Conference Paper

FreeDetector: Device-Free Occupancy Detection with Commodity WiFi

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... The Signal tendency index (STI) [20,21] is based on Procrustes analysis to compare shape similarity across diferent packets. It is calculated from the Channel State Information (CSI) containing inegrained information of both the magnitude and the phase of each subcarrier between each transmitter-receiver antenna pair [17]. ...
... At irst glance, the results reported in this paper may seem to have fallen short of what similar systems have reported [21,16]. However, there is a major diference in how the evaluation is done and how the experiment (Tx and Rx) was setup. ...
... In other words, we train and test on completely diferent environments. On the other hand, existing works [21,16] collect a single dataset and then split it into training and testing sets. Due to the limited number of locations, their training and testing sets contain the same transmitter and receiver location pairsśwhich makes the classiication task easier. ...
Conference Paper
As robots penetrate into real-world environments, practical human-robot co-existence issues such as the requirement for safe human-robot interaction are becoming increasingly important. In almost every vision-capable mobile robot, the field of view of the robot is occluded by the presence of obstacles such as indoor walls, furniture, and humans. Such occlusions force the robots to be stationary or to move slowly so that they can avoid collisions and violations of entry into the personal spaces of humans. We see this as a barrier to robots being able to optimally plan motions with reasonable speeds. In order to solve this problem, we propose to augment the sensing capability of a robot by using a commodity WiFi receiver. Using our proposed method, a robot can observe the changes in the properties of received signals, and thus be able to infer whether a human is present behind the wall or obstacles, which enhances its ability to plan and navigate efficiently and intelligently.
... Accurate human sensing is essential for context-awareness to improve the building management system (BMS) and design of impact building. It also plays an important role in many e-Healthcare applications, such as infant monitoring at room, elder monitoring at home, patient monitoring in hospital, and safety management in office [1]- [6]. Traditional approaches for indoor context-awareness include vision [7], RFID [8], environment sensors [9] and Passive Infra-Red (PIR) [10]. ...
... With the widely deployment of WiFi infrastructure in residential, commercial and industrial buildings, WiFi has become the primary resources for non-invasive human sensing [1,2,12,19]. There are a number of WiFi-based approach for context-awareness applications such as indoor localization [1,11], activity recognition [12], and writing recognition [13]. ...
... With the widely deployment of WiFi infrastructure in residential, commercial and industrial buildings, WiFi has become the primary resources for non-invasive human sensing [1,2,12,19]. There are a number of WiFi-based approach for context-awareness applications such as indoor localization [1,11], activity recognition [12], and writing recognition [13]. The basic principle behind WiFi sensing is to measure the variations of WiFi signal intrusive by human motion. ...
Article
Full-text available
Human presence detection and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security and low impact building. However, existing Radio Frequency IDentification (RFID) tags, wearables and passive infrared (PIR) approaches require the user to carry dedicated electronic devices, suffer low detection accuracy and false alarm problems. This paper proposes a novel system for non-invasive human sensing by analyzing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to the stationary objects, thus no requirement on scenario-specific calibration that makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the Support Vector Machine (SVM) classifier towards activity event recognition. The proposed methodology is evaluated on a Software Defined Radio (SDR) system together with experiment of five different events. The results indicate that the proposed system is sufficient for indoor context-awareness with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional Received Signal Strength (RSS) approach 69.3% for event classification and 83.3% for human presence detection.
... The most comparable work to ours is the one recently published by Zou et al. [103], which uses modified COTS WiFi routers to observe changes in the channel state information (CSI). Aside from using electromagnetic signals (WiFi) in comparison to mechanical signals (ultrasound), the main differentiation from our work is that they observe the change in the CSI over frequency subcarriers by using OFDM packets. ...
... We compare the results of our proposed system to the work of Zou et al. [103]. In their work, they used two routers placed at 5 meters from each other. ...
Thesis
Full-text available
As human beings, we rely on audible sounds as one way to communicate between each other and to infer information about our surrounding environment. Similarly, ultrasounds are used by some species in the animal kingdom to sense objects around them and get relevant information about their environment. In this thesis, we build on the inherent characteristics of ultrasounds and explore their application in occupancy sensing of indoor spaces, as ultrasounds exhibit interesting advantages compared to other technologies. Specifically, we design methods and algorithms to generate and process ultrasonic signals and infer the room occupancy, and we develop systems to evaluate their performance. Throughout the work, we address the implementation of our methods using commodity hardware, we pay attention to design algorithms that are computationally efficient, and we evaluate their time and space complexity. We focus on the reusability aspects in our designs, with the aim of bringing the technology to a wide range of existing and potential commercial devices, that would be able to implement our methods and algorithms seamlessly, and offer insights for new applications (like improving users' experience, enhancing home automation, etc.).
