers have also demonstrated multi-person tracking using commodity devices, they either require the people to continuously keep moving without stopping, and/or require the number of people to be input manually, and/or keep the WiFi devices from performing their primary function of data communication. Motivated by the increasing availability of polarized antennas on modern WiFi devices, WiPolar leverages signal polarization to perform accurate multi-person tracking using commodity devices while addressing the three limitations of prior work mentioned above. The key insight that WiPolar is based on is that different people expose different instantaneous horizontal and vertical radar cross-sections to WiFi transmitters due to differences in their physiques and orientations with respect to the transmitter. This enables WiPolar to accurately separate the multipaths reflected from different people, which, in turn, allows it to track them simultaneously. To the best of our knowledge, this is the first work that leverages polarization of WiFi signals to localize and track people. We implement WiPolar using commodity WiFi devices and extensively evaluate it for tracking up to five people in three different environments. Our results show that WiPolar achieved a median tracking error of just 56cm across all experiments. It also accurately tracks people even when they were not moving. WiPolar achieved a median tracking error of 74cm for people that were either stationary or just taking a small pause.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5ee9f10a9fced0a24b0a1bb6

Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern. Such issues may vary from fatigue, asthma, stroke, to even heart attack, yet they can be adequately indicated by vital signs and abnormal activities. Therefore, in-vehicle vital sign monitoring can help us predict and hence prevent these issues. Whereas existing sensor-based (including camera) methods could be used to detect these indicators, privacy concern and system complexity both call for a convenient yet effective and robust alternative. This paper aims to develop V2iFi, an intelligent system performing monitoring tasks using a COTS impulse radio mounted on the windshield. V2iFi is capable of reliably detecting driver's vital signs under driving condition and with the presence of passengers, thus allowing for potentially inferring corresponding health issues. Compared with prior work based on Wi-Fi CSI, V2iFi is able to distinguish reflected signals from multiple users, and hence provide finer-grained measurements under more realistic settings. We evaluate V2iFi both in lab environments and during real-life road tests; the results demonstrate that respiratory rate, heart rate, and heart rate variability can all be estimated accurately. Based on these estimation results, we further discuss how machine learning models can be applied on top of V2iFi so as to improve both physiological and psychological wellbeing in driving environments.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5ee9f10a9fced0a24b0a1bbc

Wireless signals have been extensively utilized for contactless sensing in the past few years. Due to the intrinsic nature of employing the weak target-reflected signal for sensing, the sensing range is limited. For instance, WiFi and RFID can achieve 3-6 meter sensing range while acoustic-based sensing is limited to less than one meter. In this work, we identify exciting sensing opportunities with LoRa, which is the new long-range communication technology designed for IoT communication. We explore the sensing capability of LoRa, both theoretically and experimentally. We develop the sensing model to characterize the relationship between target movement and signal variation, and propose novel techniques to increase LoRa sensing range to over 25 meters for human respiration sensing. We further build a prototype system which is capable of sensing both coarse-grained and fine-grained human activities. Experimental results show that (1) human respiration can still be sensed when the target is 25 meters away from the LoRa devices, and 15 meters away with a wall in between; and (2) human walking (both displacement and direction) can be tracked accurately even when the target is 30 meters away from the LoRa transceiver pair.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5f4864c17eeba056ce38afb2

Recent research has shown great potential of exploiting Channel State Information (CSI) retrieved from commodity Wi-Fi devices for contactless human sensing in smart homes. Despite much work on Wi-Fi based indoor localization and motion/intrusion detection, no prior solution is capable of detecting a person entering a room with a precise sensing boundary, making room-based services infeasible in the real world. In this paper, we present WiBorder, an innovative technique for accurate determination of Wi-Fi sensing boundary. The key idea is to harness antenna diversity to effectively eliminate random phase shifts while amplifying through-wall amplitude attenuation. By designing a novel sensing metric and correlating it with human's through-wall discrimination, WiBorder is able to precisely determine Wi-Fi sensing boundaries by leveraging walls in our daily environments. To demonstrate the effectiveness of WiBorder, we have developed an intrusion detection system and an area detection system. Extensive results in real-life scenarios show that our intrusion detection system achieves a high detection rate of 99.4% and a low false alarm rate of 0.68%, and the area detection system's accuracy can be as high as 97.03%. To the best of our knowledge, WiBorder is the first work that enables precise sensing boundary determination via through-wall discrimination, which can immediately benefit other Wi-Fi based applications.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

