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FreeCount: Device-Free Crowd Counting with Commodity WiFi

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... The existing WiFi-based ICC methods can be mainly divided into two categories, depending on the need of manual feature extraction [6]- [13]. One relies on explicit manual features engineered from raw data, such as mean value, variance, median, and range, and then uses threshold-based or learningbased classifiers like support vector machine (SVM) to identify the crowd number. ...
... The performance of the former method is critically related to the data feature selection. For example, to select the best-performing features, Zou et al. [6] proposed a "Transfer Kernel Learning (TKL)" method that selects data features based on a mutual information criterion from a feature pool including several statistical, transformation-based, and shape-based features. The performance of the latter method highly depends on iteratively training with a large number of labeled samples. ...
... Although human activity causes fluctuation of both amplitude and phase, many studies only use the amplitude information for ICC (like in [6], [11], [12]), mainly because the phase information often suffers from more severe hardware measurement noise such as carrier frequency offset and sampling time offset [21]- [23]. Instead of using raw phase measurements, Zong et al. [13] computed the phase difference between adjacent antennas as the input to an SVM-based ICC classifier. ...
Preprint
Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of few-shot learning (FSL) paradigm consisting of two major stages: source domain meta training and target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain dataset to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few shots of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68\%, and on average 96.37\% detection accuracy in a 0-8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.
... However, these approaches require deploying specific sensors in the interest region that cannot be blocked. General WiFi sensors-based [22][23][24][25] methods can overcome the above drawbacks, but they need people to keep moving. Therefore, they cannot detect static crowds, for instance, passengers in the elevator. ...
... In radio communications, the OFDM systems are widely used to divide the wireless network spectrum into orthogonal sub-carriers. However, the emitted WiFi signals often do not reach the receive antenna directly due to obstacle blocking and multi-path effects of signal propagation [23]. A portion of the signals passes through the medium, while other signals are reflected or absorbed by the medium. ...
... For the M1 [25] and M2 [22] methods, about 50% of the results are accurate due to the RSSI-based schemes performing poorly in multi-path complex environments and scene migration. FreeCount [23] (M3) can achieve more than 60% accuracy because CSI is more sensitive to the diversity of transmission channels than RSSI. Due to M4 [24] not only estimating the number of people but also providing human dynamics monitoring through participant number estimation, its F1-score reaches more than 80%. ...
Article
Full-text available
Elevators have become a kind of indispensable facility for everyday life, which bring people both convenience and safety hazards. Specifically in the household environment, an elevator’s lifespan is expected to be more than 20 years. An appropriate and regularly maintained counterweight is conducive to extending elevator life. This paper proposes a passenger counting approach in the elevator for regular counterweight adjustment based on commodity WiFi called ECC. Since the running time of the elevator between two adjacent floors is short, the major challenge of ECC is how to count passengers from the limited captured data. This paper first theoretically analyzes the relationship between the number of passengers and the variation of channel state information (CSI). Then ECC constructs a multi-dimensional feature by extracting the average of amplitude (AOA), time-varying spectrum (TVS), and percentage of non-zero elements (PEM) features from the limited data. Finally, the random forest (RF) classifier is used for passenger counting and the local optimization problem is solved by expanding the feature dataset through data segmentation. ECC is implemented by using off-the-shelf IEEE 802.11n devices, and its performance is evaluated via extensive experiments in typical real-world scenes. The estimated precision of ECC can reach more than 95%, and more than 97% of estimation errors are less than 2 persons, which demonstrates the superior effectiveness and generalizability of ECC.
... Wi-Fi derived occupant counts have been shown to have strong correlation with actual ground truth counts where R 2 values ranging from 0.85-0.96 have been reported in the literature [25,[28][29][30] as well as Wi-Fi device to occupant ratio of 1.27 [31] and 1.2 [32]. Wi-Fi-based occupancy can be utilized in solving occupancy estimation, future occupancy prediction and occupancy pattern clustering problems [16]. ...
... In solving the occupancy estimation problem, [23] achieved 98.85% occupancy detection accuracy, 0.096 Normalized Root Mean Square Derivative occupancy count accuracy and 1.385 m occupancy tracking accuracy using their framework that utilized Received Signal Strength (RSS) levels and device Media Access Control (MAC) addresses. [30] achieved 90% accuracy using Channel State Information (CSI) to infer occupant counts, where the sensitivity of transmitted radio signals to occupant movements was captured. Their approach eliminated the need for occupant terminal devices such as smartphones. ...