... However, this presents several challenges as BLE devices often lack computation power, must be frugal with their energy, and lack channel state information (CSI) which state-of-the-art DfOD algorithms typically rely on [16,19,21]. Moreover, the majority of existing WiFi-based DfOD systems require multiple measurements and offloading data to the application server, resulting in high energy consumption and communication latency [18,22,24,25]. ...
... Less sensor-heavy approaches use only commodity Wi-Fi access points to predict the occupancy of a particular room [18,24,25]. These solutions may not scale well as deploying one or more access points in every room is costly. ...
Conference Paper
The emerging area of device-free occupancy detection (DfOD) has seen slow adoption due to deployability, scalability, and energy efficiency concerns resulting from the use of large, costly, and power-hungry devices like laptops and WiFi routers in the state-of-the-art solutions. Moreover, these approaches often rely on cloud-offloading for data processing which requires extra communication latency and energy. To overcome these challenges, we develop an RF-based DfOD system using easily-deployable Bluetooth Low Energy (BLE) devices. Our system uses a kilobyte-sized machine learning algorithm running on the BLE device to predict the occupancy of a room from a small number of wireless packets, thereby enabling energy-frugal realtime analytics. We validate our approach with experiments in two indoor rooms using four nRF52840 BLE radios. Initial results suggest our system can detect occupancy of an indoor environment with 95% accuracy, 96% precision, and 92% recall while drawing a meager amount of current.
... Wearable sensors [3], [4] solve these problems to some extent but human being do not necessarily carry them in daily life. Recently, WiFi-based approaches [5], [6] are proposed based on the analysis of multi-path distortions in WiFi signals caused by human activities. They preserve user privacy and perform better robustness against vision-based and sensor-based method. ...
... where B y is the background sample for pixel y and the calculation of probability estimation can be achieved by Equation (5). Then the upper bound restriction of P N is able to eliminate some false detections by small motions. ...
... In this situation, by analyzing the historical activity patterns of each occupant captured by WinOSS, we could predict and infer their activities and then confirm with them through the interface to ensure correct occupancy information are obtained and feedback to BMS. WiFi-based device-free occupancy sensing system[54]can be integrated with WinOSS to compensate the performance when occupants forget to carry their MDs. Other special case is when one individual carries multiple MDs, such as one phone and one tablet, in buildings and WinOSS will assume that two occupants are detected. ...
Article
Buildings accounted for half of global electricity consumption in recent years. Accurate occupancy information could improve the energy efficiency and reduce the energy consumption in built environments. Although prior studies have explored various sensing techniques for occupancy sensing, these solutions still suffer from serious drawbacks, e.g. their estimated occupancy information is coarse, extra infrastructure is required, and the privacy of occupants is exposed. In this paper, we present the design and implementation of a novel and practical occupancy sensing system, WinOSS, which is able to provide fine-grained occupancy information thoroughly by leveraging existing commodity WiFi infrastructure along with the WiFi-enabled mobile devices carried by occupants. We have implemented WinOSS in a 1500 m² built environment for four weeks to validate its performance. Extensive experimental results demonstrate that WinOSS outperforms existing occupancy sensing techniques, and provides comprehensive fine-grained occupancy information (including occupancy detection, counting and tracking) in an accurate, reliable, cost-effective and non-intrusive manner.
... The Channel State Information (CSI) metric has become a popular localization metric over RSSI as it is more immune to the adverse effects of multipath propagation [32] and outperforms RSSI based methods [33]. Since CSI offers more fine-grained information than RSSI, it has been extensively utilized in machine learning based DFL approaches including shapelet learning [34], SVM [9,35], Random Forest [36], HMM [37], and Deep Learning [38]. A shortcoming of CSI is that it is currently only accessible using modified drivers in legacy Intel 5300 [11,39], Atheros ath9k [12] based devices, or by using Software Defined Radio (SDR) platforms like USRP [40] or WARP [41]. ...