5ff68dcbd4150a363cd65dee

Activity recognition based on channel state information (CSI) plays an increasingly important role in human computer interaction. However most CSI activity recognition systems need to re-collect a large amount of samples and retrain model when they are used in new environments or recognize new types of activities, which greatly reduces the practicality of CSI activity recognition. To address this problem we design an adaptable CSI activity recognition system based on meta-learning, which only needs to fine-tune model with very little train effort when it is used in new environments or recognize new types of activities. Specifically, we first use meta-learning algorithm to get the pre-trained model that adapts to task distribution, when the environment or activity category changes, our system doesn't need to retrain the model and has maximal performance after updates the pre-trained model through one or more gradient steps computed with a small amount of samples from new activities. To prevent the loss of CSI time information after feature extraction with multi-layer CNN, we add time encoding on CSI data as the input of CNN neural network. Considering that CSI data may be labeled incorrectly during labeling process, we improve categorical cross entropy loss(CCE) to enhance the system's robustness to these mislabeled data. We test our system on the gesture dataset and the body activity dataset, and the experimental results show that our system achieves average accuracy of 72 percent with one sample of each new activity and 89.6 percent with five samples of each new activity.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

600957c1d4150a363cfb2d97

Owing to the ubiquitous penetration of Wi-Fi in our daily lives, Wi-Fi indoor localization has attracted intensive attentions in the last decade or so. Despite some significant progresses, the high accuracy of existing systems is still achieved at the cost of dense access point (AP) deployment. The more practical single AP localization is largely left as an open problem because the hardware-induced time delay “contaminates” the measurement of signal propagation time in the air. In this article, we design and implement M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to tackle this challenge with commodity Wi-Fi cards. M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> exploits a multipath-assisted approach that turns the harmful multipath from foe to friend to enable single AP localization: a device can be pinpointed through the combination of azimuths and the relative time of flight (ToF) of Line-of-Sight (LoS) signal and reflection signals, eliminating the need for multiple APs along with their absolute ToF measurements. M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> further utilizes frequency hopping to combine multiple channels to form a virtually wider-spectrum channel for higher ToF resolution. As a prominent feature of M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , the channels do not need to be adjacent. Comprehensive experiments demonstrate that M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> outperforms the state-of-the-art systems and achieves a median localization accuracy of 71 cm in three environments with a single AP.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

609a6c70e4510cd7c88cffc7

Wi-Fi based gait recognition has many potential applications. However, the gait information derived from Wi-Fi changes with the walking path. This makes the human identification through gait really challenging, the existing Wi-Fi based gait recognition systems require the subject walking along a predetermined path. This path dependence restriction impedes Wi-Fi based gait recognition from being widely used. In this paper, a path independent gait recognition system for a single subject, Wi-PIGR, is proposed. In Wi-PIGR, the subject is identified through the gait regardless of the walking path. Specifically, an extra receiver is introduced to get CSI data in orthogonal directions. A series of signal processing techniques are proposed to eliminate the differences among signals introduced by walking along the arbitrary paths and generate a high quality path independent signal spectrogram. Furthermore, a deep learning approach is integrated into the feature extraction. The experiment results in typical indoor environment demonstrate the superior performance of Wi-PIGR, with the average recognition accuracy of 77.15 percent, when the number of subjects is 50.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

611b7ba75244ab9dcbcc3bca

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

6124cf995244ab9dcb9769ad

With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

6218a92c5aee126c0f597118

Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, ...

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62930ef95d72d8000db3e992

Device-free gesture recognition is a potential noncontact human–computer interaction technique. It leverages the unique influence of the conducted gesture on surrounding wireless signals to accomplish gesture recognition. Existing methods usually leverage doppler spectrogram of the influenced wireless signals to characterize the motion pattern of gestures. These methods have achieved satisfactory accuracy when the gestures are conducted in a relatively fixed location, direction, and speed. However, when gestures are conducted in a different scenario, the recognition accuracy will drop dramatically. In this article, we try to solve this issue by characterizing the gesture motion pattern using a novel robust intrinsic spectrogram, which is independent of the conducted scenario. Specifically, we create a virtual coordinate system in which the coordinates of a gesture trajectory remain unchanged no matter where and how the gesture is conducted. Then, we design a coordinate transformation method to transform the raw doppler spectrogram into the robust intrinsic spectrogram to characterize the intrinsic motion pattern of the gesture. We further feed the intrinsic spectrogram into a deep network to realize gesture recognition. Extensive evaluations on a 77-GHz mmWave testbed show that the proposed method could achieve an average recognize accuracy of 88.4% with ten types of gestures.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62d16a7d5aee126c0fcf669c

Fall is recognized as one of the most frequent accidents among elderly people. Many solutions, either wearable or noncontact, have been proposed for fall detection (FD) recently. Among them, WiFi-based noncontact approaches are gaining popularity due to the ubiquity and noninvasiveness. The existing works, however, usually rely on labor-intensive and time-consuming training before it can achieve a reasonable performance. In addition, the trained models often contain environment-specific information and, thus, cannot be generalized well for new environments. In this article, we propose DeFall, a WiFi-based passive FD system that is independent of the environment and free of prior training in new environments. Unlike previous works, our key insight is to probe the physiological features inherently associated with human falls, i.e., the distinctive patterns of speed and acceleration during a fall. DeFall consists of an offline template-generating stage and an online decision-making stage, both taking the speed estimates as input. In the offline stage, augmented dynamic time-warping (DTW) algorithms are performed to generate a representative template of the speed and acceleration patterns for a typical human fall. In the online phase, we compare the patterns of the real-time speed/acceleration estimates against the template to detect falls. To evaluate the performance of DeFall, we built a prototype using commercial WiFi devices and conducted experiments under different settings. The results demonstrate that DeFall achieves a detection rate above 95% with a false alarm rate lower than 1.50% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios with one single pair of transceivers. Extensive comparison study verifies that DeFall can be generalized well to new environments without any new training.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62d16a7d5aee126c0fcf66d4