... There is the possibility of overestimated occupant counts, which is critical when determining ( ) and ( ) based on, as overestimated occupant counts will result in much earlier ( ) or later ( ) and diminished energy savings. In addition to the preprocessing steps we take in Section 4.2.1, another remedy that has been used in literature is to calibrate device counts against ground truth occupancy [28][29][30] however, this approach assumes static ratio of device counts to actual occupancy counts. Park et al. investigated the use of Capture-Recapture, a methodology adopted from the field of zoology, to infer the spatial and temporal variation in Wi-Fi device count to actual occupancy count ratio as an alternative to a statically determined ratio [57]. ...
Article
HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an Energy Conservation Measure (ECM) where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building's real occupancy is required to maximize the success of this measure. In addition, there is the need for a scalable way to estimate energy savings potential from ECMs that is not limited by building specific parameters and experimental or simulation modeling investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of occupant-derived Heating, Ventilation and Air Conditioning (HVAC) schedules and energy savings that leverages the ubiquity of energy smart meters and Wi-Fi infrastructure in commercial buildings. We estimate the schedules by clustering Wi-Fi derived occupancy profiles and, estimate energy savings by shifting ramp-up and setback times observed in typical operational/static load profiles that are obtained by clustering smart meter energy profiles. Our case-study results with five buildings over seven months show an average of 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy. We validate our method with results from Building Energy Performance Simulation (BEPS) and find that estimated average savings of MARTINI are within 0.9%-2.4% of the BEPS predictions. In the absence of occupancy information, we can still estimate potential savings from increasing ramp-up time and decreasing setback start time. In 51 academic buildings, we find savings potentials between 1%-5%.
... WiFi derived occupant counts have been shown to have strong correlation with actual ground truth counts where R 2 values ranging from 0.85 -0.96 have been reported in the literature [23,24,25,21] as well as a strong occupant to device ratio of 1.27 [26]. WiFi-based occupancy can be utilized in solving occupancy estimation, future occupancy prediction and occupancy pattern clustering problems [13]. ...
... In solving the occupancy estimation problem, [19] achieved 98.85% occupancy detection accuracy, 0.096 Normalized Root Mean Square Derivative occupancy count accuracy and 1.385 m occupancy tracking accuracy using their framework that utilized Received Signal Strength (RSS) levels and device MAC addresses. [25] achieved 90% accuracy using Channel State Information (CSI) to infer occupant counts where the sensitivity of transmitted radio signals to occupant movements was captured. Their approach eliminated the need for occupant terminal devices such as smartphones. ...
... There is the possibility of overestimated occupant counts which is critical when determining ( ) and ( ) based on as overestimated occupant counts will result in much earlier ( ) or later ( ) and diminished energy savings. A remedy that has been used in literature is to calibrate device counts against ground truth occupancy [23,24,25] however, this approach assumes static ratio of device counts to actual occupancy counts. Park et al. investigated the use of Capture-Recapture, a methodology adopted from the field of zoology, to infer the spatial and temporal variation in WiFi device count to actual occupancy count ratio as an alternative to a statically determined ratio. ...
Preprint
Full-text available
HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an energy conservation measure where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building's real occupancy is required to maximize the success of this measure. In addition, there is the need for a scalable way to estimate energy savings potential from energy conservation measures that is not limited by building specific parameters and experimental or simulation modeling investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of occupant-derived HVAC schedules and energy savings that leverages the ubiquity of energy smart meters and WiFi infrastructure in commercial buildings. We estimate the schedules by clustering WiFi-derived occupancy profiles and, energy savings by shifting ramp-up and setback times observed in typical/measured load profiles obtained by clustering smart meter energy profiles. Our case-study results with five buildings over seven months show an average of 8.1%-10.8% (summer) and 0.2%-5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy. We validate our method with results from building energy performance simulation (BEPS) and find that estimated average savings of MARTINI are within 0.9%-2.4% of the BEPS predictions. In the absence of occupancy information, we can still estimate potential savings from increasing ramp-up time and decreasing setback start time. In 51 academic buildings, we find savings potentials between 1%-5%.
... By analysing the patterns of its wireless signal, today's AP has evolved beyond a pure WiFi router, but is also widely used as a type of 'sensor device' to enable new services for human sensing. Particularly, recent studies have found that WiFi signals in the form of Channel State Information (CSI) [1], [2] are extremely promising for a variety of devicefree human sensing tasks, such as occupancy detection [3], activity recognition [4], [5], [6], [7], fall detection [8], gesture recognition [9], [10], human identification [11], [12], and people counting [13], [14]. Unlike the coarse-grained received signal strengths, WiFi CSI records more fine-grained information about how a signal propagates between WiFi J. Yang, X. Chen, D. Wang, H. Zou devices and how a signal is reflected from the surrounding environment in which humans move around. ...