Article
Full-text available
Device-free Localization (DFL) algorithms using the Received Signal Strength Indicator (RSSI) metric, have become a popular research focus in recent years as they allow for location-based service using Commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc, a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low node deployment density.
... The motivation for using SVM classifier was that it has been used in various previous baseline works. It would be interesting to measure the accuracy using other methods such as Extreme Learning Machine [36] which has lower training time, Random forest [37] which operates by constructing a multitude of decision trees at the training time. Artificial Neural Network [38] is another method which seems quite promising. ...
Thesis
Wi-Fi based activity recognition serves a multitude of applications in fields such as smart homes, health care, and security. The property of the channel state information to provide fine-grained information has been utilized in the above-mentioned fields. However, we wanted to diversify the use of channel state information to even broader areas and that’s why used it to detect the water flow and differentiate between different water patterns. Given the huge scarcity of water in the current world our work tends to serve a practical purpose. The basic idea is to utilize the signal variation caused due to the water flow pattern. The static objects such as furniture, still human causes reflection of signal whereas dynamic or moving objects causes additional propagation paths. These additional propagation paths can be observed by measuring the channel state information amplitude between the two routers. There have been a number of challenges on the way which needed to be figured out before we can get a satisfactory result. Some of them include removing the background noise from the CSI data collected, selecting a classifier which can accurately differentiate between the different patterns of the water flow. We performed various signal processing techniques to reduce the noise and to get a much better representation of the CSI waveform. The Multi-class SVM classifier was modeled and trained to predict the accuracy of different labels collected. Our model achieved 90.35 % accuracy in classifying different labels. Considering this as base work for the detection of water flow pattern, accuracy can be improved later on with some additional functionalities.
... Daniel Konings is with the Department of Mechanical & Electrical Engineering, Massey University, Auckland 0632, New Zealand (email: d.konings@massey.ac.nz). grained features than the competing Received Signal Strength Indicator (RSSI) metric, recent machine learning approaches have largely focused on CSI-based DFL using: shapelet learning [17], Support-vector machines (SVM) [6,18], Random Forest [19], Hidden Markov Models (HMM) [20], and Deep Learning [21]. Though CSI is routinely used in recent literature, it can only currently be implemented using SDR or legacy Wi-Fi radios with modified drivers [22][23][24][25]. ...
Article
Device-free localization (DFL) systems that that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram Distance based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. Existing HD-DFL methods use either Kullback-Leibler or the subsequent improvement, Kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. Results from practical implementation in two different environments show that some distance metrics perform considerably better than Kernel distance when used for existing DFL techniques like Radio Tomographic Imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.
... Table 2 summarizes and compares these approaches. All of them need more or less calibration before use: some, e.g., E-eyes [22], CARM [20], TR-BREATH [5] and Omni-PHD [32], need to build a database storing the CSIs for the normal state and motion is detected as long as the incoming CSI is very dissimilar to the stored CSIs; others, e.g., DeMan [23], PADS [15], FreeSense [27], FreeDetector [34] and SIED [12], investigate intrinsic features of the CSI in the presence of human motion yet need a training phase to tune system parameters in order to balance the false positive rate and false negative rate. Basically, most of the existing works apply a data-driven approach and try to extract features from CSIs to distinguish between motion and non-motion scenarios. ...
Article
Full-text available
Motion detection acts as a key component for a range of applications such as home security, occupancy and activity monitoring, retail analytics, etc. Most existing solutions, however, require special installation and calibration and suffer from frequent false alarms with very limited coverage. In this paper, we propose WiDetect, a highly accurate, robust, and calibration-free wireless motion detector that achieves almost zero false alarm rate and large through-the-wall coverage. Different from previous approaches that either extract data-driven features or assume a few reflection multipaths, we model the problem from a perspective of statistical electromagnetic (EM) by accounting for all multipaths indoors. By exploiting the statistical theory of EM waves, we establish a connection between the autocorrelation function of the physical layer channel state information (CSI) and target motion in the environment. On this basis, we devise a novel motion statistic that is independent of environment, location, orientation, and subjects, and then perform a hypothesis testing for motion detection. By harnessing abundant multipaths indoors, WiDetect can detect arbitrary motion, be it in Line-Of-Sight vicinity or behind multiple walls, providing sufficient whole-home coverage for typical apartments and houses using a single link on commodity WiFi. We conduct extensive experiments in a typical office, an apartment, and a single house with different users for an overall period of more than 5 weeks. The results show that WiDetect achieves a remarkable detection accuracy of 99.68% with a zero false rate, significantly outperforming the state-of-the-art solutions and setting up the stage for ubiquitous motion sensing in practice.
