NotiFi: A ubiquitous WiFi-based abnormal activity detection system

Zhu, Daqi; Pang, Na; Li, Gang; Liu, Shaowu

doi:10.1109/ijcnn.2017.7966064

Cited by 21 publications

(10 citation statements)

References 24 publications

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“…Currently, the general classifiers include Dynamic Time Warping (DTW), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Hidden Markov Model (HMM), BP Neural Network (BPNN), Self-Organizing Map (SOM), etc. This article presents some typical behavior recognition applications, including daily behavior recognition [55]- [62], table tennis action recognition [63], bodyweight exercise recognition [64], danger pose detection [65], abnormal activity detection [66], falling detection [67]- [71], hand gesture recognition [72]- [81], sign language recognition [82], [83], sleep monitoring [84], respiration detection [85]- [88], lip reading and speech recognition [89], [90], keystroke detection [91], [92], writing recognition [93]- [95], sedentary behavior monitoring [96], smoking detection [97], crowd counting [98]- [103], step counting [104], [105], human presence detection [106]- [111], and user authentication [112]- [123].…”

Section: A Pattern-based Behavior Recognitionmentioning

confidence: 99%

“…For instance, WiseFi [176] achieves better performance (e.g., average accuracy: LOS 89.1%, NLOS 82.5%, and through-one-wall 73.4%) for activity recognition by using the combined approach. NotiFi [66] can automatically detect abnormal activities by applying the combination of phase and amplitude of CSI. SignFi [150] identifies nine digits finger gestures with an average 86.66% precision by utilizing the information of phase and amplitude.…”

Section: ) Combination Of Amplitude and Phasementioning

confidence: 99%

“…Zhu et al propose a device-free non-invasive abnormal activity detection system in 2017, called NotiFi [66]. This system leverages some steps including PCA, non-parametric Bayesian model, and Hierarchical Dirichlet Process (HDP) to detect five kinds of abnormal actions (e.g., slipping on the ground, falling, running, and breath pausing, etc.).…”

Section: E: Abnormal Activity Detectionmentioning

confidence: 99%

“…Consequently, human behavior recognition in a through-thewall scenario by using CSI extends the research field and provides us with promising applications. With the powerful communication through the wall, many systems have implemented attractive functions, such as E-Eyes [55], Ten-sorBeat [86], WiHACS [57], WiseFi [176], Smokey [97], NotiFi [66], R-TTWD [108], WIGeR [74], FallDeFi [70], DFS [60], and TW-See [61]. However, from these throughthe-wall activity recognition applications, we find that only DFS [60] and TW-See [61] involve the material of walls, such as concrete walls and glass walls.…”

Section: Through the Wall Detectionmentioning

confidence: 99%

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A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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

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Section: A Pattern-based Behavior Recognitionmentioning

confidence: 99%

Section: ) Combination Of Amplitude and Phasementioning

confidence: 99%

Section: E: Abnormal Activity Detectionmentioning

confidence: 99%

Section: Through the Wall Detectionmentioning

confidence: 99%

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A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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“…Research [24] seeks to monitor the position change of entities without actively carrying any physical devices. And research [25] shows that by creating a multiple hierarchical Dirichlet processes, NotiFi automatically learns the number of human body activity categories for abnormal detection.…”

Section: Introductionmentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

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Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

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Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

Zhao

Bhuiyan

et al. 2022

Concurrency and Computation

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Summary Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.

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NotiFi: A ubiquitous WiFi-based abnormal activity detection system

Cited by 21 publications

References 24 publications

A Survey on Human Behavior Recognition Using Channel State Information

A Survey on Human Behavior Recognition Using Channel State Information

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

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