... In contrast, by feeding a massive amount of data into machine learning [22] or deep learning networks, [9], [5], learning based achieve remarkable performances in complicated sensing tasks. Various deep neural networks are designed to enable many applications including activity recognition [23], gesture recognition [9], human identification [11], [12], [24], and people counting [13], [14]. Though deep learning models have a strong ability of function approximation, they require tremendous labeled data that is expensive to collect and suffer from the negative effect of distribution shift caused by environmental dynamics [25]. ...
Preprint
Full-text available
We are excited to share our work “Deep Learning and Its Applications to WiFi Human Sensing” which is the first open-sourced benchmark including well-processed public datasets collected by multiple WiFi sensing platforms. IEEE 802.11bf will come into existence in 2024, and WiFi sensing will enable ubiquitous human perception in smart homes and buildings. Paper: https://arxiv.org/abs/2207.07859 Code: https://github.com/CHENXINYAN-sg/WiFi-CSI-Sensing-Benchmark
... This sensing methodology is advantageous because it has the tendency to be non-intrusive and low-cost by leveraging existing wireless network infrastructure such as WiFi routers, Bluetooth beacons and mobile devices to passively infer occupant information. WiFi derived occupant counts have been shown to have a strong correlation with actual ground truth counts, where R 2 values ranging from 0.85 to 0.96 have been reported in the literature [14,[18][19][20]. WiFi-based occupancy can be utilized in solving occupancy estimation, future occupancy prediction and occupancy pattern clustering problems [8]. ...
... While these methods tackle overestimated counts problem, under counting remains an issue and are associated with privacy risks. To remedy the problems originating from the need of a device-carrying occupants, researchers have utilized Channel State Information (CSI), where the effect of occupant movement on transmitted WiFi signals is used to derive occupancy information [20,27,28]. Demrozi et al. proposed using Bluetooth Low Energy (BLE) as a cheaper but comparably accurate alternative to CSI technology for a device-free occupant detection and counting system [29]. ...
Article
The occupancy information in buildings is fundamental for smart buildings (e.g., occupant-centric controls). Opportunistic occupancy detection (OOD) uses connection data of mobile devices. While OOD has been developed and applied, one critical drawback is that it requires the ground truth of occupants to calibrate, which is limited to gather. Here, we introduce CROOD: a capture and recapture (CRc) inspired OOD. In ecology, CRc has been established for the estimation of animal populations, when the manual count is impossible. We adopt this unique approach to estimate the number of mobile devices in a building. Then, using a simple estimate on the total population, CROOD determines the relationship between the numbers of occupants and mobile devices. We evaluate CROOD numerically on the synthetic building populations and demonstrate its application in a university library using WiFi connection data. We find that CROOD can estimate the number of mobile devices and subsequently the number of occupants with 1–2 weeks to converge a reasonable accuracy. A long term experiment shows that CROOD can adapt to varying population characteristics (e.g., occupants bring more mobile devices), outperforming the reference sample mean estimator. The real building implementation demonstrates that while in the first 1–2 weeks, the sample mean estimator is superior, eventually CROOD adapts and provides better estimates without ground-truth calibration. Although CROOD has a limitation of building types and systems, our results envision that CROOD could be a viable addition to other OOD methods to better utilize existing mobile device connection data to estimate occupancy in buildings.
... The works closest to our research are those that focus on counting people in spaces and that leave aside the analysis of the behavior of the people counted. These solutions, in general, are classified into two groups, those that analyze the Wi-Fi signal [29][30][31][32] and those that focus on the analysis of the information packets sent by the Wi-Fi devices [33][34][35]. Solutions based on Wi-Fi signal analysis (the received signal strength indicator or RSSI and the channel state or CSI, above all) show promising results but for in indoor setups. ...
... From the point of view of the accuracy of the results, regardless of the approach used, the necessary infrastructure, or the type of experimentation, the analyzed articles that were used as a basis for our research show very different values, between 80% and 96% [29,30,35]. These values can only be considered for direct comparison with special caution, as they come from developments based on very different experimentation approaches. ...
Article
Full-text available
Estimating the number of people present in a given venue in real-time is extremely useful from security, management, and resource optimization perspective. This article presents the architecture of a system based on the use ofWi-Fi sensor devices that allows estimating, almost in real-time, the number of people attending an event that is taking place in a venue. The estimate is based on the analysis of the “probe request” messages periodically transmitted by smartphones to determine the existence of Wi-Fi access points in the vicinity. The method considers the MAC address randomization mechanisms introduced in recent years in smartphones, which prevents the estimation of the number of devices by simply counting different MAC addresses. To solve this difficulty, our Wi-Fi sensors analyze other fields present in the header of the IEEE 802.11 frames, the information elements, to extract a unique fingerprint from each smartphone. The designed system was tested in a set of real scenarios, obtaining an estimate of attendance at different public events with an accuracy close to 95%.