... RSSI-based methods, such as PAWS [8], WiSee [31], WiGest [32], are effective ways to detect human activity but the accuracy is not satisfactory due to the restriction of resolution of RSSI. CSI is more fine-grained that has been utilized for various applications such as occupancy detection [33], activity recognition [22], [23], [34], [35] (E-eyes, CARM, DeepSense and FALAR), sedentary behavior monitoring [36], falling detection [37] (WiFall), crowd counting [38], indoor localization [17], [39], [40] and person identification [41]- [43] These methods realize fine-grained recognition of human activity, but they are deficient in availability due to the lack of scalable platform. Our innovative CSI-enable IoT platform fills the gap and provide a chance to promote these applications for practical usage. ...
Article
Full-text available
Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free and privacy-preserving WiFi-enabled IoT platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf (COTS) WiFi routers to collect Channel State Information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition respectively.
... Recent literature has witnessed many successful employments of CSI measurements for various applications, such as device-free indoor localization [9], human activity recognition [20], [21] and crowd counting [22], [23]. E-eyes [24] employs the similarity of CSI waves and dynamic time warping to recognize human activities. ...
Article
Full-text available
We propose a gesture recognition system that leverages existing WiFi infrastructures and learns gestures from Channel State Information (CSI) measurements. Having developed an innovative OpenWrt-based platform for commercial WiFi devices to extract CSI data, we propose a novel deep Siamese representation learning architecture for one-shot gesture recognition. Technically, our model extends the capacity of spatio-temporal patterns learning for the standard Siamese structure by incorporating convolutional and bidirectional recurrent neural networks. More importantly, the representation learning is ameliorated by our Siamese framework and transferable pairwise loss which helps to remove structured noise such as individual heterogeneity and various measurement conditions during domain-different training. Meanwhile, our Siamese model also enables one-shot learning for higher availability in reality. We prototype our system on commercial WiFi routers. The experiments demonstrate that our model outperforms state-of-the-art solutions for temporal-spatial representation learning and achieves satisfactory results under one-shot conditions.
... In the recent years, numerous wireless technology-based solutions have been proposed and reported in the literature. They utilized Radio Tomographic Imaging (RTI) [3][4][5][6], energy minimization [7,8], and machine learning approaches (including: Support Vector Machines (SVM) [9,10], Random Forest [11], Hidden Markov Models (HMM) [12], and Deep Learning [13]) to mention a few. These approaches are commonly implemented using either the received signal strength indicator (RSSI) metric, or the Wi-Fi channel state information (CSI) metric. ...
Article
Device-free or passive localization techniques allow positioning of targets, without requiring them to carry any form of transceiver or tag. In this paper, a novel device-free visible light positioning technique is proposed. It exploits the variation of the ambient light levels caused by a moving entity. The target is localized by employing a system of artificial potential fields associated with a set of photodiodes embedded into an indoor environment. The system does not require the existing lighting infrastructure to be modified. It also employs a novel calibration procedure that does not require labelled training data, thus significantly reducing the calibration cost. The developed prototype system is installed in three typical indoor environments consisting of a corridor, foyer, and laboratory and was able to attain median errors of 0.68m, 1.20m and 0.84m respectively. Through experimental results, the proposed VLP technique is benchmarked against an existing wireless RSSI-based device-free localization approach, and was able to attain a median error 0.63m lower than the wireless technique.
... There is prior work on presence detection using CSI. The FreeDetector system [22] achieves occupancy detection by computing the temporal similarity of CSIs across frequencies; however, it can only detect walking across line of sight between the transmitter and the receiver. The PADS and R-TTWD systems in [23] and [26] utilize support vector machine (SVM) to detect motion; the inputs to the SVM come from CSI time series after dimensionality reduction through principal component analysis. ...
Preprint
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious pre-processing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments such as timing or frequency offset. Addressing these challenges, the proposed learning system uses pre-processing to preserve human motion induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf WiFi devices, the proposed deep learning based RF sensing achieves near perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. The learning based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.