Neurodegenerative disorder diseases, such as Parkinson’s disease (PD), are progressive, and their motor symptoms develop slowly. Assessing the motor functions of PD patients relies on the physician’s experience and subjective judgment. However, the disease’s actual condition may not fully present during the clinical examination due to the motor fluctuation or symptom variation in a day. This article proposed a wireless detection method that can enable long-term monitoring and quantification of walking, one of the challenging metrics to be evaluated in clinics. We utilized the channel state information (CSI) from two Wi-Fi links to construct a 2-D coordinate system in space to locate, track, and capture the gait information of a walking subject, including walking distance, step length, and cadence. The results showed that the estimation errors were 0.02–0.16 m (17.84%–38.48%) for step length, 0.01–0.16 Hz (1%–11%) for cadence, and less than 8.84° for walking direction when a subject walked along a straight line. The method could also detect walking that constantly changes direction (circular path) with localization errors of 0.31 (CW) and 0.50 m (CCW). Moreover, the proposed method could distinguish two subjects when they simultaneously walked along nonpredefined paths. The method can be further developed as a home health monitoring technology to provide long-term and reliable data without personally identifiable information for physicians to make more precise treatments.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62d16bc05aee126c0fd17b93

Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.38cm/s$ </tex-math></inline-formula> , 13.42°, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$31.08cm$ </tex-math></inline-formula> median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62d16d1e5aee126c0fd41414

As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62d1725f5aee126c0fdcda2e

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub)meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either unavailable indoor (e.g., global positioning system) or coarse grained (e.g., user check-ins). In order to provide ubiquitous localization services, the widespread WiFi signals are being leveraged to establish (sub)meter-level localization systems. Fine-grained WiFi propagation characteristics, which are sensitive to human body locations, have been employed to create location fingerprints. However, these WiFi characteristics are also sensitive to: 1) the body shapes of different users and 2) the objects in the background environment. Consequently, systems based on WiFi fingerprints are vulnerable in the presence of: 1) new users with different body shapes and 2) daily changes of the environment, e.g., opening/closing doors. To tackle this issue, this article proposes a WiFi-based localization system based on domain-adaptation with cluster assumption, named Fidora. Fidora is able to: 1) localize different users with labeled data from only one or two example users and 2) localize the same user in a changed environment without labeling any new data. To achieve these, Fidora integrates two major modules. It first adopts a data augmenter that introduces data diversity using a variational autoencoder (VAE). It then trains a domain-adaptive classifier that adjusts itself to newly collected unlabeled data using a joint classification-reconstruction structure. We conducted real-world experiments to evaluate Fidora against the state of the art. It is demonstrated that when tested on an unlabeled user, Fidora increases the average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score by 17.8% and improves the worst case accuracy by 20.2%. Moreover, when applied in a varied environment, Fidora outperforms the state of the art by 23.1%.

5a260c6917c44a4ba8a2d60c

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise. In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.

62d1726e5aee126c0fdcec70

Driven by the Internet of Things (IoT), many device-free crowd density estimation techniques can roughly estimate the crowd density based on the relationship between the dynamic crowd and the variation of wireless signals. However, they cannot distinguish the path information of different persons in a fine-grained manner. In this article, we propose Wisual, a channel state information (CSI)-based device-free crowd density estimation framework and can visualize the distribution of people. The major challenge of Wisual is how to extract proper quantifiable indexes to distinguish the path information of multiple targets and maximize the resolution of crowd density estimation. To address this challenge, Wisual first presents a method for estimating the frequency of the CSI propagation path (FoC) for the moving persons and constructs a joint multifeature parameter (JMFP) spectrum matrix with the other two parameters. Then the multitarget spectrum matrix is put into a proposed deep-learning model called CSI stream 3-D convolutional neural networks (CS-3DCNNs) for implementing crowd density estimation and the target path information differentiation. Finally, Wi-Fi imaging is implemented based on the 2-D-MUSIC algorithm, which shows the approximate distribution situation of indoor persons through the spectrograms. The experimental results in typical real-world scenes demonstrate that Wisual can forecast the crowd density with 98% precision and accurately display the frequency spectra of moving persons. Besides, the results also prove the superior effectiveness, scalability, and generalizability of the proposed framework.