... 1 ...
... The number of persons counting within an indoor environment is the demand for many applications to accommodate their services and management [1]. The IoT products are the consecration in this regard to count the number of visitors in such areas as shops, airports, supermarkets, cinemas, stadiums, etc. ...
... 2.7.6. Freecount: Device-Free Crowd Counting with Commodity Wi-Fi FreeCount [67] is a high-accuracy system for crowd counting that uses a set of features of three kinds: statistics, frequency domain and shape. In their publication, Zou et al. focus in the problem of temporal variation of the channel conditions and the unpractical need of re-training the classifier every period of time. ...
... From this foundation, different authors take different approaches for crowd counting. For instance, Zou et al. [67], use a set of features coming from statistics of magnitude and phase, Fourier transformation and shaped metrics, all combined to achieve predictors of human motion. Di Domenico et al. [25], extract Average Spectral Kurtosis as the unique feature they use for their model, while Yang et al. [68], use only the first link of the MIMO grid for feature extraction. ...
Article
Full-text available
Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links.
... This information can be used to allocate resources and ensure public safety. • Shopping Malls Management: Shopping malls and retail stores can use crowd counting to analyze foot traffic and customer behavior [25], [26]. This can help store managers optimize store layout, staffing, and marketing strategies. ...
Preprint
Full-text available
p>In machine vision, the tasks of both the crowd counting and the crowd density estimation attracted a number of researchers from different research institutes throughout the world. These tasks are of significance importance while analyzing the surveillance videos and images both at the real and offline modes. A number of different conventional and modern algorithms are being applied to estimate the accurate amount of people from the crowd from the target image or video file but failed to provide the required results due to heterogeneity of the data instances. To resolve the issue, machine learning models like deep networks like Convolution Neural Network (CNN) and its variants were introduced. In this paper, a detailed and comprehensive review of the work related to both of the crowd counting and density estimation using deep network is provided in quite a very unique way to make it useful for the researchers working in the field of computer vision. Following are the salient features of our review that play key role in making our contribution worthwhile and unique among the surveys and reviews of similar kind: (i) our survey intends to classify the noteworthy literature with respect to the application and the tasks related to crowd counting and density estimation, (ii) the survey also tried to cover in-depth contributions in crowd counting existing works at different level like images or videos (iii) this state-of-the-art review covers each article in the following dimensions: the designated task performed, source level, results obtained, features used, and (iv) lastly it concludes the summary of the related articles according to the publishing years, related tasks (or subtasks), and types of classifiers used. In the end, major challenges and tasks related to crowd counting and density estimation in computer vision are also discussed.</p
... This information can be used to allocate resources and ensure public safety. • Shopping Malls Management: Shopping malls and retail stores can use crowd counting to analyze foot traffic and customer behavior [25], [26]. This can help store managers optimize store layout, staffing, and marketing strategies. ...
Preprint
Full-text available
In machine vision, the tasks of both the crowd counting and the crowd density estimation attracted a number of researchers from different research institutes throughout the world. These tasks are of significance importance while analyzing the surveillance videos and images both at the real and offline modes. A number of different conventional and modern algorithms are being applied to estimate the accurate amount of people from the crowd from the target image or video file but failed to provide the required results due to heterogeneity of the data instances. To resolve the issue, machine learning models like deep networks like Convolution Neural Network (CNN) and its variants were introduced. In this paper, a detailed and comprehensive review of the work related to both of the crowd counting and density estimation using deep network is provided in quite a very unique way to make it useful for the researchers working in the field of computer vision. Following are the salient features of our review that play key role in making our contribution worthwhile and unique among the surveys and reviews of similar kind: (i) our survey intends to classify the noteworthy literature with respect to the application and the tasks related to crowd counting and density estimation, (ii) the survey also tried to cover in-depth contributions in crowd counting existing works at different level like images or videos (iii) this state-of-the-art review covers each article in the following dimensions: the designated task performed, source level, results obtained, features used, and (iv) lastly it concludes the summary of the related articles according to the publishing years, related tasks (or subtasks), and types of classifiers used. In the end, major challenges and tasks related to crowd counting and density estimation in computer vision are also discussed.
... However, carrying out multi-object tracking using CSI is challenging due to the complexity of overlapped signals in the time and frequency domains. Additionally, environmental monitoring techniques such as crowd counting [20] and intrusion detection [21] are popular with CSI-based methods, but they require a room to be equipped with both a transmitter (TX) and a receiver (RX), which is costly and impractical. Presence detection, which infers the presence of people in indoor environments based on the variation of received signals [22], [23], faces the challenge of false detection caused by a range of events that result in time-varying signals. ...