... We evaluate our method on this sensor dataset. It was collected by IoTbased WiFi sensors and the Channel State Information (CSI) data [32] was extracted that represents the states of the WiFi propagation [33]. When persons perform actions, such CSI data will perform different patterns [26] that can be modeled by traditional models [34] or deep neural network [35] for activity recognition [36], [37], [38], [25], gesture recognition [13], and crowd counting [39]. ...
Article
Full-text available
Deep neural networks (DNNs) have made significant advances in computer vision and sensor-based smart sensing. DNNs achieve prominent results based on standard datasets and powerful servers, whereas in real applications with domain-shift data and resource-constrained environments such as Internet of Things (IoT) devices in the edge computing, DNNs are likely to have degraded performance in terms of accuracy and efficiency. To this end, we develop the MobileDA framework that learns transferable features while keeping the simple structure of the deep model. Our method allows a novel teacher network trained in the server to distill the knowledge for a student network running in the edge device, which is achieved by cross-domain distillation. Leveraging unlabeled data in the new environment, our student model amends the feature learning to be domain-invariant, then being our objective model running in the edge device. Our approach is evaluated on a challenging IoT-based WiFi gesture recognition scenario, and three classic visual adaptation benchmarks. The empirical studies corroborate the effectiveness of distillation for domain transfer, and the overall results show that our model achieves state-of-the-art performance merely using a simple network.
... However, low degree of occupancy resolution, intrusiveness, and cost of execution are considered as the disadvantages of such methods [25]. To address these limitations, researchers [88][89][90][91][92][93][94][95][96][97][98] have leveraged Wi-Fi information for occupancy sensing (such as detection [99] and localization [100]) in commercial buildings. Wi-Fi networks are able to create databases based on the MAC addresses of Wi-Fi enabled devices (such as laptops and smartphones) to easily differentiate between users (i.e., occupants) in a building [101,102]. ...
... Including these factors too, the authors could check if these are important in the forecast. Random Forest has been proved to give the best results for classification in terms of efficiency and accuracy, for occupancy detection [10]. On the other hand, the drawback of running time aspect of the algorithm is not a concern in our application and type of situation, because we do not have a large number of features. ...
Preprint
Manuscript draft of a privacy-preserving system for detecting and predicting occupancy in a building, deployed in a real application
... This is different from other approaches which look at the WiFi signal's interactions with the surrounding area. In [14] the authors examine the channel state information between 2 WiFi routers to determine occupancy, while the authors in [15] off the shelf components and achieve similar results by using the Received Signal Strength (RSS) of the WiFi router. One can also probe the traffic or connect directly to the WiFi router itself to determine the number of number of active devices if privacy is ignored. ...
Article
Presence and occupancy detection in residential and office environments is used to predict movement of people, detect intruders and manage electric power consumption. Specifically, we are developing methods to improve demand side electrical power management by reducing electrical power waste in unoccupied spaces. In this work we conduct an extensive analysis on the applicability of using a WiFi router’s electrical power consumption in different types of environments to determinate the number or people present in a space. We show the importance of a moving average filter for electrical load time series data, confirm the correlation between control packets and increased minimal router power consumption and present our results on the accuracy of our approach. We conclude that a WiFi router’s power consumption can improve presence detection in home environments and occupancy estimation in office environments, and where possible, should be analysed separately from the aggregated power consumption.
... It can also be used in environments involving human movement due to its disturbance of radio waves which can be detected in the WiFi channel. This has been used by previous work to count the number of people passing between two routers or even to implement hand gesture recognition from WiFi signals [8,18]. ...
Thesis
Full-text available
Indoor localization is a key technology for a world with mobile robots and ubiquitous computing. Reliable localization systems for outside environments already exist but quickly fail when applied to indoor situations. As more and more devices are integrated with a WiFi chip, locating a target with this becomes desirable. Using signal strength to achieve that is possible but only results in an accuracy of one to two meters in my experiments. The performance can be improved using the angle of arrival of the incoming signal which can be estimated from Channel State Information. To increase cost effectiveness and ease of deployment, the authors of SpotFi proposed a mathematical trick for performing this using commercial off-the-shelf devices which they implemented using the Intel WifiLink 5300 chip. This thesis describes how it is possible to extract Channel State Information for this purpose from cheaper and more widely used Atheros chips and determines for which of those it is supported. It discusses how the phase offset between antennas due to the internal wiring of a router can be calibrated by using a coaxial cable. An algorithm for filtering the obtained peaks in the MUSIC spectrum is proposed to allow clustering and detection of the direct path. The thesis investigates why angle of arrival estimation is not possible. The most likely reasons are the antenna spacing of the commodity WiFi devices being too large and the inability to calibrate the phase offset using a cable.
... However, an increase in the number of persons affected the detection accuracy. Zou et al. [53] used two commodity Wi-Fi routers to detect a person moving past these routers by comparing the similarity between static CSI and occupied CSI measurements in the time domain. Compared to infrared and ultrasonic, Wi-Fi can provide better coverage and is suitable for a large hallway or entrance. ...
Article
Indoor device-free localization and tracking can bring both convenience and privacy to users compared with traditional solutions such as camera-based surveillance and RFID tag-based tracking. Technologies such as Wi-Fi, wireless sensor, and infrared have been used to localize and track people living in care homes and office buildings. However, the presence of multiple residents introduces further challenges, such as the ambiguity in sensor measurements and target identity, to localization and tracking. In this article, we survey the latest development of device-free indoor localization and tracking in the multi-resident environment. We first present the fundamentals of device-free localization and tracking. Then, we discuss and compare the technologies used in device-free indoor localization and tracking. After discussing the steps involved in multi-resident localization and tracking including target detection, target counting, target identification, localization, and tracking, the techniques related to each step are classified and discussed in detail along with the performance metrics. Finally, we identify the research gap and point out future research directions. To the best of our knowledge, this survey is the most comprehensive work that covers a wide spectrum of the research area of device-free indoor localization and tracking.
... In a typical indoor environment, WiFi signals propagate through multiple paths from TX to RX due to reflection, scattering and diffraction introduced by walls, doors, furniture, as well as the presence and movements of occupants [13]. Nowadays, most of the COTS WiFi devices are equipped with multiple antennas for multiple input, multiple output (MIMO) communication and at the physical layer adopt OFDM that supports IEEE 802.11n/ac standard. ...
... In the future, we plan to include more riding behaviors and optimize the energy consumption of BikeMate. In addition, we consider to extend the capability of commodity WIFI device to tracking the user's riding behavior [33] and even group behiviors [17] of riders. Finally, we will investigate to utilize BikeMate on more types of bike-ways such as one-way bike-ways and single-lane bike-ways. ...
Article
Full-text available
Detecting dangerous riding behaviors is of great importance toimprove bicycling safety. Existing bike safety precautionary mea-sures rely on dedicated infrastructures that incur high installationcosts. In this work, we propose BikeMate, a ubiquitous bicyclingbehavior monitoring system with smartphones. BikeMate invokessmartphone sensors to infer dangerous riding behaviors includ-ing lane weaving, standing pedalling and wrong-way riding. Foreasy adoption, BikeMate leverages transfer learning to reduce theoverhead of training models for different users, and applies crowd-sourcing to infer legal riding directions without prior knowledge.Experiments with 12 participants show that BikeMate achieves anoverall accuracy of 86.8% for lane weaving and standing pedallingdetection, and yields a detection accuracy of 90% for wrong-wayriding using crowdsourced GPS traces.
... Human Activity Recognition (HAR) plays a vital role in the thriving research field of human-computer interaction. It is an essential part of an Internet of Things (IoT) system as it equips the system with the ability to understand and detect users and the surrounding environment to provide better services [1]- [4]. It has important applications in health care, security, entertainment and so on. ...
Article
Full-text available
Channel State Information (CSI) based human activity recognition has received great attention in recent years due to its advantages in privacy protection, insensitivity to illumination, and no requirement for wearable devices. In this paper, we propose a Multimodal Channel State Information Based Activity Recognition (MCBAR) system that leverages existing WiFi infrastructures and monitors human activities from CSI measurements. MCBAR aims to address the performances degradation of WiFi-based human recognition systems due to environmental dynamics. Specifically, we address the issue of non-uniformly distributed unlabelled data with rarely-performed activities by taking advantages of the generative adversarial network (GAN) and semi-supervised learning. We apply a multimodal generator to approximate the CSI data distribution in different environment settings with limited measured CSI data. The generated CSI data using the multimodal generator can provide better diversity for knowledge transfer. This multimodal generator improves the ability of MCBAR to recognize specific activities with various CSI patterns caused by environmental dynamics. Compared to state-of-the-art CSI-based recognition systems, MCBAR is more robust as it is able to handle the non-uniformly distributed CSI data collected from a new environment setting. In addition, diverse generated data from the multimodal generator improves the stability of the system. We have tested MCBAR under multiple experimental settings at different places. The experimental results demonstrate that our algorithm overcomes environmental dynamics and outperforms existing human activity recognition systems.