Preprint
Full-text available
Human presence detection is a crucial technology for various applications, including home automation, security, and healthcare. While camera-based systems have traditionally been used for this purpose, they raise privacy concerns. To address this issue, recent research has explored the use of channel state information (CSI) approaches that can be extracted from commercial WiFi access points (APs) and provide detailed channel characteristics. In this thesis, we propose a device-free human presence detection system for multi-room scenarios using a time-selective conditional dual feature extract recurrent Network (TCD-FERN). Our system is designed to capture significant time features with the condition on current human features using a dynamic and static (DaS) data preprocessing technique to extract moving and spatial features of people and differentiate between line-of-sight (LoS) path blocking and non-blocking cases. To mitigate the feature attenuation problem caused by room partitions, we employ a voting scheme. We conduct evaluation and real-time experiments to demonstrate that our proposed TCD-FERN system can achieve human presence detection for multi-room scenarios using fewer commodity WiFi APs.
... This subsection shows applied AI-based optimization algorithms for density estimation in crowd detection and localization approaches. FreeCount is a device-free method of crowd counting proposed by Zou et al. [61] that relies solely on common WiFi routers to calculate the precise population density of an area. In areas of average size, FreeCount can operate with just two routers. ...
Article
Full-text available
The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues.
... By analyzing the patterns of its wireless signals, current-day APs have evolved beyond being pure WiFi routers and also serve as a type of ''sensor device'' to enable new services for human sensing. In particular, recent studies have pointed out that WiFi signals in the form of channel state information (CSI) 1,2 are extremely promising for a variety of device-free human-sensing tasks, such as occupancy detection, 3 activity recognition, [4][5][6][7] fall detection, 8 gesture recognition, 9,10 human identification, [11][12][13] people counting, 14,15 and pose estimation. 16 Unlike coarsegrained received signal strengths, WiFi CSI records more finegrained information in terms of the propagation of a signal between WiFi devices and their reflection with respect to the environment of human beings. ...
Article
Full-text available
Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.
... The WiFi-based gait recognition method uses RF signals from WiFi-enabled devices to determine human identity. The transmitter emits WiFi signals, which are reflected by different body parts of the walking subject and then recorded by CSI data at the receiver [19], which has empowered many applications including occupancy detection [20], crowd counting [21], [22], human activity recognition [23], [24], [25], [26], [27], person identification [8], [28], vital sign detection [29], pose estimation [30] and gesture recognition [31], [32], [33]. To use WiFi sensing in the real world, current research aims at efficient communication [16], model security [34] and dataefficient training [35]. ...
Preprint
As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this paper, we propose a novel multimodal gait recognition method, namely GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, Channel State Information (CSI) that reflects the multi-path propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a Lightweight Residual Convolution Network (LRCN) as the backbone network, and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.
... The authors show the percentage of non-zero elements (PEM) metric and the cumulative density function (CDF) of error estimation for scenarios with different speeds. FreeCount is a crowd counting scheme using WiFi routers presented in [26]. Their model presented high precision for indoors in rooms with moderate size. ...
Conference Paper
Currently, there is a requirement in many countries to keep public and work spaces safe due to COVID-19. In fact, indoor spaces must be monitored to control the allowed capacity, which can vary depending on the alert level of a city at a given time. This has motivated some researchers to investigate several technologies to implement methods and strategies to enable the reopening of these spaces in a safe manner. In this paper, we propose a crowd counting detection system that addresses the problem of controlling the indoor capacity of offices inside buildings. The proposed solution uses an existing communication technology such as WiFi in order to determine the crowd counting for the indoor environment. In particular, the existing infrastructure consists of two Wireless LAN Controllers (WLC) and several APs deployed in a building, which allows us to estimate the number of people based on the access to Wireless Access Points (APs). Thus, the proposed system takes into account when a mobile device connects/disconnects to the AP to increase or decrease the number of people in a particular office and sends the respective alert to the system administrator when this capacity is about to be exceeded or already surpassed.
... Previous researches have shown that human pose can affect the propagation of WiFi signals and these signal variations are reflected by Channel State Information (CSI). Enabled by advanced deep learning methods, many studies have been conducted to enable various applications, such as human activity recognition [5]- [7], gesture recognition [8], [9], human identification [10], [11], and people counting [12], [13]. Many advanced learning methods further promote the automation and performance of these tasks, such as transfer learning [14], [15], unsupervised learning [16], and modelrobust learning [17]. ...
Preprint
Avatar refers to a representative of a physical user in the virtual world that can engage in different activities and interact with other objects in metaverse. Simulating the avatar requires accurate human pose estimation. Though camera-based solutions yield remarkable performance, they encounter the privacy issue and degraded performance caused by varying illumination, especially in smart home. In this paper, we propose a WiFi-based IoT-enabled human pose estimation scheme for metaverse avatar simulation, namely MetaFi. Specifically, a deep neural network is designed with customized convolutional layers and residual blocks to map the channel state information to human pose landmarks. It is enforced to learn the annotations from the accurate computer vision model, thus achieving cross-modal supervision. WiFi is ubiquitous and robust to illumination, making it a feasible solution for avatar applications in smart home. The experiments are conducted in the real world, and the results show that the MetaFi achieves very high performance with a PCK@50 of 95.23%.
... Counting the number of occupants is essential for building monitoring and management. The use of WPS for counting the number of occupants can help control people in specific places and monitor their entry and exit [30,[50][51][52][53]. In addition, WPS can be a good alternative for counting people inside the building, such as shopping centers, airports, and hospitals [54]. ...
Article
Full-text available
Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.
... Alternatively, at the physical layer, Channel State Information (CSI) describes how WiFi signal propagates from a transmitter (TX) to a receiver (RX) through multiple paths at the granularity of Frequency Division Multiplexing (OFDM) subcarriers, which is more sensitive to the presence and movements of an object and is more robust to background noise. Recent literature has witnessed many successful employments of CSI measurements for various applications, such as crowd counting (Zou et al. 2017b) and human ac-The Thirty-Second AAAI Conference on Artificial Intelligence tivity recognition (Wang et al. 2015). Being an off-the-shelf and fine-grained sensing measurement without the introduction of any extra infrastructure or user involvement, CSI data is the ideal sensing recourse for device-free and low-cost human identification. ...
Article
We propose AutoID, a human identification system that leverages the measurements from existing WiFi-enabled Internet of Things (IoT) devices and produces the identity estimation via a novel sparse representation learning technique. The key idea is to use the unique fine-grained gait patterns of each person revealed from the WiFi Channel State Information (CSI) measurements, technically referred to as shapelet signatures, as the "fingerprint" for human identification. For this purpose, a novel OpenWrt-based IoT platform is designed to collect CSI data from commercial IoT devices. More importantly, we propose a new optimization-based shapelet learning framework for tensors, namely Convex Clustered Concurrent Shapelet Learning (C3SL), which formulates the learning problem as a convex optimization. The global solution of C3SL can be obtained efficiently with a generalized gradient-based algorithm, and the three concurrent regularization terms reveal the inter-dependence and the clustering effect of the CSI tensor data. Extensive experiments are conducted in multiple real-world indoor environments, showing that AutoID achieves an average human identification accuracy of 91% from a group of 20 people. As a combination of novel sensing and learning platform, AutoID attains substantial progress towards a more accurate, cost-effective and sustainable human identification system for pervasive implementations.
... Then the E-eyes system is developed to achieve better performance by dividing human activities into in-place and dynamic ones [23]. The FreeCount system leverages a feature selection scheme based on information theory to conduct people counting [24]. These early-stage works show good performance on normal activities such as walking and sitting, but they cannot identify fine-grained subtle gestures. ...
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WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented and balanced CSI samples in a new environment for adaptation algorithms, but randomly captured CSI samples can be easily collected. In this paper, we firstly explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an automatic WiFi sensing model based on a novel geometric self-supervised learning algorithm. The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public datasets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement while overcoming the cross-site issue.
... Zou et al. [25] proposed FreeCount, which is a device-free crowd counting scheme using a modified CSI tool running on commercial WiFi devices. They adopted the transfer kernel learning (TKL) model to take account of temporal variation of CSI measurements, and trained the model with 20 features based on de-noised CSI data by wavelet filter, which are categorized in common statistics, transformationbased, and shape-based features. ...
Article
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Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days. So far, those fields are mostly considered as separate topics in WiFi CSI-based methods, on the contrary, some camera and vision-based crowd estimation systems intuitively estimate both crowd size and location at the same time. Our work is inspired by the idea that WiFi CSI also may be able to perform the same as the camera does. In this paper, we construct Wi-CaL , a simultaneous crowd counting and localization system by using ESP32 modules for WiFi links. We extract several features that contribute to dynamic state (moving crowd) and static state (location of the crowd) from the CSI bundles, then assess our system by both conventional machine learning (ML) and deep learning (DL). As a result of ML-based evaluation, we achieved 0.35 median absolute error (MAE) of counting and 91.4% of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1% of localization accuracy with 10 people in a medium-sized room, by leave-one-session-out cross-validation. We compared our result with percentage of non-zero elements metric (PEM), which is a state-of-the-art metric for crowd counting, and confirmed that our system shows higher performance (0.41 MAE, 81.8% of within-1-person error) than PEM (0.62 MAE, 66.5% of within-1-person error).