... Occupant's movements change the signal paths, affect the transmission channel and lead to high variation of CSI. [17] built a device-free occupancy detection system using CSI and achieve detection accuracy 94%. A CSI-based device-free crowd counting scheme has been proposed in [18]. ...
... Including these factors too, the authors could check if these are important in the forecast. Random Forest has been proved to give the best results for classification in terms of efficiency and accuracy, for occupancy detection [16]. On the other hand, the drawback of running time aspect of the algorithm is not a concern in our application and type of situation, because we do not have a large number of features. ...
Article
Full-text available
Sensing and predicting occupancy in buildings is an important task that can lead to significant improvements in both energy efficiency and occupant comfort. Rich data streams are now available that allow for machine learning-based algorithm implementation of direct and indirect occupancy estimation. We evaluate ensemble models, namely, random forests, on data collected from an 8×8 PIR matrix thermopile sensor with the dual goal of predicting individual cell temperature values and subsequently detecting the occupancy status. Evaluation of the method is based on a real case study deployed in an IT Hub in Bucharest, for which we have collected over three weeks of ground data, analyzed, and used it in order to predict occupancy in a room. Results show a 2–4% mean absolute percentage error for the temperature prediction and >99% accuracy for a three-class model to detect human presence. The resulting outputs can be used by predictive building control models to optimize the commands to various subsystems. By separating the specific deployment from the system architecture and data structure, the application can be easily translated to other usage profiles and built environment entities. As compared to vision-based systems, our solution preserves privacy with improved performance when compared to single PIR or indirect estimation.
... In this case, we plan to analyze the historical activity patterns of each occupant captured by WinLight and identify which MD is the most frequently used personal device to present the occupant. WiFi-based device-free occupancy sensing system [82] can be integrated with WinLight to compensate the performance when occupants forget to carry their MDs. Other sensing modalities, such as PIR occupancy sensor or cameras, can be integrated with WinLight to ensure the correct occupancy information are obtained and feedback to BMS. ...
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Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.
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Despite of several years of innovative research, indoor localization is still not mainstream. Existing techniques either employ cumbersome fingerprinting, or rely upon the deployment of additional infrastructure. Towards a solution that is easier to adopt, we propose CUPID, which is free from these restrictions, yet is comparable in accuracy. While existing WiFi based solutions are highly susceptible to indoor multipath, CUPID utilizes physical layer (PHY) information to extract the signal strength and the angle of only the direct path, successfully avoiding the effect of multipath reflections. Our main observation is that natural human mobility, when combined with PHY layer information, can help in accurately estimating the angle and distance of a mobile device from an wireless access point (AP). Real-world indoor experiments using off-the-shelf wireless chipsets confirm the feasibility of CUPID. In addition, while previous approaches rely on multiple APs, CUPID is able to localize a device when only a single AP is present. When a few more APs are available, CUPID can improve the median localization error to 2.7m, which is comparable to schemes that rely on expensive fingerprinting or additional infrastructure.
Statistical Learning for Sparse Sensing and Agile Operation
  • Y Zhou
Y. Zhou, Statistical Learning for Sparse Sensing and Agile Operation. PhD thesis, EECS Department, University of California, Berkeley, May 2017.
Parametric dual maximization for non-convex learning problems
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Y. Zhou, Z. Kang, and C. J. Spanos, "Parametric dual maximization for non-convex learning problems," in Thirty-First AAAI Conference, AAAI, 2017.
Intelligent sleep stage mining service with smartphones
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W. Gu, Z. Yang, L. Shangguan, W. Sun, K. Jin, and Y. Liu, "Intelligent sleep stage mining service with smartphones," in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 649-660, ACM, 2014.
Environmental sensing by wearable device for indoor activity and location estimation
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M. Jin, H. Zou, K. Weekly, R. Jia, A. M. Bayen, and C. J. Spanos, "Environmental sensing by wearable device for indoor activity and location estimation," in Industrial Electronics Society, IECON 2014-40th Annual Conference of the IEEE, pp. 5369-5375, IEEE, 2014.
From rssi to csi: Indoor localization via channel response
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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.