... The ability to estimate the total number of people is important to improve the convenience of when utilizing facilities [1]. For example, by counting people in queues, airports, restaurants and shopping malls attempt to improve their services, and also prepare for an effective evacuation guidance during times of emergency [2]. Most of the existing works have realized such estimation by leveraging either image recognition techniques [3] or Wi-Fi signals [4], [5]. ...
Article
As smartphones have become widespread in the past decade, Wi-Fi signal-based crowd estimation schemes are receiving increased attention. These estimation schemes count the number of unique MAC addresses in Wi-Fi signals, hereafter called probe requests (PRs), instead of counting the number of people. However, these estimation schemes have low accuracy of crowd estimation under MAC address randomization that replaces a unique MAC address with various dummy MAC addresses. To solve this problem, in this paper, we propose an indoor crowd estimation scheme using the number of PRs under MAC address randomization. The main idea of the proposed scheme is to leverage the fact that the number of PRs per a unit of time changes in proportion to the number of smartphones. Since a smartphone tends to send a constant number of PRs per a unit of time, the proposed scheme can estimate the accurate number of smartphones. Various experiment results show that the proposed scheme reduces estimation error by at most 75% compared to the conventional Wi-Fi signal-based crowd estimation scheme in an indoor environment.
... Therefore, the system based on the sensor is generally expensive to use or is likely to cause inconvenience to users. Compared with the above two types of technologies, the sensing system based on Wi-Fi signals [10][11][12] can overcome the above shortcomings and has the advantages of convenience, low cost, non-contact, and non-infringement of privacy. Wang et al. [13] proposed the CARM system, which uses the Wi-Fi signal phase to extract the characteristics of motion duration and motion speed, and builds a hidden Markov model to classify single-person activity. ...
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Abstract Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi‐Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi‐dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two‐level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one‐person and two‐person is 99.6% and 94.6% respectively.
... Additional hardware adds additional cost to the estimation process. There exist another procedures that depends on using WiFi to estimate the number of people as our case in this paper [10]- [20], all of them lack accuracy, and used a small number of people in testing. The authors in [21] proposed a framework to estimate the number of people in a specific area depending on the concept that the transmitted signal carries the signature of people, in other words, blocking the Line of Sight and Propagation effects. ...
Article
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WiFi access points are widely spread everywhere in all our daily life routines. Using these devices to provide services other than the Internet is becoming familiar nowadays.This paper conducts an experimental study to estimate the number of people in an indoor environment through two system setups, line of sight, and non-line of sight. Relationship modeling between WiFi received signal and the number of people uses polynomial regression. The experiment comprised of two stages: first is the data collection from a controlled number of people. Then, the collected data used to train the system through polynomial regression. The second is testing the system’s effectiveness by applying it to an uncontrolled environment. Testing results revealed efficiency in using WiFi received signal strength to do the people counting (up to 60) because of the accuracy achievements of 93.17% in the line of sight system. The non-line of sight system disclosed randomness in the received signal strength indicator regardless of the change in the number of people. The randomness is mainly caused by the fading effect of the concrete wall. Therefore it is inefficient to use the non-line of sight system in concrete buildings.
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In the metaverse, digital avatar plays an important role in representing human beings for various interaction with virtual objects and environments, which puts a high demand on effective pose estimation. Though camera-based solutions yield remarkable performance, they encounter privacy issues and degraded performance caused by varying illumination, especially in the smart home. In this article, we propose a WiFi-based Internet of Things-enabled human pose estimation scheme for metaverse avatar simulation, namely, MetaFi++. Specifically, WPFormer is designed with a shared convolutional module and a Transformer block to map the channel state information of WiFi signals to human pose landmarks, effectively exploring spatial information of human pose through self-attention. It is enforced to learn the annotations from the accurate computer vision model, thus achieving cross-modal supervision. Due to the ubiquitous existence of WiFi and robustness to various illumination conditions, WiFi-based human poses are suitable to instruct the movement of digital avatars in the metaverse, promoting avatar applications in smart homes. The experiments are conducted in the real world, and the results show that the MetaFi++ achieves very high performance with a PCK@50 of 97.30%. Our codes are available in https://github.com/pridy999/metafi_pose_estimation .
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Purpose: The main purpose of this study was to examine the application of sensors and sensor networks for detection of people crowds in developing cities. This paper discusses unique challenges associated with people crowd detection especially in the urban towns of developing countries and gives a comparative review and analysis of popular human sensing approaches in the detection of people crowds. Methodology: This study provides a survey and categorization of popular human sensing approaches using literature especially published within the past two decades. The paper then analyzes current human sensing technologies vis-à-vis people crowd detection in developing cities. The respective strengths and shortfalls of various approaches are highlighted. Finally, by means of examples, a comparative analysis of different human sensing categories is carried out. Findings: The spontaneous, dynamic and chaotic nature of people crowds, together with the poor infrastructural development characteristic of developing economies pose unique challenges to the effectiveness of people crowd detection systems. Although there are advances in crowd detection, most of these are in the area of non-people crowds, while most of the research done on people crowd detection have been on indoor crowd settings. In addition, challenges unique to people crowd detection in developing countries include: scalability and cost of crowd detection systems, security of the detection system infrastructure, confidentiality of subjects being monitored, requirements for incentives and the ability to support passive and real time people crowd detection. Unique contribution to theory, practice and policy: This study emphasizes the need for both indoor and outdoor people crowd detection systems appropriate for the needs of developing cities. The study contributes to the body of knowledge since people crowds unlike other types of crowds present a unique set of challenges that call for special attention.
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Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of few-shot learning (FSL) paradigm consisting of two major stages: source domain meta training and target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain dataset to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few shots of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0-8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.
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Wi-Fi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by channel state information (CSI) extracted from Wi-Fi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented, and balanced CSI samples in a new environment for adaptation algorithms, but randomly captured CSI samples can be easily collected. In this article, we first explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an annotation-efficient Wi-Fi sensing model based on a novel geometric self-supervised learning algorithm. The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in Wi-Fi sensing. The AutoFi is implemented on a pair of Atheros Wi-Fi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public data sets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar data sets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that AutoFi takes a huge step toward automatic Wi-Fi sensing without any developer engagement. Our codes have been included in https://github.com/xyanchen/Wi-Fi-CSI-Sensing-Benchmark .
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As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this paper, we propose a novel multimodal gait recognition method, namely GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, Channel State Information (CSI) that reflects the multi-path propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a Lightweight Residual Convolution Network (LRCN) as the backbone network, and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.
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Recently, wireless sensing is gaining immense attention in the Internet of things (IoT) for crowd counting and occupancy detection. As wireless signals propagate, they tend to scatter and reflect in various directions depending on the number of people in the indoor environment. The combined effect of these variations on wireless signals is characterized by the channel state information (CSI), which can be further exploited to identify the presence of people. State-of-the-art CSI-based supervised crowd counting systems are vulnerable to temporal and environmental dynamics in practical scenarios as their performance degrades with fluctuations in the indoor environments due to multipath fading. Inspired by the breakthroughs of transfer learning and advancement in edge computing, we have leveraged in this work the concept of transfer learning to minimize this problem via exploiting the trained model from source environment for other indoor environments to perform device-free crowd counting (CrossCount) at the target rooms. Our results show that this technique can combat the dynamics of the environment and achieves 4.7% better accuracy with 40% reduction in training time as compared to conventional convolutional neural networks. In essence, our results imply the future possibility of harnessing crowdsourced CSI data collected at different indoor environments to boost the accuracy and efficiency of local crowd counting systems.
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Existing channel-state information (CSI)-based human authentication systems in the literature require a large amount of CSI data to train deep neural network (DNN) models and are ineffective for unknown intruder detection. To address this issue, we propose a CSI-based human authentication system (CAUTION) which is able to learn distinctive gait features of different users through CSI data to perform human authentication in this article. By taking advantage of few-shot learning, CAUTION is able to construct an accurate user identification model with a very limited number of CSI training data. By converting the CSI samples into low-dimensional representations on the feature plane, it computes central points for different users as their CSI profiles and introduces an intruder threshold to measure whether the CSI data matches one of the user classes by a margin. The intruder threshold is able to be optimized without any intruders’ data. CAUTION does not require a large number of training data and provides an effective way to train the system for unknown intruder detection. We have tested CAUTION at different places and compared it with state-of-the-art CSI-based authentication systems. The experimental results demonstrate that CAUTION is able to perform accurate human authentication with a limited amount of CSI training data (one-fifth of data needed by compared systems) and outperforms the compared human authentication systems.
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From rssi to csi: Indoor localization via channel response
  • Z Yang
  • Z Zhou
  • Y Liu
Z. Yang, Z. Zhou, and Y. Liu, "From rssi to csi: Indoor localization via channel response," ACM Computing Surveys (CSUR), vol. 46, no. 2, p. 25, 2013.